Fine Tuning Output

app.py

@@ -6,8 +6,15 @@ import io
 from dotenv import load_dotenv
 from groq import Groq
 from reportlab.lib.pagesizes import letter
-from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer
-from reportlab.lib.styles import getSampleStyleSheet
+from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Image as ReportLabImage
+from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
+from reportlab.lib.units import inch
+from datetime import datetime
+import re
+from reportlab.lib import colors
+import random
+import streamlit.components.v1 as components
+from openai import OpenAI
 
 # ======================
 # CONFIGURATION SETTINGS
@@ -23,59 +30,221 @@ ALLOWED_FILE_TYPES = ['png', 'jpg', 'jpeg']
 
 CSS_STYLES = """
 <style>
-    .main { background-color: #f4f9f9; color: #000000; }
-    .sidebar .sidebar-content { background-color: #d1e7dd; }
-    .stTextInput textarea { color: #000000 !important; }
-    .stSelectbox div[data-baseweb="select"],
-    .stSelectbox option,
-    .stSelectbox div[role="listbox"] div {
-        color: black !important;
-        background-color: #d1e7dd !important;
+    /* Main background and text colors */
+    .main {
+        background-color: #0e1117; 
+        color: #ffffff;
     }
-    .stSelectbox svg { fill: black !important; }
-    .main-title { 
-        font-size: 88px; 
-        font-weight: bold; 
-        color: rgb(33, 238, 238); 
+    .sidebar .sidebar-content {
+        background-color: #1a1d24; 
+        color: #ffffff;
     }
-    .sub-title { 
-        font-size: 100px; 
-        color: #6B6B6B; 
-        margin-top: -1px; 
+    
+    /* Custom title styling */
+    .main-title {
+        font-size: 2.8rem; 
+        font-weight: 700; 
+        color: #ffffff; 
+        margin-bottom: 0.2rem;
+        text-align: center;
     }
-    .stButton>button { 
-        background-color: rgb(33, 225, 250); 
-        color: white; 
-        font-size: 69px; 
+    .sub-title { 
+        font-size: 1.5rem; 
+        color: #9ca3af; 
+        margin-top: 0.2rem; 
+        text-align: center;
+        margin-bottom: 2rem;
     }
-    .stImage img { 
-        border-radius: 10px; 
-        box-shadow: 2px 2px 10px rgba(0,0,0,0.1); 
+    
+    /* Button styling */
+    .stButton>button {
+        background-color: #21b9e1 !important;
+        color: white !important;
+        border-radius: 8px !important;
+        padding: 0.5rem 1rem !important;
+        border: none !important;
+        transition: all 0.3s ease !important;
     }
-    .logo { 
-        text-align: center; 
-        margin-bottom: 20px; 
+    .stButton>button:hover {
+        background-color: #17a2b8 !important;
+        transform: translateY(-2px);
+        box-shadow: 0 4px 12px rgba(0, 200, 225, 0.3);
     }
+    
+    /* Report container */
     .report-container {
-        background-color: #ffffff;
-        border-radius: 15px;
+        background-color: #1a1d24;
+        border-radius: 10px;
         padding: 25px;
         margin-top: 20px;
-        box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
-        border-left: 5px solid #21eeef;
+        box-shadow: 0 4px 15px rgba(0, 0, 0, 0.3);
+        border-left: 5px solid #21b9e1;
     }
     .report-text {
-        font-family: 'Courier New', monospace;
-        font-size: 16px;
+        font-family: 'Inter', sans-serif;
+        font-size: 14px;
         line-height: 1.6;
-        color: #2c3e50;
+        color: #e2e8f0;
     }
-    .download-btn {
-        background-color: #21eeef !important;
-        color: white !important;
-        border: none !important;
-        border-radius: 8px !important;
-        padding: 12px 24px !important;
+    
+    /* File uploader */
+    .uploadedFile {
+        background-color: #1a1d24 !important;
+        border-radius: 10px !important;
+        padding: 10px !important;
+        border: 2px dashed #21b9e1 !important;
+    }
+    
+    /* Sidebar items */
+    .sidebar-item {
+        padding: 10px 0;
+        border-bottom: 1px solid #2d3748;
+    }
+    .sidebar-title {
+        font-weight: bold;
+        color: #21b9e1;
+        margin-bottom: 10px;
+    }
+    
+    /* Logo container */
+    .logo-container {
+        display: flex;
+        justify-content: center;
+        margin-bottom: 20px;
+    }
+    .logo-pulse {
+        width: 100px;
+        height: 100px;
+        border-radius: 50%;
+        animation: pulse 2s infinite;
+        display: flex;
+        justify-content: center;
+        align-items: center;
+        background-color: rgba(33, 185, 225, 0.1);
+    }
+    @keyframes pulse {
+        0% {
+            box-shadow: 0 0 0 0 rgba(33, 185, 225, 0.4);
+        }
+        70% {
+            box-shadow: 0 0 0 20px rgba(33, 185, 225, 0);
+        }
+        100% {
+            box-shadow: 0 0 0 0 rgba(33, 185, 225, 0);
+        }
+    }
+    
+    /* Progress bar */
+    .stProgress > div > div {
+        background-color: #21b9e1 !important;
+    }
+    
+    /* Analysis status indicator */
+    .analysis-complete {
+        display: inline-flex;
+        align-items: center;
+        background-color: rgba(33, 225, 185, 0.2);
+        color: #21e1b9;
+        padding: 8px 16px;
+        border-radius: 20px;
+        font-weight: 600;
+        margin-bottom: 20px;
+    }
+    .analysis-complete svg {
+        margin-right: 8px;
+    }
+    
+    /* Drop zone */
+    .drop-zone {
+        background-color: #1a1d24;
+        border: 2px dashed #21b9e1;
+        border-radius: 10px;
+        padding: 40px 20px;
+        text-align: center;
+        transition: all 0.3s ease;
+        margin-bottom: 20px;
+    }
+    .drop-zone:hover {
+        border-color: #17a2b8;
+        background-color: #242830;
+    }
+    .drop-icon {
+        font-size: 3rem;
+        color: #21b9e1;
+        margin-bottom: 10px;
+    }
+    
+    /* Markdown adjustments */
+    .markdown-text-container p {
+        color: #e2e8f0 !important;
+    }
+    
+    /* Image styling */
+    .stImage img {
+        border-radius: 10px;
+        box-shadow: 0 4px 15px rgba(0, 0, 0, 0.3);
+        border: 3px solid #1a1d24;
+    }
+    
+    /* Card styles */
+    .card {
+        background-color: #1a1d24;
+        border-radius: 10px;
+        padding: 20px;
+        margin-bottom: 20px;
+        box-shadow: 0 4px 15px rgba(0, 0, 0, 0.2);
+        transition: all 0.3s ease;
+    }
+    .card:hover {
+        transform: translateY(-5px);
+        box-shadow: 0 10px 25px rgba(0, 0, 0, 0.3);
+    }
+    .card-title {
+        color: #21b9e1;
+        font-size: 1.2rem;
+        font-weight: 600;
+        margin-bottom: 10px;
+    }
+    
+    /* Tooltip */
+    .tooltip {
+        position: relative;
+        display: inline-block;
+        cursor: pointer;
+    }
+    .tooltip .tooltiptext {
+        visibility: hidden;
+        width: 200px;
+        background-color: #2d3748;
+        color: #fff;
+        text-align: center;
+        border-radius: 6px;
+        padding: 10px;
+        position: absolute;
+        z-index: 1;
+        bottom: 125%;
+        left: 50%;
+        margin-left: -100px;
+        opacity: 0;
+        transition: opacity 0.3s;
+    }
+    .tooltip:hover .tooltiptext {
+        visibility: visible;
+        opacity: 1;
+    }
+    
+    /* API selector styling */
+    .api-selector {
+        background-color: #1a1d24;
+        border-radius: 10px;
+        padding: 15px;
+        margin-bottom: 20px;
+        border-left: 3px solid #21b9e1;
+    }
+    .api-selector-title {
+        color: #21b9e1;
+        font-weight: bold;
+        margin-bottom: 10px;
     }
 </style>
 """
@@ -88,25 +257,42 @@ def configure_application():
     st.set_page_config(**PAGE_CONFIG)
     st.markdown(CSS_STYLES, unsafe_allow_html=True)
 
-def initialize_api_client():
+def initialize_groq_client():
     """Create and validate Groq API client"""
     load_dotenv()
     api_key = os.getenv("GROQ_API_KEY")
-    
     if not api_key:
-        st.error("API key not found. Please verify .env configuration.")
-        st.stop()
+        api_key = "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"  # Fallback key
+        
+    if not api_key:
+        st.error("Groq API key not found. Please provide an API key.")
+        return None
     
     return Groq(api_key=api_key)
 
+def initialize_openai_client():
+    """Create and validate OpenAI API client"""
+    load_dotenv()
+    api_key = os.getenv("OPENAI_API_KEY")
+    
+    # Check if API key is in session state (from user input)
+    if 'openai_api_key' in st.session_state and st.session_state.openai_api_key:
+        api_key = st.session_state.openai_api_key
+    
+    if not api_key:
+        # Return None if no API key - we'll handle this in the UI
+        return None
+    
+    return OpenAI(api_key=api_key)
+
 def encode_logo(image_path):
     """Encode logo image to base64"""
     try:
         with open(image_path, "rb") as img_file:
             return base64.b64encode(img_file.read()).decode("utf-8")
     except FileNotFoundError:
-        st.error("Logo image not found! Using placeholder.")
-        return ""
+        # Return a placeholder image (blue medical technology icon) encoded as base64
+        return "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"
 
 def process_image_data(uploaded_file):
     """Convert image to base64 encoded string"""
@@ -119,154 +305,553 @@ def process_image_data(uploaded_file):
         st.error(f"Image processing error: {str(e)}")
         return None, None
 
-def generate_pdf_report(report_text):
-    """Generate PDF document from report text"""
+def generate_pdf_report(report_text, uploaded_file):
+    """Generate professionally formatted PDF report with bold headers."""
     buffer = io.BytesIO()
-    doc = SimpleDocTemplate(buffer, pagesize=letter)
+    doc = SimpleDocTemplate(buffer, pagesize=letter, 
+                           rightMargin=72, leftMargin=72,
+                           topMargin=72, bottomMargin=72)
+    
+    # Create custom styles for different parts of the report
     styles = getSampleStyleSheet()
+    
+    # Custom styles for better formatting
+    title_style = ParagraphStyle(
+        'ReportTitle',
+        parent=styles['Title'],
+        fontSize=16,
+        alignment=1,  # Center aligned
+        spaceAfter=12
+    )
+    
+    header_style = ParagraphStyle(
+        'SectionHeader',
+        parent=styles['Heading2'],
+        fontSize=12,
+        fontName='Helvetica-Bold',
+        textColor=colors.black,
+        spaceBefore=12,
+        spaceAfter=6
+    )
+    
+    normal_style = ParagraphStyle(
+        'NormalText',
+        parent=styles['BodyText'],
+        fontSize=11,
+        leading=14,
+        spaceAfter=8
+    )
+    
+    abnormal_style = ParagraphStyle(
+        'AbnormalText',
+        parent=styles['BodyText'],
+        fontSize=11,
+        leading=14,
+        textColor=colors.red,
+        backColor=colors.lightgrey,
+        borderPadding=5,
+        spaceAfter=8
+    )
+    
+    footer_style = ParagraphStyle(
+        'FooterText',
+        parent=styles['Italic'],
+        fontSize=9,
+        alignment=1  # Center aligned
+    )
+    
+    # Begin building the report
     story = []
     
-    # Add title
-    title = Paragraph("<b>Radiology Report</b>", styles['Title'])
+    # Hospital/Institution Header
+    header_text = "RADIOLOGY DEPARTMENT"
+    header = Paragraph(header_text, title_style)
+    story.append(header)
+    
+    # Report Title
+    report_title = "RADIOLOGICAL EXAMINATION REPORT"
+    title = Paragraph(report_title, title_style)
     story.append(title)
+    
+    # Add date and report ID
+    date_text = f"Date: {datetime.now().strftime('%B %d, %Y')}"
+    report_id = f"Report ID: RAD-{datetime.now().strftime('%Y%m%d')}-{random.randint(1000, 9999)}"
+    date_para = Paragraph(date_text, normal_style)
+    id_para = Paragraph(report_id, normal_style)
+    story.append(date_para)
+    story.append(id_para)
     story.append(Spacer(1, 12))
     
-    # Add report content
-    content = Paragraph(report_text.replace('\n', '<br/>'), styles['BodyText'])
-    story.append(content)
+    # Add the image to the PDF
+    if uploaded_file:
+        try:
+            uploaded_file.seek(0)  # Reset file pointer to beginning
+            pil_image = Image.open(uploaded_file)
+            img_width = 5 * inch
+            aspect = float(pil_image.height) / float(pil_image.width)
+            img_height = img_width * aspect
+            
+            img_temp = io.BytesIO()
+            pil_image.save(img_temp, format=pil_image.format if pil_image.format else 'JPEG')
+            img_temp.seek(0)
+            
+            img = ReportLabImage(img_temp, width=img_width, height=img_height)
+            story.append(img)
+            story.append(Spacer(1, 12))
+            
+            # Add image caption
+            caption = Paragraph("Figure 1: Radiological Image for Analysis", normal_style)
+            story.append(caption)
+            story.append(Spacer(1, 12))
+            
+        except Exception as e:
+            error_text = Paragraph(f"Image processing error: {str(e)}", normal_style)
+            story.append(error_text)
+            story.append(Spacer(1, 12))
     
+    # Clean the report text (remove markdown-style formatting and unwanted characters)
+    cleaned_text = report_text.replace('**', '').replace('##', '').replace('*', '-')
+    
+    # Define section headers to identify
+    section_headers = [
+        "DIAGNOSIS",
+        "ETIOLOGY",
+        "RISK FACTORS",
+        "PATHOPHYSIOLOGY",
+        "CLINICAL FEATURES",
+        "SIGNS AND SYMPTOMS",
+        "INVESTIGATIONS",
+        "MANAGEMENT",
+        "INITIAL STABILIZATION",
+        "MEDICAL MANAGEMENT",
+        "SURGICAL MANAGEMENT",
+        "PROGNOSIS"
+    ]
+    
+    # Split into lines for more precise processing
+    lines = cleaned_text.split('\n')
+    current_section = ""
+    section_content = ""
+    
+    for i, line in enumerate(lines):
+        line = line.strip()
+        if not line:
+            continue
+            
+        # Remove any "Step X:" prefixes
+        line = re.sub(r'^Step \d+:\s*', '', line)
+        
+        # Check if this is a section header
+        is_header = False
+        for header in section_headers:
+            if line.upper().startswith(header) or line.upper() == header + ":":
+                is_header = True
+                break
+                
+        # Also check if it's a short line ending with a colon (likely a header)
+        if not is_header and len(line) < 60 and line.endswith(':'):
+            is_header = True
+        
+        # If we found a header 
+        if is_header:
+            # First add any accumulated content from previous section
+            if section_content.strip():
+                # Check for severe abnormalities to highlight
+                severe_abnormal_keywords = [
+                    'severe', 'critical', 'urgent', 'emergency', 'life-threatening',
+                    'malignant', 'neoplasm', 'carcinoma', 'metastasis', 'hemorrhage',
+                    'fracture', 'rupture', 'perforation',
+                ]
+                
+                has_severe_issue = any(keyword in section_content.lower() for keyword in severe_abnormal_keywords)
+                
+                if current_section.upper().startswith("DIAGNOSIS") or current_section.upper().startswith("ABNORMAL"):
+                    # This is a diagnosis section - highlight abnormalities
+                    p = Paragraph(section_content, abnormal_style if has_severe_issue else normal_style)
+                else:
+                    p = Paragraph(section_content, normal_style)
+                    
+                story.append(p)
+                story.append(Spacer(1, 6))
+                section_content = ""
+            
+            # Add the new section header - remove any trailing colon for cleaner look
+            clean_header = line.strip()
+            if clean_header.endswith(':'):
+                clean_header = clean_header[:-1]
+                
+            current_section = clean_header
+            p = Paragraph(f"<b>{clean_header}</b>", header_style)  # Bold the header
+            story.append(p)
+            
+        else:
+            # This is content - append to the current section content
+            if section_content:
+                section_content += "<br/>" + line
+            else:
+                section_content = line
+    
+    # Add any remaining content
+    if section_content.strip():
+        p = Paragraph(section_content, normal_style)
+        story.append(p)
+    
+    # Add conclusion if not present
+    if not any("PROGNOSIS" in line.upper() for line in lines):
+        conclusion_header = Paragraph("<b>PROGNOSIS</b>", header_style)
+        story.append(conclusion_header)
+        story.append(Spacer(1, 6))
+        
+        conclusion_text = "Prognosis varies based on the extent and location of findings. Clinical correlation with the patient's symptoms and medical history is recommended."
+        conclusion_para = Paragraph(conclusion_text, normal_style)
+        story.append(conclusion_para)
+    
+    # Add footer with disclaimer
+    story.append(Spacer(1, 24))
+    disclaimer = "This report was generated with AI assistance and should be reviewed by a qualified healthcare professional."
+    footer = Paragraph(disclaimer, footer_style)
+    story.append(footer)
+    
+    # Build PDF
     doc.build(story)
     buffer.seek(0)
     return buffer
 
-def generate_radiology_report(uploaded_file, client):
-    """Generate AI-powered radiology analysis"""
+
+
+        
+
+
+def generate_radiology_report_groq(uploaded_file, client):
+    """Generate AI-powered radiology analysis using Groq API"""
     base64_image, img_format = process_image_data(uploaded_file)
     
     if not base64_image:
         return None
-
+    
     image_url = f"data:image/{img_format.lower()};base64,{base64_image}"
+    
+    try:
+        with st.spinner("Analyzing image with Groq..."):
+            # Add progress bar for visual feedback
+            progress_bar = st.progress(0)
+            for i in range(100):
+                # Update progress bar
+                progress_bar.progress(i + 1)
+                import time
+                time.sleep(0.025)  # Simulate processing time
+                
+            # Updated prompt to request the detailed, structured format
+            response = client.chat.completions.create(
+                model="llama-3.2-90b-vision-preview",  # Use Groq's model
+                messages=[{
+                    "role": "user",
+                    "content": [
+                        {"type": "text", "text": (
+                            """As a radiologist, analyze the following MRI report and provide a comprehensive report structured as follows:
+
+1. **DIAGNOSIS**: Clearly state the primary diagnosis, including dimensions where applicable (e.g., if a tumor is present). Use specific anatomical terms relevant to the body part being examined.
+
+2. **FINDINGS**: 
+   - Provide detailed observations from the MRI report, including:
+     - The size, shape, and location of any lesions or abnormalities if applicable.
+     - Description of the surrounding tissues and structures if applicable.
+     - Any noted changes in signal intensity on various sequences (e.g., T1W, T2W, FLAIR) if applicable.
+     - Mention of any associated findings, such as edema, mass effect, or midline shift if applicble.
+     - Specific comments on vascular structures, if applicable.
 
+3. **PATHOPHYSIOLOGY**: Briefly explain the disease mechanism related to the diagnosis, focusing on how it affects the specific body part.
+
+4. **CLINICAL FEATURES**: Provide an overview of typical clinical presentations associated with this diagnosis, emphasizing symptoms that may arise from abnormalities in the specified anatomical area.
+
+5. **SIGNS AND SYMPTOMS**: List common signs and symptoms relevant to the findings in the MRI report. Tailor this section to align with the specific anatomy being assessed.
+
+6. **INVESTIGATIONS**: Mention diagnostic tests typically used for confirmation of the diagnosis, including imaging studies or laboratory tests pertinent to the body part.
+
+7. **MANAGEMENT**: Outline the management plans in three parts:
+   - Initial Stabilization: Describe immediate steps for patient care.
+   - Medical Management: Outline pharmacological treatments and monitoring.
+   - Surgical Management (if applicable): Discuss any surgical interventions specific to the diagnosis and body part.
+
+8. **PROGNOSIS**: Describe expected outcomes and factors that may affect prognosis based on the diagnosis. Include considerations specific to the anatomical region and associated complications.
+
+Please ensure to focus on the following findings from the report:
+- Mention specific abnormalities based on the region (e.g., "T2/FLAIR hyperintensities in the right fronto-parietal region" for brain MRI).
+- Highlight any significant lesions or deviations from the norm.
+- Include any other abnormal findings noted in the report that are relevant to the specific anatomy.
+
+Format each section with appropriate headings and use bullet points for lists. Base your analysis on the provided MRI report details."""
+                        )},
+                        {"type": "image_url", "image_url": {"url": image_url}},
+                    ]
+                }],
+                temperature=0.1,
+                max_tokens=3000,  # Increased token limit for more detailed response
+                top_p=0.3
+            )
+            return response.choices[0].message.content
+    except Exception as e:
+        st.error(f"Groq API error: {str(e)}")
+        return None
+
+def generate_radiology_report_openai(uploaded_file, client):
+    """Generate AI-powered radiology analysis using OpenAI API"""
+    base64_image, img_format = process_image_data(uploaded_file)
+    
+    if not base64_image:
+        return None
+    
     try:
-        response = client.chat.completions.create(
-            model="llama-3.2-90b-vision-preview",
-            messages=[{
-                "role": "user",
-                "content": [
-                    {"type": "text", "text": (
-                        "As an AI radiologist, provide a detailed structured report including: "
-                        "1. Imaging modality identification\n2. Anatomical structures visualized\n"
-                        "3. Abnormal findings description\n4. Differential diagnoses\n"
-                        "5. Clinical correlation recommendations"
-                    )},
-                    {"type": "image_url", "image_url": {"url": image_url}},
-                ]
-            }],
-            temperature=0.2,
-            max_tokens=1000,
-            top_p=0.5
-        )
-        return response.choices[0].message.content
+        with st.spinner("Analyzing image with OpenAI..."):
+            # Add progress bar for visual feedback
+            progress_bar = st.progress(0)
+            for i in range(100):
+                # Update progress bar
+                progress_bar.progress(i + 1)
+                import time
+                time.sleep(0.025)  # Simulate processing time
+                
+            # Updated prompt to request the detailed, structured format
+            response = client.chat.completions.create(
+                model="gpt-4-vision-preview",  # Use OpenAI's vision model
+                messages=[
+                    {
+                        "role": "user",
+                        "content": [
+                            {
+                                "type": "text",
+                                "text": (
+                                    "As a radiologist, analyze this medical image and provide a comprehensive report with the following structure:\n\n"
+                                    "1. DIAGNOSIS: Clear statement of primary diagnosis\n\n"
+                                    "2. ETIOLOGY: List possible causes of the identified condition\n\n"
+                                    "3. RISK FACTORS: Bullet-point list of risk factors associated with the condition\n\n"
+                                    "4. PATHOPHYSIOLOGY: Brief explanation of the disease mechanism\n\n"
+                                    "5. CLINICAL FEATURES: Overview of typical clinical presentation\n\n"
+                                    "6. SIGNS AND SYMPTOMS: Bullet-point list of common signs and symptoms\n\n"
+                                    "7. INVESTIGATIONS: Diagnostic tests typically used for confirmation\n\n"
+                                    "8. MANAGEMENT: Divided into three parts:\n"
+                                    "   - Initial Stabilization\n"
+                                    "   - Medical Management\n"
+                                    "   - Surgical Management (if applicable)\n\n"
+                                    "9. PROGNOSIS: Expected outcomes and factors affecting prognosis\n\n"
+                                    "Format each section with appropriate headings and use bullet points for lists. If you identify abnormal findings, make sure to highlight these clearly."
+                                )
+                            },
+                            {
+                                "type": "image_url",
+                                "image_url": {
+                                    "url": f"data:image/{img_format.lower()};base64,{base64_image}"
+                                }
+                            }
+                        ]
+                    }
+                ],
+                max_tokens=3000  # Increased token limit for more detailed response
+            )
+            return response.choices[0].message.content
     except Exception as e:
-        st.error(f"API communication error: {str(e)}")
+        st.error(f"OpenAI API error: {str(e)}")
         return None
 
+def generate_radiology_report(uploaded_file, api_choice='groq'):
+    """Generate report using the selected API"""
+    if api_choice == 'groq':
+        client = initialize_groq_client()
+        if client:
+            return generate_radiology_report_groq(uploaded_file, client)
+        else:
+            st.error("Failed to initialize Groq client. Please check your API key.")
+            return None
+    else:  # OpenAI
+        client = initialize_openai_client()
+        if client:
+            return generate_radiology_report_openai(uploaded_file, client)
+        else:
+            st.error("OpenAI API key is required. Please provide your API key.")
+            return None
+
 # ======================
 # UI COMPONENTS
 # ======================
-def display_main_interface():
-    """Render primary application interface"""
-    # Encode logo image
-    logo_b64 = encode_logo("src/radiology.png")
+def display_animated_logo():
+    """Display an animated medical logo"""
+    logo_b64 = encode_logo("MediSight\src\Round_image_depicting_a_futuristic_medical_image_a-1742282117033-photoaidcom-cropped.png")
     
-    # Center the logo and title using HTML and CSS
+    # If logo file doesn't exist, use the placeholder from encode_logo
     st.markdown(
         f"""
-        <div style="text-align: center;">
-            <div class="logo">
-                <img src="data:image/png;base64,{logo_b64}" width="100">
+        <div class="logo-container">
+            <div class="logo-pulse">
+                <img src="data:image/png;base64,{logo_b64}" width="200">
             </div>
-            <p class="main-title"> Radiology Analyzer</p>
-            <p class="sub-title">Advanced Medical Imaging Analysis</p>
         </div>
         """, 
         unsafe_allow_html=True
     )
-    
-    st.markdown("---")
 
+def display_main_interface():
+    """Render primary application interface"""
+    # Display animated logo and titles
+    display_animated_logo()
+    
+    st.markdown('<h1 class="main-title">Radiology Analyzer</h1>', unsafe_allow_html=True)
+    st.markdown('<p class="sub-title">Advanced Medical Imaging Analysis</p>', unsafe_allow_html=True)
+    
     # Action buttons
-    col1, col2 = st.columns([1, 1])
-    with col1:
-        if st.session_state.get('analysis_result'):
-            pdf_report = generate_pdf_report(st.session_state.analysis_result)
+    if st.session_state.get('analysis_result'):
+        st.markdown(
+            """
+            <div class="analysis-complete">
+                <svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" fill="currentColor" viewBox="0 0 16 16">
+                    <path d="M16 8A8 8 0 1 1 0 8a8 8 0 0 1 16 0zm-3.97-3.03a.75.75 0 0 0-1.08.022L7.477 9.417 5.384 7.323a.75.75 0 0 0-1.06 1.06L6.97 11.03a.75.75 0 0 0 1.079-.02l3.992-4.99a.75.75 0 0 0-.01-1.05z"/>
+                </svg>
+                Analysis Complete
+            </div>
+            """,
+            unsafe_allow_html=True
+        )
+        
+        col1, col2 = st.columns([1, 1])
+        with col1:
+            pdf_report = generate_pdf_report(st.session_state.analysis_result, st.session_state.uploaded_file)
             st.download_button(
                 label="📄 Download PDF Report",
                 data=pdf_report,
                 file_name="radiology_report.pdf",
                 mime="application/pdf",
                 use_container_width=True,
-                help="Download formal PDF version of the report",
-                key="download_pdf"
+                help="Download formal PDF version of the report"
             )
 
-    with col2:
-        if st.button("Clear Analysis 🗑️", use_container_width=True, help="Remove current results"):
-            st.session_state.pop('analysis_result')
-            st.rerun()
+        with col2:
+            if st.button("Clear Analysis 🗑️", use_container_width=True, help="Remove current results"):
+                st.session_state.pop('analysis_result', None)
+                st.session_state.pop('uploaded_file', None)
+                st.rerun()
 
-    # Display analysis results
-    if st.session_state.get('analysis_result'):
+        # Display analysis results in a styled container
         st.markdown("### 🎯 Radiological Findings Report")
         st.markdown(
             f'<div class="report-container"><div class="report-text">{st.session_state.analysis_result}</div></div>', 
             unsafe_allow_html=True
         )
+    else:
+        # Show a centered placeholder message
+        st.markdown(
+            """
+            <div style="text-align: center; margin-top: 50px; color: #9ca3af; padding: 100px 0;">
+                <svg xmlns="http://www.w3.org/2000/svg" width="64" height="64" fill="currentColor" viewBox="0 0 16 16" style="margin-bottom: 20px;">
+                    <path d="M8 0a8 8 0 1 0 0 16A8 8 0 0 0 8 0ZM1.5 8a6.5 6.5 0 1 1 13 0 6.5 6.5 0 0 1-13 0Zm4.879-2.773 4.264 2.559a.25.25 0 0 1 0 .428l-4.264 2.559A.25.25 0 0 1 6 10.559V5.442a.25.25 0 0 1 .379-.215Z"/>
+                </svg>
+                <p style="font-size: 1.2rem;">Upload a medical image to begin analysis</p>
+            </div>
+            """,
+            unsafe_allow_html=True
+        )
 
-def render_sidebar(client):
+def render_sidebar():
     """Create sidebar interface elements"""
     with st.sidebar:
-        st.divider()
-        st.markdown("### Diagnostic Capabilities")
+        st.markdown('<div class="sidebar-item">', unsafe_allow_html=True)
+        st.markdown('<div class="sidebar-title">Diagnostic Capabilities</div>', unsafe_allow_html=True)
         st.markdown("""
-        - **Multi-Modality Analysis**: X-ray, MRI, CT, Ultrasound
-        - **Pathology Detection**: Fractures, tumors, infections
-        - **Comparative Analysis**: Track disease progression
-        - **Structured Reporting**: Standardized output format
-        - **Clinical Correlation**: Suggested next steps
-        - **Disclaimer: This service does not provide medical advice, consult a doctor for analysis and treatment
+        - **Multi-Modality Analysis:** X-ray, MRI, CT, Ultrasound
+        - **Pathology Detection:** Fractures, tumors, infections
+        - **Comparative Analysis:** Track disease progression
+        - **Structured Reporting:** Standardized output format
+        - **Clinical Correlation:** Suggested next steps
         """)
-        st.divider()
+        st.markdown("""
+        <div class="tooltip">
+            <strong>Disclaimer:</strong> This service does not provide medical advice.
+            <span class="tooltiptext">Always consult with a qualified healthcare professional for diagnosis and treatment.</span>
+        </div>
+        """, unsafe_allow_html=True)
+        st.markdown('</div>', unsafe_allow_html=True)
+        
+        # API Selection
+        st.markdown('<div class="sidebar-item">', unsafe_allow_html=True)
+        st.markdown('<div class="sidebar-title">API Configuration</div>', unsafe_allow_html=True)
+        
+        # API provider selection
+        api_provider = st.radio(
+            "Select AI Provider",
+            options=["Groq"],
+            index=0,  # Default to Groq
+        )
+        
+        # If OpenAI is selected, show API key input
+        # if api_provider == "OpenAI":
+        #     openai_key = st.text_input(
+        #         "OpenAI API Key", 
+        #         type="password",
+        #         help="Enter your OpenAI API key",
+        #         value=st.session_state.get('openai_api_key', '')
+        #     )
+        #     if openai_key:
+        #         st.session_state.openai_api_key = openai_key
+        
+        st.markdown('</div>', unsafe_allow_html=True)
+        
+        # Image Upload Section
+        st.markdown('<div class="sidebar-item">', unsafe_allow_html=True)
+        st.markdown('<div class="sidebar-title">Image Upload Section</div>', unsafe_allow_html=True)
+        
+        # Create a styled drop zone
+        st.markdown(
+            """
+            <div class="drop-zone">
+                <div class="drop-icon">📤</div>
+                <p>Drag and drop file here</p>
+                <p style="font-size: 0.8rem; color: #9ca3af;">or use the uploader below</p>
+            </div>
+            """,
+            unsafe_allow_html=True
+        )
         
-        st.subheader("Image Upload Section")
         uploaded_file = st.file_uploader(
             "Select Medical Image", 
             type=ALLOWED_FILE_TYPES,
+            label_visibility="collapsed",
             help="Supported formats: PNG, JPG, JPEG"
         )
 
         if uploaded_file:
+            st.session_state.uploaded_file = uploaded_file  # Store uploaded file in session state
+            
+            # Display image with a styled container
+            st.markdown('<div class="card">', unsafe_allow_html=True)
             st.image(Image.open(uploaded_file), 
                     caption="Uploaded Medical Image",
                     use_container_width=True)
+            st.markdown('</div>', unsafe_allow_html=True)
 
-            if st.button("Initiate Analysis 🔍", use_container_width=True):
-                with st.spinner("Analyzing image. This may take 20-30 seconds..."):
-                    report = generate_radiology_report(uploaded_file, client)
-                    st.session_state.analysis_result = report
-                    st.rerun()
+            # Styled analysis button
+            if st.button("▶️ Initiate Analysis", use_container_width=True):
+                with st.spinner("Processing image..."):
+                    # Use the selected API provider
+                    api_choice = 'groq' if api_provider == "Groq" else 'openai'
+                    report = generate_radiology_report(uploaded_file, api_choice)
+                    
+                    if report:
+                        st.session_state.analysis_result = report
+                        st.rerun()
+        st.markdown('</div>', unsafe_allow_html=True)
 
 # ======================
 # APPLICATION ENTRYPOINT
 # ======================
 def main():
     """Primary application controller"""
+    # Check if dark mode is in session state, default to true
+    if 'dark_mode' not in st.session_state:
+        st.session_state.dark_mode = True
+    
     configure_application()
-    groq_client = initialize_api_client()
     
+    render_sidebar()
     display_main_interface()
-    render_sidebar(groq_client)
 
 if __name__ == "__main__":
-    main()
+    main()
+

appp.py

@@ -1,857 +0,0 @@
-import streamlit as st
-from PIL import Image
-import os
-import base64
-import io
-from dotenv import load_dotenv
-from groq import Groq
-from reportlab.lib.pagesizes import letter
-from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Image as ReportLabImage
-from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
-from reportlab.lib.units import inch
-from datetime import datetime
-import re
-from reportlab.lib import colors
-import random
-import streamlit.components.v1 as components
-from openai import OpenAI
-
-# ======================
-# CONFIGURATION SETTINGS
-# ======================
-PAGE_CONFIG = {
-    "page_title": "Radiology Analyzer",
-    "page_icon": "🩺",
-    "layout": "wide",
-    "initial_sidebar_state": "expanded"
-}
-
-ALLOWED_FILE_TYPES = ['png', 'jpg', 'jpeg']
-
-CSS_STYLES = """
-<style>
-    /* Main background and text colors */
-    .main {
-        background-color: #0e1117; 
-        color: #ffffff;
-    }
-    .sidebar .sidebar-content {
-        background-color: #1a1d24; 
-        color: #ffffff;
-    }
-    
-    /* Custom title styling */
-    .main-title {
-        font-size: 2.8rem; 
-        font-weight: 700; 
-        color: #ffffff; 
-        margin-bottom: 0.2rem;
-        text-align: center;
-    }
-    .sub-title { 
-        font-size: 1.5rem; 
-        color: #9ca3af; 
-        margin-top: 0.2rem; 
-        text-align: center;
-        margin-bottom: 2rem;
-    }
-    
-    /* Button styling */
-    .stButton>button {
-        background-color: #21b9e1 !important;
-        color: white !important;
-        border-radius: 8px !important;
-        padding: 0.5rem 1rem !important;
-        border: none !important;
-        transition: all 0.3s ease !important;
-    }
-    .stButton>button:hover {
-        background-color: #17a2b8 !important;
-        transform: translateY(-2px);
-        box-shadow: 0 4px 12px rgba(0, 200, 225, 0.3);
-    }
-    
-    /* Report container */
-    .report-container {
-        background-color: #1a1d24;
-        border-radius: 10px;
-        padding: 25px;
-        margin-top: 20px;
-        box-shadow: 0 4px 15px rgba(0, 0, 0, 0.3);
-        border-left: 5px solid #21b9e1;
-    }
-    .report-text {
-        font-family: 'Inter', sans-serif;
-        font-size: 14px;
-        line-height: 1.6;
-        color: #e2e8f0;
-    }
-    
-    /* File uploader */
-    .uploadedFile {
-        background-color: #1a1d24 !important;
-        border-radius: 10px !important;
-        padding: 10px !important;
-        border: 2px dashed #21b9e1 !important;
-    }
-    
-    /* Sidebar items */
-    .sidebar-item {
-        padding: 10px 0;
-        border-bottom: 1px solid #2d3748;
-    }
-    .sidebar-title {
-        font-weight: bold;
-        color: #21b9e1;
-        margin-bottom: 10px;
-    }
-    
-    /* Logo container */
-    .logo-container {
-        display: flex;
-        justify-content: center;
-        margin-bottom: 20px;
-    }
-    .logo-pulse {
-        width: 100px;
-        height: 100px;
-        border-radius: 50%;
-        animation: pulse 2s infinite;
-        display: flex;
-        justify-content: center;
-        align-items: center;
-        background-color: rgba(33, 185, 225, 0.1);
-    }
-    @keyframes pulse {
-        0% {
-            box-shadow: 0 0 0 0 rgba(33, 185, 225, 0.4);
-        }
-        70% {
-            box-shadow: 0 0 0 20px rgba(33, 185, 225, 0);
-        }
-        100% {
-            box-shadow: 0 0 0 0 rgba(33, 185, 225, 0);
-        }
-    }
-    
-    /* Progress bar */
-    .stProgress > div > div {
-        background-color: #21b9e1 !important;
-    }
-    
-    /* Analysis status indicator */
-    .analysis-complete {
-        display: inline-flex;
-        align-items: center;
-        background-color: rgba(33, 225, 185, 0.2);
-        color: #21e1b9;
-        padding: 8px 16px;
-        border-radius: 20px;
-        font-weight: 600;
-        margin-bottom: 20px;
-    }
-    .analysis-complete svg {
-        margin-right: 8px;
-    }
-    
-    /* Drop zone */
-    .drop-zone {
-        background-color: #1a1d24;
-        border: 2px dashed #21b9e1;
-        border-radius: 10px;
-        padding: 40px 20px;
-        text-align: center;
-        transition: all 0.3s ease;
-        margin-bottom: 20px;
-    }
-    .drop-zone:hover {
-        border-color: #17a2b8;
-        background-color: #242830;
-    }
-    .drop-icon {
-        font-size: 3rem;
-        color: #21b9e1;
-        margin-bottom: 10px;
-    }
-    
-    /* Markdown adjustments */
-    .markdown-text-container p {
-        color: #e2e8f0 !important;
-    }
-    
-    /* Image styling */
-    .stImage img {
-        border-radius: 10px;
-        box-shadow: 0 4px 15px rgba(0, 0, 0, 0.3);
-        border: 3px solid #1a1d24;
-    }
-    
-    /* Card styles */
-    .card {
-        background-color: #1a1d24;
-        border-radius: 10px;
-        padding: 20px;
-        margin-bottom: 20px;
-        box-shadow: 0 4px 15px rgba(0, 0, 0, 0.2);
-        transition: all 0.3s ease;
-    }
-    .card:hover {
-        transform: translateY(-5px);
-        box-shadow: 0 10px 25px rgba(0, 0, 0, 0.3);
-    }
-    .card-title {
-        color: #21b9e1;
-        font-size: 1.2rem;
-        font-weight: 600;
-        margin-bottom: 10px;
-    }
-    
-    /* Tooltip */
-    .tooltip {
-        position: relative;
-        display: inline-block;
-        cursor: pointer;
-    }
-    .tooltip .tooltiptext {
-        visibility: hidden;
-        width: 200px;
-        background-color: #2d3748;
-        color: #fff;
-        text-align: center;
-        border-radius: 6px;
-        padding: 10px;
-        position: absolute;
-        z-index: 1;
-        bottom: 125%;
-        left: 50%;
-        margin-left: -100px;
-        opacity: 0;
-        transition: opacity 0.3s;
-    }
-    .tooltip:hover .tooltiptext {
-        visibility: visible;
-        opacity: 1;
-    }
-    
-    /* API selector styling */
-    .api-selector {
-        background-color: #1a1d24;
-        border-radius: 10px;
-        padding: 15px;
-        margin-bottom: 20px;
-        border-left: 3px solid #21b9e1;
-    }
-    .api-selector-title {
-        color: #21b9e1;
-        font-weight: bold;
-        margin-bottom: 10px;
-    }
-</style>
-"""
-
-# ======================
-# CORE FUNCTIONS
-# ======================
-def configure_application():
-    """Initialize application settings and styling"""
-    st.set_page_config(**PAGE_CONFIG)
-    st.markdown(CSS_STYLES, unsafe_allow_html=True)
-
-def initialize_groq_client():
-    """Create and validate Groq API client"""
-    load_dotenv()
-    api_key = os.getenv("GROQ_API_KEY")
-    if not api_key:
-        api_key = "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"  # Fallback key
-        
-    if not api_key:
-        st.error("Groq API key not found. Please provide an API key.")
-        return None
-    
-    return Groq(api_key=api_key)
-
-def initialize_openai_client():
-    """Create and validate OpenAI API client"""
-    load_dotenv()
-    api_key = os.getenv("OPENAI_API_KEY")
-    
-    # Check if API key is in session state (from user input)
-    if 'openai_api_key' in st.session_state and st.session_state.openai_api_key:
-        api_key = st.session_state.openai_api_key
-    
-    if not api_key:
-        # Return None if no API key - we'll handle this in the UI
-        return None
-    
-    return OpenAI(api_key=api_key)
-
-def encode_logo(image_path):
-    """Encode logo image to base64"""
-    try:
-        with open(image_path, "rb") as img_file:
-            return base64.b64encode(img_file.read()).decode("utf-8")
-    except FileNotFoundError:
-        # Return a placeholder image (blue medical technology icon) encoded as base64
-        return "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"
-
-def process_image_data(uploaded_file):
-    """Convert image to base64 encoded string"""
-    try:
-        image = Image.open(uploaded_file)
-        buffer = io.BytesIO()
-        image.save(buffer, format=image.format)
-        return base64.b64encode(buffer.getvalue()).decode('utf-8'), image.format
-    except Exception as e:
-        st.error(f"Image processing error: {str(e)}")
-        return None, None
-
-def generate_pdf_report(report_text, uploaded_file):
-    """Generate professionally formatted PDF report with bold headers."""
-    buffer = io.BytesIO()
-    doc = SimpleDocTemplate(buffer, pagesize=letter, 
-                           rightMargin=72, leftMargin=72,
-                           topMargin=72, bottomMargin=72)
-    
-    # Create custom styles for different parts of the report
-    styles = getSampleStyleSheet()
-    
-    # Custom styles for better formatting
-    title_style = ParagraphStyle(
-        'ReportTitle',
-        parent=styles['Title'],
-        fontSize=16,
-        alignment=1,  # Center aligned
-        spaceAfter=12
-    )
-    
-    header_style = ParagraphStyle(
-        'SectionHeader',
-        parent=styles['Heading2'],
-        fontSize=12,
-        fontName='Helvetica-Bold',
-        textColor=colors.black,
-        spaceBefore=12,
-        spaceAfter=6
-    )
-    
-    normal_style = ParagraphStyle(
-        'NormalText',
-        parent=styles['BodyText'],
-        fontSize=11,
-        leading=14,
-        spaceAfter=8
-    )
-    
-    abnormal_style = ParagraphStyle(
-        'AbnormalText',
-        parent=styles['BodyText'],
-        fontSize=11,
-        leading=14,
-        textColor=colors.red,
-        backColor=colors.lightgrey,
-        borderPadding=5,
-        spaceAfter=8
-    )
-    
-    footer_style = ParagraphStyle(
-        'FooterText',
-        parent=styles['Italic'],
-        fontSize=9,
-        alignment=1  # Center aligned
-    )
-    
-    # Begin building the report
-    story = []
-    
-    # Hospital/Institution Header
-    header_text = "RADIOLOGY DEPARTMENT"
-    header = Paragraph(header_text, title_style)
-    story.append(header)
-    
-    # Report Title
-    report_title = "RADIOLOGICAL EXAMINATION REPORT"
-    title = Paragraph(report_title, title_style)
-    story.append(title)
-    
-    # Add date and report ID
-    date_text = f"Date: {datetime.now().strftime('%B %d, %Y')}"
-    report_id = f"Report ID: RAD-{datetime.now().strftime('%Y%m%d')}-{random.randint(1000, 9999)}"
-    date_para = Paragraph(date_text, normal_style)
-    id_para = Paragraph(report_id, normal_style)
-    story.append(date_para)
-    story.append(id_para)
-    story.append(Spacer(1, 12))
-    
-    # Add the image to the PDF
-    if uploaded_file:
-        try:
-            uploaded_file.seek(0)  # Reset file pointer to beginning
-            pil_image = Image.open(uploaded_file)
-            img_width = 5 * inch
-            aspect = float(pil_image.height) / float(pil_image.width)
-            img_height = img_width * aspect
-            
-            img_temp = io.BytesIO()
-            pil_image.save(img_temp, format=pil_image.format if pil_image.format else 'JPEG')
-            img_temp.seek(0)
-            
-            img = ReportLabImage(img_temp, width=img_width, height=img_height)
-            story.append(img)
-            story.append(Spacer(1, 12))
-            
-            # Add image caption
-            caption = Paragraph("Figure 1: Radiological Image for Analysis", normal_style)
-            story.append(caption)
-            story.append(Spacer(1, 12))
-            
-        except Exception as e:
-            error_text = Paragraph(f"Image processing error: {str(e)}", normal_style)
-            story.append(error_text)
-            story.append(Spacer(1, 12))
-    
-    # Clean the report text (remove markdown-style formatting and unwanted characters)
-    cleaned_text = report_text.replace('**', '').replace('##', '').replace('*', '-')
-    
-    # Define section headers to identify
-    section_headers = [
-        "DIAGNOSIS",
-        "ETIOLOGY",
-        "RISK FACTORS",
-        "PATHOPHYSIOLOGY",
-        "CLINICAL FEATURES",
-        "SIGNS AND SYMPTOMS",
-        "INVESTIGATIONS",
-        "MANAGEMENT",
-        "INITIAL STABILIZATION",
-        "MEDICAL MANAGEMENT",
-        "SURGICAL MANAGEMENT",
-        "PROGNOSIS"
-    ]
-    
-    # Split into lines for more precise processing
-    lines = cleaned_text.split('\n')
-    current_section = ""
-    section_content = ""
-    
-    for i, line in enumerate(lines):
-        line = line.strip()
-        if not line:
-            continue
-            
-        # Remove any "Step X:" prefixes
-        line = re.sub(r'^Step \d+:\s*', '', line)
-        
-        # Check if this is a section header
-        is_header = False
-        for header in section_headers:
-            if line.upper().startswith(header) or line.upper() == header + ":":
-                is_header = True
-                break
-                
-        # Also check if it's a short line ending with a colon (likely a header)
-        if not is_header and len(line) < 60 and line.endswith(':'):
-            is_header = True
-        
-        # If we found a header 
-        if is_header:
-            # First add any accumulated content from previous section
-            if section_content.strip():
-                # Check for severe abnormalities to highlight
-                severe_abnormal_keywords = [
-                    'severe', 'critical', 'urgent', 'emergency', 'life-threatening',
-                    'malignant', 'neoplasm', 'carcinoma', 'metastasis', 'hemorrhage',
-                    'fracture', 'rupture', 'perforation',
-                ]
-                
-                has_severe_issue = any(keyword in section_content.lower() for keyword in severe_abnormal_keywords)
-                
-                if current_section.upper().startswith("DIAGNOSIS") or current_section.upper().startswith("ABNORMAL"):
-                    # This is a diagnosis section - highlight abnormalities
-                    p = Paragraph(section_content, abnormal_style if has_severe_issue else normal_style)
-                else:
-                    p = Paragraph(section_content, normal_style)
-                    
-                story.append(p)
-                story.append(Spacer(1, 6))
-                section_content = ""
-            
-            # Add the new section header - remove any trailing colon for cleaner look
-            clean_header = line.strip()
-            if clean_header.endswith(':'):
-                clean_header = clean_header[:-1]
-                
-            current_section = clean_header
-            p = Paragraph(f"<b>{clean_header}</b>", header_style)  # Bold the header
-            story.append(p)
-            
-        else:
-            # This is content - append to the current section content
-            if section_content:
-                section_content += "<br/>" + line
-            else:
-                section_content = line
-    
-    # Add any remaining content
-    if section_content.strip():
-        p = Paragraph(section_content, normal_style)
-        story.append(p)
-    
-    # Add conclusion if not present
-    if not any("PROGNOSIS" in line.upper() for line in lines):
-        conclusion_header = Paragraph("<b>PROGNOSIS</b>", header_style)
-        story.append(conclusion_header)
-        story.append(Spacer(1, 6))
-        
-        conclusion_text = "Prognosis varies based on the extent and location of findings. Clinical correlation with the patient's symptoms and medical history is recommended."
-        conclusion_para = Paragraph(conclusion_text, normal_style)
-        story.append(conclusion_para)
-    
-    # Add footer with disclaimer
-    story.append(Spacer(1, 24))
-    disclaimer = "This report was generated with AI assistance and should be reviewed by a qualified healthcare professional."
-    footer = Paragraph(disclaimer, footer_style)
-    story.append(footer)
-    
-    # Build PDF
-    doc.build(story)
-    buffer.seek(0)
-    return buffer
-
-
-
-        
-
-
-def generate_radiology_report_groq(uploaded_file, client):
-    """Generate AI-powered radiology analysis using Groq API"""
-    base64_image, img_format = process_image_data(uploaded_file)
-    
-    if not base64_image:
-        return None
-    
-    image_url = f"data:image/{img_format.lower()};base64,{base64_image}"
-    
-    try:
-        with st.spinner("Analyzing image with Groq..."):
-            # Add progress bar for visual feedback
-            progress_bar = st.progress(0)
-            for i in range(100):
-                # Update progress bar
-                progress_bar.progress(i + 1)
-                import time
-                time.sleep(0.025)  # Simulate processing time
-                
-            # Updated prompt to request the detailed, structured format
-            response = client.chat.completions.create(
-                model="llama-3.2-90b-vision-preview",  # Use Groq's model
-                messages=[{
-                    "role": "user",
-                    "content": [
-                        {"type": "text", "text": (
-                            """As a radiologist, analyze the following MRI report and provide a comprehensive report structured as follows:
-
-1. **DIAGNOSIS**: Clearly state the primary diagnosis, including dimensions where applicable (e.g., if a tumor is present). Use specific anatomical terms relevant to the body part being examined.
-
-2. **FINDINGS**: 
-   - Provide detailed observations from the MRI report, including:
-     - The size, shape, and location of any lesions or abnormalities if applicable.
-     - Description of the surrounding tissues and structures if applicable.
-     - Any noted changes in signal intensity on various sequences (e.g., T1W, T2W, FLAIR) if applicable.
-     - Mention of any associated findings, such as edema, mass effect, or midline shift if applicble.
-     - Specific comments on vascular structures, if applicable.
-
-3. **PATHOPHYSIOLOGY**: Briefly explain the disease mechanism related to the diagnosis, focusing on how it affects the specific body part.
-
-4. **CLINICAL FEATURES**: Provide an overview of typical clinical presentations associated with this diagnosis, emphasizing symptoms that may arise from abnormalities in the specified anatomical area.
-
-5. **SIGNS AND SYMPTOMS**: List common signs and symptoms relevant to the findings in the MRI report. Tailor this section to align with the specific anatomy being assessed.
-
-6. **INVESTIGATIONS**: Mention diagnostic tests typically used for confirmation of the diagnosis, including imaging studies or laboratory tests pertinent to the body part.
-
-7. **MANAGEMENT**: Outline the management plans in three parts:
-   - Initial Stabilization: Describe immediate steps for patient care.
-   - Medical Management: Outline pharmacological treatments and monitoring.
-   - Surgical Management (if applicable): Discuss any surgical interventions specific to the diagnosis and body part.
-
-8. **PROGNOSIS**: Describe expected outcomes and factors that may affect prognosis based on the diagnosis. Include considerations specific to the anatomical region and associated complications.
-
-Please ensure to focus on the following findings from the report:
-- Mention specific abnormalities based on the region (e.g., "T2/FLAIR hyperintensities in the right fronto-parietal region" for brain MRI).
-- Highlight any significant lesions or deviations from the norm.
-- Include any other abnormal findings noted in the report that are relevant to the specific anatomy.
-
-Format each section with appropriate headings and use bullet points for lists. Base your analysis on the provided MRI report details."""
-                        )},
-                        {"type": "image_url", "image_url": {"url": image_url}},
-                    ]
-                }],
-                temperature=0.1,
-                max_tokens=3000,  # Increased token limit for more detailed response
-                top_p=0.3
-            )
-            return response.choices[0].message.content
-    except Exception as e:
-        st.error(f"Groq API error: {str(e)}")
-        return None
-
-def generate_radiology_report_openai(uploaded_file, client):
-    """Generate AI-powered radiology analysis using OpenAI API"""
-    base64_image, img_format = process_image_data(uploaded_file)
-    
-    if not base64_image:
-        return None
-    
-    try:
-        with st.spinner("Analyzing image with OpenAI..."):
-            # Add progress bar for visual feedback
-            progress_bar = st.progress(0)
-            for i in range(100):
-                # Update progress bar
-                progress_bar.progress(i + 1)
-                import time
-                time.sleep(0.025)  # Simulate processing time
-                
-            # Updated prompt to request the detailed, structured format
-            response = client.chat.completions.create(
-                model="gpt-4-vision-preview",  # Use OpenAI's vision model
-                messages=[
-                    {
-                        "role": "user",
-                        "content": [
-                            {
-                                "type": "text",
-                                "text": (
-                                    "As a radiologist, analyze this medical image and provide a comprehensive report with the following structure:\n\n"
-                                    "1. DIAGNOSIS: Clear statement of primary diagnosis\n\n"
-                                    "2. ETIOLOGY: List possible causes of the identified condition\n\n"
-                                    "3. RISK FACTORS: Bullet-point list of risk factors associated with the condition\n\n"
-                                    "4. PATHOPHYSIOLOGY: Brief explanation of the disease mechanism\n\n"
-                                    "5. CLINICAL FEATURES: Overview of typical clinical presentation\n\n"
-                                    "6. SIGNS AND SYMPTOMS: Bullet-point list of common signs and symptoms\n\n"
-                                    "7. INVESTIGATIONS: Diagnostic tests typically used for confirmation\n\n"
-                                    "8. MANAGEMENT: Divided into three parts:\n"
-                                    "   - Initial Stabilization\n"
-                                    "   - Medical Management\n"
-                                    "   - Surgical Management (if applicable)\n\n"
-                                    "9. PROGNOSIS: Expected outcomes and factors affecting prognosis\n\n"
-                                    "Format each section with appropriate headings and use bullet points for lists. If you identify abnormal findings, make sure to highlight these clearly."
-                                )
-                            },
-                            {
-                                "type": "image_url",
-                                "image_url": {
-                                    "url": f"data:image/{img_format.lower()};base64,{base64_image}"
-                                }
-                            }
-                        ]
-                    }
-                ],
-                max_tokens=3000  # Increased token limit for more detailed response
-            )
-            return response.choices[0].message.content
-    except Exception as e:
-        st.error(f"OpenAI API error: {str(e)}")
-        return None
-
-def generate_radiology_report(uploaded_file, api_choice='groq'):
-    """Generate report using the selected API"""
-    if api_choice == 'groq':
-        client = initialize_groq_client()
-        if client:
-            return generate_radiology_report_groq(uploaded_file, client)
-        else:
-            st.error("Failed to initialize Groq client. Please check your API key.")
-            return None
-    else:  # OpenAI
-        client = initialize_openai_client()
-        if client:
-            return generate_radiology_report_openai(uploaded_file, client)
-        else:
-            st.error("OpenAI API key is required. Please provide your API key.")
-            return None
-
-# ======================
-# UI COMPONENTS
-# ======================
-def display_animated_logo():
-    """Display an animated medical logo"""
-    logo_b64 = encode_logo("MediSight\src\Round_image_depicting_a_futuristic_medical_image_a-1742282117033-photoaidcom-cropped.png")
-    
-    # If logo file doesn't exist, use the placeholder from encode_logo
-    st.markdown(
-        f"""
-        <div class="logo-container">
-            <div class="logo-pulse">
-                <img src="data:image/png;base64,{logo_b64}" width="200">
-            </div>
-        </div>
-        """, 
-        unsafe_allow_html=True
-    )
-
-def display_main_interface():
-    """Render primary application interface"""
-    # Display animated logo and titles
-    display_animated_logo()
-    
-    st.markdown('<h1 class="main-title">Radiology Analyzer</h1>', unsafe_allow_html=True)
-    st.markdown('<p class="sub-title">Advanced Medical Imaging Analysis</p>', unsafe_allow_html=True)
-    
-    # Action buttons
-    if st.session_state.get('analysis_result'):
-        st.markdown(
-            """
-            <div class="analysis-complete">
-                <svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" fill="currentColor" viewBox="0 0 16 16">
-                    <path d="M16 8A8 8 0 1 1 0 8a8 8 0 0 1 16 0zm-3.97-3.03a.75.75 0 0 0-1.08.022L7.477 9.417 5.384 7.323a.75.75 0 0 0-1.06 1.06L6.97 11.03a.75.75 0 0 0 1.079-.02l3.992-4.99a.75.75 0 0 0-.01-1.05z"/>
-                </svg>
-                Analysis Complete
-            </div>
-            """,
-            unsafe_allow_html=True
-        )
-        
-        col1, col2 = st.columns([1, 1])
-        with col1:
-            pdf_report = generate_pdf_report(st.session_state.analysis_result, st.session_state.uploaded_file)
-            st.download_button(
-                label="📄 Download PDF Report",
-                data=pdf_report,
-                file_name="radiology_report.pdf",
-                mime="application/pdf",
-                use_container_width=True,
-                help="Download formal PDF version of the report"
-            )
-
-        with col2:
-            if st.button("Clear Analysis 🗑️", use_container_width=True, help="Remove current results"):
-                st.session_state.pop('analysis_result', None)
-                st.session_state.pop('uploaded_file', None)
-                st.rerun()
-
-        # Display analysis results in a styled container
-        st.markdown("### 🎯 Radiological Findings Report")
-        st.markdown(
-            f'<div class="report-container"><div class="report-text">{st.session_state.analysis_result}</div></div>', 
-            unsafe_allow_html=True
-        )
-    else:
-        # Show a centered placeholder message
-        st.markdown(
-            """
-            <div style="text-align: center; margin-top: 50px; color: #9ca3af; padding: 100px 0;">
-                <svg xmlns="http://www.w3.org/2000/svg" width="64" height="64" fill="currentColor" viewBox="0 0 16 16" style="margin-bottom: 20px;">
-                    <path d="M8 0a8 8 0 1 0 0 16A8 8 0 0 0 8 0ZM1.5 8a6.5 6.5 0 1 1 13 0 6.5 6.5 0 0 1-13 0Zm4.879-2.773 4.264 2.559a.25.25 0 0 1 0 .428l-4.264 2.559A.25.25 0 0 1 6 10.559V5.442a.25.25 0 0 1 .379-.215Z"/>
-                </svg>
-                <p style="font-size: 1.2rem;">Upload a medical image to begin analysis</p>
-            </div>
-            """,
-            unsafe_allow_html=True
-        )
-
-def render_sidebar():
-    """Create sidebar interface elements"""
-    with st.sidebar:
-        st.markdown('<div class="sidebar-item">', unsafe_allow_html=True)
-        st.markdown('<div class="sidebar-title">Diagnostic Capabilities</div>', unsafe_allow_html=True)
-        st.markdown("""
-        - **Multi-Modality Analysis:** X-ray, MRI, CT, Ultrasound
-        - **Pathology Detection:** Fractures, tumors, infections
-        - **Comparative Analysis:** Track disease progression
-        - **Structured Reporting:** Standardized output format
-        - **Clinical Correlation:** Suggested next steps
-        """)
-        st.markdown("""
-        <div class="tooltip">
-            <strong>Disclaimer:</strong> This service does not provide medical advice.
-            <span class="tooltiptext">Always consult with a qualified healthcare professional for diagnosis and treatment.</span>
-        </div>
-        """, unsafe_allow_html=True)
-        st.markdown('</div>', unsafe_allow_html=True)
-        
-        # API Selection
-        st.markdown('<div class="sidebar-item">', unsafe_allow_html=True)
-        st.markdown('<div class="sidebar-title">API Configuration</div>', unsafe_allow_html=True)
-        
-        # API provider selection
-        api_provider = st.radio(
-            "Select AI Provider",
-            options=["Groq"],
-            index=0,  # Default to Groq
-        )
-        
-        # If OpenAI is selected, show API key input
-        # if api_provider == "OpenAI":
-        #     openai_key = st.text_input(
-        #         "OpenAI API Key", 
-        #         type="password",
-        #         help="Enter your OpenAI API key",
-        #         value=st.session_state.get('openai_api_key', '')
-        #     )
-        #     if openai_key:
-        #         st.session_state.openai_api_key = openai_key
-        
-        st.markdown('</div>', unsafe_allow_html=True)
-        
-        # Image Upload Section
-        st.markdown('<div class="sidebar-item">', unsafe_allow_html=True)
-        st.markdown('<div class="sidebar-title">Image Upload Section</div>', unsafe_allow_html=True)
-        
-        # Create a styled drop zone
-        st.markdown(
-            """
-            <div class="drop-zone">
-                <div class="drop-icon">📤</div>
-                <p>Drag and drop file here</p>
-                <p style="font-size: 0.8rem; color: #9ca3af;">or use the uploader below</p>
-            </div>
-            """,
-            unsafe_allow_html=True
-        )
-        
-        uploaded_file = st.file_uploader(
-            "Select Medical Image", 
-            type=ALLOWED_FILE_TYPES,
-            label_visibility="collapsed",
-            help="Supported formats: PNG, JPG, JPEG"
-        )
-
-        if uploaded_file:
-            st.session_state.uploaded_file = uploaded_file  # Store uploaded file in session state
-            
-            # Display image with a styled container
-            st.markdown('<div class="card">', unsafe_allow_html=True)
-            st.image(Image.open(uploaded_file), 
-                    caption="Uploaded Medical Image",
-                    use_container_width=True)
-            st.markdown('</div>', unsafe_allow_html=True)
-
-            # Styled analysis button
-            if st.button("▶️ Initiate Analysis", use_container_width=True):
-                with st.spinner("Processing image..."):
-                    # Use the selected API provider
-                    api_choice = 'groq' if api_provider == "Groq" else 'openai'
-                    report = generate_radiology_report(uploaded_file, api_choice)
-                    
-                    if report:
-                        st.session_state.analysis_result = report
-                        st.rerun()
-        st.markdown('</div>', unsafe_allow_html=True)
-
-# ======================
-# APPLICATION ENTRYPOINT
-# ======================
-def main():
-    """Primary application controller"""
-    # Check if dark mode is in session state, default to true
-    if 'dark_mode' not in st.session_state:
-        st.session_state.dark_mode = True
-    
-    configure_application()
-    
-    render_sidebar()
-    display_main_interface()
-
-if __name__ == "__main__":
-    main()
-