Update app.py

app.py

@@ -1,166 +1,99 @@
 import streamlit as st
-import pandas as pd
-import matplotlib.pyplot as plt
-from datetime import date
-from transformers import pipeline, set_seed
+from transformers import pipeline
 from gtts import gTTS
 import tempfile
-import requests
+import os
+from googletrans import Translator
 
-# Set your API keys here
-news_api_key = '11a477927da6466ca67f7fc512a2d8f2'  # Add your News API key
+# Initialize Hugging Face text-generation model pipeline (you can choose a different model like GPT-2, GPT-3, etc.)
+generator = pipeline("text-generation", model="gpt2")
 
-# Initialize Hugging Face pipeline for text generation
-generator = pipeline("text-generation", model="gpt2")  # You can replace it with a different model
+# Initialize Translator for language detection and translation
+translator = Translator()
 
-# Function for MedBot using Hugging Face's GPT-2
-def query_huggingface(prompt):
+# Function for text generation
+def generate_text(user_input):
     try:
-        response = generator(prompt, max_length=100, num_return_sequences=1)
+        # Generate response using the GPT-2 model
+        response = generator(user_input, max_length=150, num_return_sequences=1)
         return response[0]['generated_text']
     except Exception as e:
-        return f"Error interacting with Hugging Face API: {e}"
+        return f"Error generating text: {e}"
 
-# Function for text-to-speech conversion
-def text_to_speech(text):
+# Function for text-to-speech with language support
+def text_to_speech(text, lang):
     try:
-        tts = gTTS(text=text, lang='en')
+        tts = gTTS(text=text, lang=lang)
+        # Create a temporary file to store the audio
         temp_audio = tempfile.NamedTemporaryFile(delete=False, suffix=".mp3")
         tts.save(temp_audio.name)
         return temp_audio.name
     except Exception as e:
         return f"Error generating speech: {e}"
 
-# MedBot chatbot function
-def med_chatbot(user_input):
-    llm_response = query_huggingface(user_input)
-    if "Error" in llm_response:
-        return llm_response, None
-    audio_path = text_to_speech(llm_response)
-    return llm_response, audio_path
-
-# Function to fetch the latest healthcare/therapist news
-def get_healthcare_news():
-    url = f'https://newsapi.org/v2/everything?q=healthcare OR therapy&apiKey={news_api_key}'
-    try:
-        response = requests.get(url)
-        news_data = response.json()
-        articles = news_data.get('articles', [])
-        news_list = []
-        for article in articles[:5]:  # Get the top 5 articles
-            title = article.get('title', 'No Title')
-            description = article.get('description', 'No Description')
-            url = article.get('url', '')
-            news_list.append(f"**{title}**\n{description}\n[Read more]({url})\n\n")
-        return news_list
-    except Exception as e:
-        return [f"Error fetching news: {e}"]
-
 # Streamlit Page Configuration
-st.set_page_config(page_title="Healthcare Application", layout="wide")
-
-# Sidebar Navigation
-menu = st.sidebar.radio("Navigation", ["MedBot", "Therapist Dashboard", "Healthcare News"])
-
-# MedBot Interface
-if menu == "MedBot":
-    st.title("MedBot")
-    st.write("A real-time medical chatbot with text-to-speech capabilities.")
-
-    user_input = st.text_input("Ask your medical question:")
-    if st.button("Submit"):
-        response, audio_path = med_chatbot(user_input)
-        st.text("Response:")
-        st.write(response)
-        if audio_path:
+st.set_page_config(page_title="GPT-2 Chatbot with Text-to-Speech (Multi-language)", layout="wide")
+
+# Sidebar navigation
+menu = st.sidebar.radio("Navigation", ["Chatbot", "About"])
+
+# Language options
+languages = {
+    "English": "en",
+    "Spanish": "es",
+    "French": "fr",
+    "German": "de",
+    "Italian": "it",
+    "Portuguese": "pt",
+    "Dutch": "nl",
+    "Russian": "ru",
+    "Chinese": "zh",
+    "Japanese": "ja",
+    "Hindi": "hi"
+}
+
+# Chatbot Interface
+if menu == "Chatbot":
+    st.title("GPT-2 Chatbot with Multi-language Support")
+    st.write("Welcome to the GPT-2 powered chatbot with multiple language support. Ask any question, and I will generate a response.")
+
+    # Language selection
+    lang_choice = st.selectbox("Select Language for Chatbot", list(languages.keys()))
+    lang_code = languages[lang_choice]
+
+    # Text input for the user
+    user_input = st.text_input(f"Ask your question in {lang_choice}:", "")
+    
+    if st.button("Generate Response"):
+        if user_input:
+            # Translate user input to English (or the language of the model)
+            translated_input = translator.translate(user_input, src=lang_code, dest="en").text
+            # Generate response from GPT-2 model
+            generated_text = generate_text(translated_input)
+            
+            # Translate the response back to the selected language
+            translated_response = translator.translate(generated_text, src="en", dest=lang_code).text
+            st.write(f"### Response in {lang_choice}:")
+            st.write(translated_response)
+
+            # Convert response to speech in selected language
+            audio_path = text_to_speech(translated_response, lang_code)
             st.audio(audio_path, format="audio/mp3")
-
-# Therapist Dashboard Interface
-elif menu == "Therapist Dashboard":
-    st.title("Therapist Dashboard")
-    st.write("A comprehensive tool for therapists to manage sessions, track progress, and use therapy tools.")
-
-    dashboard_menu = st.sidebar.radio(
-        "Dashboard Menu",
-        ["Home", "Session Tracker", "Mood Tracker", "Therapy Tools", "Patient Analytics"]
-    )
-
-    if dashboard_menu == "Home":
-        st.header("Welcome to the Therapist Dashboard")
-        st.write("This application provides tools to enhance your therapy practice.")
-
-    elif dashboard_menu == "Session Tracker":
-        st.header("Session Tracker")
-        session_date = st.date_input("Session Date", value=date.today())
-        session_time = st.time_input("Session Time")
-        patient_name = st.text_input("Patient Name")
-        session_notes = st.text_area("Session Notes")
-
-        if st.button("Save Session"):
-            st.success(f"Session for {patient_name} on {session_date} at {session_time} saved successfully!")
-
-        st.write("### Recent Sessions")
-        sessions_data = pd.DataFrame({
-            "Date": ["2024-12-20", "2024-12-21"],
-            "Time": ["10:00 AM", "3:00 PM"],
-            "Patient": ["John Doe", "Jane Smith"],
-            "Notes": ["Discussed anxiety triggers.", "CBT exercise on thought patterns."]
-        })
-        st.table(sessions_data)
-
-    elif dashboard_menu == "Mood Tracker":
-        st.header("Mood Tracker")
-        mood_date = st.date_input("Date", value=date.today(), key="mood_date")
-        mood_level = st.slider("Mood Level (1-10)", 1, 10, 5)
-        mood_notes = st.text_area("Notes", key="mood_notes")
-
-        if st.button("Save Mood Entry"):
-            st.success("Mood entry saved!")
-
-        st.write("### Mood Trends")
-        mood_data = pd.DataFrame({
-            "Date": pd.date_range(start="2024-12-15", periods=7),
-            "Mood Level": [5, 6, 7, 5, 8, 9, 7]
-        })
-        fig, ax = plt.subplots()
-        ax.plot(mood_data["Date"], mood_data["Mood Level"], marker="o")
-        ax.set_title("Mood Trends")
-        ax.set_xlabel("Date")
-        ax.set_ylabel("Mood Level")
-        st.pyplot(fig)
-
-    elif dashboard_menu == "Therapy Tools":
-        st.header("Therapy Tools")
-        tools = st.selectbox("Select a Tool", ["CBT Worksheet", "Guided Relaxation", "Stress Management Tips"])
-
-        if tools == "CBT Worksheet":
-            st.text_area("Identify Negative Thoughts")
-            st.text_area("Challenge Negative Thoughts")
-            st.text_area("Replace with Positive Thoughts")
-
-        elif tools == "Guided Relaxation":
-            st.audio("https://example.com/guided_relaxation.mp3")  # Replace with actual file URL
-
-        elif tools == "Stress Management Tips":
-            st.write("- Practice deep breathing exercises.\n- Maintain a regular sleep schedule.\n- Engage in physical activity.")
-
-    elif dashboard_menu == "Patient Analytics":
-        st.header("Patient Analytics")
-        analytics_data = pd.DataFrame({
-            "Patient": ["John Doe", "Jane Smith"],
-            "Sessions Conducted": [10, 8],
-            "Average Mood": [7.2, 8.1]
-        })
-        st.bar_chart(analytics_data.set_index("Patient"))
-
-        if st.button("Generate Report"):
-            st.success("Report generated successfully!")
-
-# Healthcare News Section
-elif menu == "Healthcare News":
-    st.title("Latest Healthcare & Therapist News")
-    st.write("Stay updated with the latest news and trends in healthcare and therapy.")
+        else:
+            st.warning("Please enter a question.")
+
+# About Section
+elif menu == "About":
+    st.title("About")
+    st.write("""
+    This is a simple web application powered by GPT-2 (Hugging Face Transformers) and Google Text-to-Speech (gTTS).
+    
+    **Features:**
+    - Ask a question in your preferred language, and GPT-2 will generate a response.
+    - The response will be translated back to your language and also converted to speech.
     
-    news_list = get_healthcare_news()
-    for news_item in news_list:
-        st.markdown(news_item)
+    **How it works:**
+    - GPT-2 model generates text based on your input in any language.
+    - The text is then translated to English, processed by GPT-2, and translated back to your preferred language.
+    - The response is converted to speech using the `gTTS` library in the selected language.
+    """)