Update app.py

app.py

@@ -1,6 +1,4 @@
-# Minimal version for Hugging Face Spaces
-# File: app.py
-
+# Minimal app.py for Hugging Face Spaces - Vision 2030 Virtual Assistant
 import gradio as gr
 import time
 import logging
@@ -24,13 +22,11 @@ import spaces
 logging.basicConfig(
     level=logging.INFO,
     format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
-    handlers=[
-        logging.StreamHandler()
-    ]
+    handlers=[logging.StreamHandler()]
 )
 logger = logging.getLogger('vision2030_assistant')
 
-# Check for GPU availability (but don't rely on it)
+# Check for GPU availability
 has_gpu = torch.cuda.is_available()
 logger.info(f"GPU available: {has_gpu}")
 
@@ -84,13 +80,12 @@ class Vision2030Assistant:
         """Create fallback embedding methods if model loading fails"""
         logger.warning("Using fallback embedding methods")
         
-        # Simple fallback using character-level encoding (not a real embedding, just for demo)
+        # Simple fallback using character-level encoding
         def simple_encode(text, dim=384):
             import hashlib
             # Create a hash of the text
             hash_object = hashlib.md5(text.encode())
             # Use the hash to seed a random number generator
-            import numpy as np
             np.random.seed(int(hash_object.hexdigest(), 16) % 2**32)
             # Generate a random vector
             return np.random.randn(dim).astype(np.float32)
@@ -467,8 +462,11 @@ class Vision2030Assistant:
         try:
             logger.info(f"Processing uploaded file")
             
-            # Use PyPDF2 to read the file content directly
-            reader = PyPDF2.PdfReader(file)
+            # Convert bytes to file-like object
+            file_stream = io.BytesIO(file)
+            
+            # Use PyPDF2 to read the file content
+            reader = PyPDF2.PdfReader(file_stream)
             
             # Extract text from the PDF
             full_text = ""
@@ -516,129 +514,118 @@ class Vision2030Assistant:
 
 # Create the Gradio interface
 def create_interface():
-    try:
-        # Initialize the assistant
-        assistant = Vision2030Assistant()
-        
-        def chat(message, history):
-            if not message or message.strip() == "":
-                return history, ""
-            
-            # Generate response
-            reply = assistant.generate_response(message)
-            
-            # Update history
-            history.append((message, reply))
-            
+    # Initialize the assistant
+    assistant = Vision2030Assistant()
+    
+    def chat(message, history):
+        if not message or message.strip() == "":
             return history, ""
         
-        def provide_feedback(history, rating, feedback_text):
-            # Record feedback for the last conversation
-            if history and len(history) > 0:
-                last_interaction = history[-1]
-                assistant.record_user_feedback(last_interaction[0], last_interaction[1], rating, feedback_text)
-                return f"Thank you for your feedback! (Rating: {rating}/5)"
-            return "No conversation found to rate."
-        
-        @spaces.GPU
-        def run_evaluation():
-            results = assistant.evaluate_on_test_set()
-            
-            # Create summary text
-            summary = f"""
-            Evaluation Results:
-            ------------------
-            Total questions evaluated: {len(results['detailed_results'])}
-            Overall factual accuracy: {results['average_factual_accuracy']:.2f}
-            Average response time: {results['average_response_time']:.4f} seconds
-            
-            Detailed Results:
-            """
-            
-            for i, result in enumerate(results['detailed_results']):
-                summary += f"\nQ{i+1}: {result['question']}\n"
-                summary += f"Reference: {result['reference']}\n"
-                summary += f"Response: {result['response']}\n"
-                summary += f"Accuracy: {result['factual_accuracy']:.2f}\n"
-                summary += "-" * 40 + "\n"
-            
-            # Return both the results summary and visualization
-            fig = assistant.visualize_evaluation_results(results)
-            
-            return summary, fig
-        
-        # Create the Gradio interface
-        with gr.Blocks() as demo:
-            gr.Markdown("# Vision 2030 Virtual Assistant 🌟")
-            gr.Markdown("Ask questions about Saudi Arabia's Vision 2030 in both Arabic and English")
-            
-            with gr.Tab("Chat"):
-                chatbot = gr.Chatbot(height=400)
-                msg = gr.Textbox(label="Your Question", placeholder="Ask about Vision 2030...")
-                with gr.Row():
-                    submit_btn = gr.Button("Submit")
-                    clear_btn = gr.Button("Clear Chat")
-                
-                gr.Markdown("### Provide Feedback")
-                with gr.Row():
-                    rating = gr.Slider(minimum=1, maximum=5, step=1, value=3, label="Rate the Response (1-5)")
-                    feedback_text = gr.Textbox(label="Additional Comments (Optional)")
-                feedback_btn = gr.Button("Submit Feedback")
-                feedback_result = gr.Textbox(label="Feedback Status")
-            
-            with gr.Tab("Evaluation"):
-                evaluate_btn = gr.Button("Run Evaluation on Test Set")
-                eval_output = gr.Textbox(label="Evaluation Results", lines=20)
-                eval_chart = gr.Plot(label="Evaluation Metrics")
-            
-            with gr.Tab("Upload PDF"):
-                gr.Markdown("""
-                ### Upload a Vision 2030 PDF Document
-                Upload a PDF document to enhance the assistant's knowledge base.
-                """)
-                
-                with gr.Row():
-                    file_input = gr.File(
-                        label="Select PDF File", 
-                        file_types=[".pdf"],
-                        type="binary"  # Important: Use binary mode
-                    )
-                
-                with gr.Row():
-                    upload_btn = gr.Button("Process PDF", variant="primary")
-                
-                with gr.Row():
-                    upload_status = gr.Textbox(
-                        label="Upload Status",
-                        placeholder="Upload status will appear here...",
-                        interactive=False
-                    )
-                
-                gr.Markdown("""
-                ### Notes:
-                - The PDF should contain text that can be extracted (not scanned images)
-                - After uploading, you can return to the Chat tab to ask questions about the uploaded content
-                - If no PDF is uploaded, the assistant will use default Vision 2030 information
-                """)
-            
-            # Set up event handlers
-            msg.submit(chat, [msg, chatbot], [chatbot, msg])
-            submit_btn.click(chat, [msg, chatbot], [chatbot, msg])
-            clear_btn.click(lambda: [], None, chatbot)
-            feedback_btn.click(provide_feedback, [chatbot, rating, feedback_text], feedback_result)
-            evaluate_btn.click(run_evaluation, None, [eval_output, eval_chart])
-            upload_btn.click(assistant.process_uploaded_pdf, [file_input], [upload_status])
-        
-        return demo
+        # Generate response
+        reply = assistant.generate_response(message)
+        
+        # Update history
+        history.append((message, reply))
+        
+        return history, ""
+    
+    def provide_feedback(history, rating, feedback_text):
+        # Record feedback for the last conversation
+        if history and len(history) > 0:
+            last_interaction = history[-1]
+            assistant.record_user_feedback(last_interaction[0], last_interaction[1], rating, feedback_text)
+            return f"Thank you for your feedback! (Rating: {rating}/5)"
+        return "No conversation found to rate."
+    
+    @spaces.GPU
+    def run_evaluation():
+        results = assistant.evaluate_on_test_set()
+        
+        # Create summary text
+        summary = f"""
+        Evaluation Results:
+        ------------------
+        Total questions evaluated: {len(results['detailed_results'])}
+        Overall factual accuracy: {results['average_factual_accuracy']:.2f}
+        Average response time: {results['average_response_time']:.4f} seconds
+        
+        Detailed Results:
+        """
+        
+        for i, result in enumerate(results['detailed_results']):
+            summary += f"\nQ{i+1}: {result['question']}\n"
+            summary += f"Reference: {result['reference']}\n"
+            summary += f"Response: {result['response']}\n"
+            summary += f"Accuracy: {result['factual_accuracy']:.2f}\n"
+            summary += "-" * 40 + "\n"
+        
+        # Return both the results summary and visualization
+        fig = assistant.visualize_evaluation_results(results)
+        
+        return summary, fig
+    
+    # Create the Gradio interface
+    with gr.Blocks() as demo:
+        gr.Markdown("# Vision 2030 Virtual Assistant 🌟")
+        gr.Markdown("Ask questions about Saudi Arabia's Vision 2030 in both Arabic and English")
+        
+        with gr.Tab("Chat"):
+            chatbot = gr.Chatbot(height=400)
+            msg = gr.Textbox(label="Your Question", placeholder="Ask about Vision 2030...")
+            with gr.Row():
+                submit_btn = gr.Button("Submit")
+                clear_btn = gr.Button("Clear Chat")
+            
+            gr.Markdown("### Provide Feedback")
+            with gr.Row():
+                rating = gr.Slider(minimum=1, maximum=5, step=1, value=3, label="Rate the Response (1-5)")
+                feedback_text = gr.Textbox(label="Additional Comments (Optional)")
+            feedback_btn = gr.Button("Submit Feedback")
+            feedback_result = gr.Textbox(label="Feedback Status")
+        
+        with gr.Tab("Evaluation"):
+            evaluate_btn = gr.Button("Run Evaluation on Test Set")
+            eval_output = gr.Textbox(label="Evaluation Results", lines=20)
+            eval_chart = gr.Plot(label="Evaluation Metrics")
+        
+        with gr.Tab("Upload PDF"):
+            gr.Markdown("""
+            ### Upload a Vision 2030 PDF Document
+            Upload a PDF document to enhance the assistant's knowledge base.
+            """)
+            
+            with gr.Row():
+                file_input = gr.File(
+                    label="Select PDF File", 
+                    file_types=[".pdf"],
+                    type="binary"  # This is critical - use binary mode
+                )
+            
+            with gr.Row():
+                upload_btn = gr.Button("Process PDF", variant="primary")
+            
+            with gr.Row():
+                upload_status = gr.Textbox(
+                    label="Upload Status",
+                    placeholder="Upload status will appear here...",
+                    interactive=False
+                )
+            
+            gr.Markdown("""
+            ### Notes:
+            - The PDF should contain text that can be extracted (not scanned images)
+            - After uploading, return to the Chat tab to ask questions about the uploaded content
+            """)
+        
+        # Set up event handlers
+        msg.submit(chat, [msg, chatbot], [chatbot, msg])
+        submit_btn.click(chat, [msg, chatbot], [chatbot, msg])
+        clear_btn.click(lambda: [], None, chatbot)
+        feedback_btn.click(provide_feedback, [chatbot, rating, feedback_text], feedback_result)
+        evaluate_btn.click(run_evaluation, None, [eval_output, eval_chart])
+        upload_btn.click(assistant.process_uploaded_pdf, [file_input], [upload_status])
     
-    except Exception as e:
-        logger.error(f"Error creating Gradio interface: {str(e)}")
-        # Create a simple fallback demo if there's an error
-        with gr.Blocks() as demo:
-            gr.Markdown("# Vision 2030 Virtual Assistant")
-            gr.Markdown("There was an error initializing the assistant. Please check the logs.")
-            gr.Markdown(f"Error: {str(e)}")
-        return demo
+    return demo
 
 # Launch the app
 demo = create_interface()