Create UI3

appwithSendgrid.py

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+import os
+import gradio as gr
+import torch
+import sqlite3
+from datetime import datetime
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+from huggingface_hub import snapshot_download
+from sendgrid import SendGridAPIClient
+from sendgrid.helpers.mail import Mail
+
+# Student Information
+My_info = "Student ID: 6319250G, Name: Aung Hlaing Tun"
+
+# Define Hugging Face Model Repo
+MODEL_REPO_ID = "ZAM-ITI-110/Distil_Bert_V3"
+
+# SendGrid API Key (Set in Hugging Face Space Secrets)
+SENDGRID_API_KEY = os.getenv("SENDGRID_API_KEY")  # Add this in Space Settings > Secrets
+
+# Load Model & Tokenizer from Hugging Face
+def load_model(repo_id):
+    """Download and load the model and tokenizer."""
+    cache_dir = "/home/user/app/hf_models"
+    os.makedirs(cache_dir, exist_ok=True)
+    download_dir = snapshot_download(repo_id, cache_dir=cache_dir, local_files_only=False)
+    model = AutoModelForSequenceClassification.from_pretrained(download_dir)
+    tokenizer = AutoTokenizer.from_pretrained(download_dir)
+    return model, tokenizer
+
+# Load Model
+model, tokenizer = load_model(MODEL_REPO_ID)
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model.to(device)
+model.eval()
+
+# Initialize SQLite Database
+def init_db():
+    """Create the tickets table if it doesn’t exist."""
+    conn = sqlite3.connect("tickets.db")
+    c = conn.cursor()
+    c.execute('''CREATE TABLE IF NOT EXISTS tickets
+                 (id INTEGER PRIMARY KEY AUTOINCREMENT, description TEXT, predicted_team TEXT,
+                  team_email TEXT, status TEXT, timestamp TEXT)''')
+    conn.commit()
+    conn.close()
+
+# Prediction Function (Single Ticket)
+def predict_team_and_email(text):
+    """Predict team and email for a single ticket description."""
+    if not text.strip():
+        return "", ""
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(device)
+    with torch.no_grad():
+        logits = model(**inputs).logits
+    pred = torch.argmax(logits, dim=-1).item()
+    
+    label_mapping = {
+        0: "Code Review Team", 1: "Functional Team", 2: "Infrastructure Team",
+        3: "Performance Team", 4: "Security Team"
+    }
+    email_mapping = {
+        0: "[email protected]", 1: "[email protected]",
+        2: "[email protected]", 3: "[email protected]",
+        4: "[email protected]"
+    }
+    
+    return label_mapping.get(pred, "Unknown"), email_mapping.get(pred, "Unknown")
+
+# Save Ticket to Database
+def save_ticket(description, predicted_team, team_email):
+    """Save a ticket to the SQLite database."""
+    conn = sqlite3.connect("tickets.db")
+    c = conn.cursor()
+    c.execute("INSERT INTO tickets (description, predicted_team, team_email, status, timestamp) VALUES (?, ?, ?, ?, ?)",
+              (description, predicted_team, team_email, "Open", datetime.now().isoformat()))
+    conn.commit()
+    conn.close()
+
+# Send Email via SendGrid
+def send_email(to_email, subject, content):
+    """Send an email using SendGrid."""
+    if not SENDGRID_API_KEY:
+        return "SendGrid API key not set."
+    message = Mail(
+        from_email="[email protected]",  # Replace with your verified sender email
+        to_emails=to_email,
+        subject=subject,
+        plain_text_content=content)
+    try:
+        sg = SendGridAPIClient(SENDGRID_API_KEY)
+        sg.send(message)
+        return "Email sent successfully!"
+    except Exception as e:
+        return f"Email failed: {str(e)}"
+
+# Send Tickets Function
+def send_tickets(*args):
+    """Save to DB and send emails for non-empty tickets."""
+    tickets = []
+    for i, (text, team, email) in enumerate(zip(args[::2], args[1::2], args[2::2]), 1):
+        if text.strip() and team and email:
+            save_ticket(text, team, email)
+            email_status = send_email(email, f"New Ticket Assigned to {team}", text)
+            tickets.append(f"Ticket {i}: '{text}' -> {team} ({email}) - {email_status}")
+    return "\n".join(tickets) + "\n\nProcessed successfully!" if tickets else "No tickets to send."
+
+# Clear Function
+def clear_all():
+    """Clear all inputs and outputs."""
+    return [""] * 19  # 6 tickets x (input, team, email) + 1 sent_output
+
+# Fetch Ticket History
+def get_ticket_history():
+    """Retrieve all tickets from the database."""
+    conn = sqlite3.connect("tickets.db")
+    df = pd.read_sql_query("SELECT * FROM tickets", conn)
+    conn.close()
+    return df
+
+# Gradio UI Setup
+init_db()  # Initialize database on startup
+with gr.Blocks(title="AI Ticket Classifier") as interface:
+    gr.Markdown("📩 **Development of an AI Ticket Classifier Model Using DistilBERT**")
+    gr.Markdown(f"*{My_info}*")
+    gr.Markdown(
+        """
+        **🔍 About this App**
+        - Predicts the appropriate **team** and **email** for up to 6 ticket descriptions.
+        - Click 'Predict' for each ticket, then 'Send Tickets' to save and notify teams.
+        """
+    )
+
+    # Ticket Entry Section
+    with gr.Column():
+        gr.Markdown("### Enter Ticket Descriptions")
+        inputs = []
+        outputs = []
+        buttons = []
+        for i in range(6):
+            with gr.Row():
+                ticket_input = gr.Textbox(lines=2, placeholder=f"Ticket {i+1} description...", label=f"Ticket {i+1}")
+                team_output = gr.Textbox(label="Predicted Team", interactive=False)
+                email_output = gr.Textbox(label="Team Email", interactive=False)
+                predict_btn = gr.Button(f"Predict {i+1}")
+                inputs.append(ticket_input)
+                outputs.extend([team_output, email_output])
+                buttons.append(predict_btn)
+
+    # Action Buttons
+    with gr.Row():
+        send_btn = gr.Button("Send Tickets")
+        clear_btn = gr.Button("Clear")
+
+    # Output for Sent Tickets
+    sent_output = gr.Textbox(label="Sent Tickets", interactive=False)
+
+    # Ticket History Section
+    with gr.Column():
+        gr.Markdown("### Ticket History")
+        history_btn = gr.Button("View Tickets")
+        history_output = gr.Dataframe()
+
+    # Event Handlers for Predict Buttons
+    for i, btn in enumerate(buttons):
+        btn.click(
+            fn=predict_team_and_email,
+            inputs=inputs[i],
+            outputs=[outputs[i*2], outputs[i*2 + 1]]
+        )
+
+    # Send and Clear Handlers
+    send_btn.click(
+        fn=send_tickets,
+        inputs=inputs + outputs,
+        outputs=sent_output
+    )
+    clear_btn.click(
+        fn=clear_all,
+        inputs=None,
+        outputs=inputs + outputs + [sent_output]
+    )
+
+    # History Handler
+    history_btn.click(
+        fn=get_ticket_history,
+        inputs=None,
+        outputs=history_output
+    )
+
+# Launch the interface
+interface.launch()