Update app.py

app.py

@@ -4,76 +4,266 @@ from peft import AutoPeftModelForSeq2SeqLM
 import torch
 
 # Define the Hugging Face repository ID
-repo_id = "sairika/FLAN-T5-Base-dialogsum-lora" # Replace 'your-username' with your Hugging Face username
+repo_id = "sairika/FLAN-T5-Base-dialogsum-lora"
+
+# Sample dialogues for examples
+SAMPLE_DIALOGUES = [
+    """Alice: Hi Bob, how was your meeting with the client today?
+Bob: It went really well! They loved our proposal and want to move forward.
+Alice: That's fantastic news! What's the next step?
+Bob: We need to prepare the contract and schedule a follow-up meeting next week.
+Alice: Great, I'll help you with the contract preparation.""",
+    
+    """Customer: I'm having trouble with my internet connection. It keeps dropping out.
+Support: I'm sorry to hear that. Let me help you troubleshoot this issue.
+Customer: I've already tried restarting my router.
+Support: Okay, let's check your signal strength. Can you tell me what lights are showing on your modem?
+Customer: There are green lights for power and internet, but the wifi light is blinking orange.
+Support: That indicates a wifi connectivity issue. Let's reset your wifi settings.""",
+    
+    """Mom: Have you finished your homework yet?
+Child: Almost done! I just have math left.
+Mom: Do you need any help with it?
+Child: Actually yes, I'm stuck on these algebra problems.
+Mom: Let me take a look. Oh, these are quadratic equations. Remember the formula we practiced?
+Child: Oh right! ax² + bx + c = 0. Thanks mom!"""
+]
 
 # Load the tokenizer and the PEFT model
 try:
     tokenizer = AutoTokenizer.from_pretrained(repo_id)
-    model = AutoPeftModelForSeq2SeqLM.from_pretrained(repo_id, device_map="auto", torch_dtype=torch.bfloat16)
+    model = AutoPeftModelForSeq2SeqLM.from_pretrained(
+        repo_id, 
+        device_map="auto", 
+        torch_dtype=torch.bfloat16
+    )
     print(f"✅ Model and tokenizer loaded successfully from {repo_id}")
-
-    # Define the create_prompts function (copied from the notebook)
+    
     def create_prompts(dialogues, model_type):
         """Create appropriate prompts based on model type"""
         if model_type in ['flan-t5', 't5']:
-            # T5-style models work better with explicit instructions
             prompts = [f"Summarize the following conversation.\n\n{dialogue}\n\nSummary: "
                       for dialogue in dialogues]
         else:
-            # BART-style models can work with direct input
             prompts = dialogues
         return prompts
-
-    # Define the summary generation function
-    def generate_summary(dialogue):
-        """Generates a summary for a given dialogue"""
-        model_type = 'flan-t5' if 'flan-t5' in repo_id else 't5' if 't5' in repo_id else 'bart'
-        prompt = create_prompts([dialogue], model_type)[0]
-
-        inputs = tokenizer(
-            prompt,
-            return_tensors="pt",
-            padding=True,
-            truncation=True,
-            max_length=512
-        ).to(model.device)
-
-        model.eval()
-        with torch.no_grad():
-            outputs = model.generate(
-                **inputs,
-                max_new_tokens=128,
-                num_beams=4,
-                length_penalty=0.6,
-                early_stopping=True,
-                do_sample=False
-            )
-
-        summary = tokenizer.decode(outputs[0], skip_special_tokens=True)
-        return summary
-
-    # Create Gradio interface
-    iface = gr.Interface(
-        fn=generate_summary,
-        inputs=gr.Textbox(label="Enter Dialogue:"),
-        outputs=gr.Textbox(label="Generated Summary:"),
-        title="Dialogue Summarization with Fine-tuned LoRA Model",
-        description=f"Summarize conversations using the fine-tuned model from {repo_id}"
-    )
-
+    
+    def generate_summary(dialogue, max_length, num_beams, length_penalty):
+        """Generates a summary for a given dialogue with customizable parameters"""
+        if not dialogue.strip():
+            return "⚠️ Please enter a dialogue to summarize."
+        
+        try:
+            model_type = 'flan-t5' if 'flan-t5' in repo_id else 't5' if 't5' in repo_id else 'bart'
+            prompt = create_prompts([dialogue], model_type)[0]
+            
+            inputs = tokenizer(
+                prompt,
+                return_tensors="pt",
+                padding=True,
+                truncation=True,
+                max_length=512
+            ).to(model.device)
+            
+            model.eval()
+            with torch.no_grad():
+                outputs = model.generate(
+                    **inputs,
+                    max_new_tokens=int(max_length),
+                    num_beams=int(num_beams),
+                    length_penalty=float(length_penalty),
+                    early_stopping=True,
+                    do_sample=False
+                )
+            
+            summary = tokenizer.decode(outputs[0], skip_special_tokens=True)
+            return f"📝 **Summary:** {summary}"
+            
+        except Exception as e:
+            return f"❌ Error generating summary: {str(e)}"
+    
+    # Custom CSS for better styling
+    custom_css = """
+    .gradio-container {
+        font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+    }
+    .header {
+        text-align: center;
+        background: linear-gradient(90deg, #667eea 0%, #764ba2 100%);
+        color: white;
+        padding: 2rem;
+        border-radius: 10px;
+        margin-bottom: 2rem;
+    }
+    .footer {
+        text-align: center;
+        margin-top: 2rem;
+        padding: 1rem;
+        background-color: #f8f9fa;
+        border-radius: 10px;
+    }
+    """
+    
+    # Create the Gradio interface
+    with gr.Blocks(css=custom_css, title="Dialogue Summarization Demo") as iface:
+        # Header
+        gr.HTML("""
+        <div class="header">
+            <h1>🤖 AI Dialogue Summarization</h1>
+            <p>Transform lengthy conversations into concise, meaningful summaries using a fine-tuned FLAN-T5 model</p>
+        </div>
+        """)
+        
+        with gr.Row():
+            with gr.Column(scale=2):
+                dialogue_input = gr.Textbox(
+                    label="📝 Enter Your Dialogue",
+                    placeholder="Paste your conversation here...\n\nExample:\nPerson A: Hello, how are you?\nPerson B: I'm doing well, thanks for asking!",
+                    lines=10,
+                    max_lines=20
+                )
+                
+                with gr.Row():
+                    summarize_btn = gr.Button("✨ Generate Summary", variant="primary", size="lg")
+                    clear_btn = gr.Button("🗑️ Clear", variant="secondary")
+            
+            with gr.Column(scale=1):
+                gr.Markdown("### ⚙️ Generation Parameters")
+                max_length = gr.Slider(
+                    minimum=50,
+                    maximum=256,
+                    value=128,
+                    step=10,
+                    label="Max Summary Length",
+                    info="Maximum number of tokens in the summary"
+                )
+                num_beams = gr.Slider(
+                    minimum=1,
+                    maximum=8,
+                    value=4,
+                    step=1,
+                    label="Number of Beams",
+                    info="Higher values = better quality but slower"
+                )
+                length_penalty = gr.Slider(
+                    minimum=0.1,
+                    maximum=2.0,
+                    value=0.6,
+                    step=0.1,
+                    label="Length Penalty",
+                    info="Controls summary length preference"
+                )
+        
+        # Output
+        summary_output = gr.Textbox(
+            label="🎯 Generated Summary",
+            lines=5,
+            show_copy_button=True
+        )
+        
+        # Examples section
+        gr.Markdown("### 💡 Try These Examples")
+        gr.Examples(
+            examples=SAMPLE_DIALOGUES,
+            inputs=dialogue_input,
+            label="Click on any example to load it:"
+        )
+        
+        # Model information
+        gr.Markdown(f"""
+        ### 📊 Model Information
+        - **Model:** {repo_id}
+        - **Base Architecture:** FLAN-T5-Base with LoRA fine-tuning
+        - **Task:** Dialogue Summarization
+        - **Training Dataset:** DialogSum
+        """)
+        
+        # Footer
+        gr.HTML("""
+        <div class="footer">
+            <p>Built with ❤️ using Gradio and Hugging Face Transformers</p>
+            <p><em>This demo showcases AI-powered dialogue summarization capabilities</em></p>
+        </div>
+        """)
+        
+        # Event handlers
+        summarize_btn.click(
+            fn=generate_summary,
+            inputs=[dialogue_input, max_length, num_beams, length_penalty],
+            outputs=summary_output
+        )
+        
+        clear_btn.click(
+            fn=lambda: ("", ""),
+            outputs=[dialogue_input, summary_output]
+        )
+    
     # Launch the interface
-    iface.launch()
+    iface.launch(
+        share=True,  # Creates a public link
+        show_error=True,
+        show_api=False,
+        favicon_path=None,
+        ssl_verify=False
+    )
 
 except Exception as e:
     print(f"❌ Error loading model or setting up Gradio: {e}")
-    # Provide a fallback or error message in the Gradio interface if possible
+    
+    # Enhanced error interface
     def error_message(dialogue):
-        return f"Error: Could not load the model or generate summary. Details: {e}"
-    error_iface = gr.Interface(
-        fn=error_message,
-        inputs=gr.Textbox(label="Enter Dialogue:"),
-        outputs=gr.Textbox(label="Error:"),
-        title="Error Loading Model",
-        description=f"There was an error loading the model from {repo_id}. Please check the logs."
-    )
-    error_iface.launch()
+        return f"""
+        ❌ **Error Loading Model**
+        
+        Unfortunately, there was an error loading the model from `{repo_id}`.
+        
+        **Possible causes:**
+        - Model repository not accessible
+        - Insufficient memory/resources  
+        - Network connectivity issues
+        - Invalid model format
+        
+        **Error details:** {str(e)}
+        
+        Please check the logs and try again later.
+        """
+    
+    custom_css_error = """
+    .error-container {
+        background-color: #fee;
+        border: 2px solid #fcc;
+        border-radius: 10px;
+        padding: 2rem;
+        margin: 2rem 0;
+    }
+    """
+    
+    with gr.Blocks(css=custom_css_error, title="Model Loading Error") as error_iface:
+        gr.HTML("""
+        <div class="error-container">
+            <h1>🚨 Model Loading Error</h1>
+            <p>There was an issue loading the dialogue summarization model.</p>
+        </div>
+        """)
+        
+        dialogue_input = gr.Textbox(
+            label="Enter Dialogue",
+            placeholder="The model could not be loaded...",
+            interactive=False
+        )
+        
+        error_output = gr.Textbox(
+            label="Error Details",
+            value=error_message(""),
+            interactive=False
+        )
+        
+        gr.Markdown(f"""
+        ### 🔧 Troubleshooting Steps:
+        1. Check if the model repository `{repo_id}` exists and is accessible
+        2. Verify you have sufficient system resources (RAM/GPU)
+        3. Ensure all required dependencies are installed
+        4. Check network connectivity to Hugging Face Hub
+        """)
+    
+    error_iface.launch(share=True)