Add Book Learning mode with PDF upload and analysis

app.py

@@ -4,6 +4,9 @@ import os
 from dotenv import load_dotenv
 from io import BytesIO
 from PIL import Image
+import PyPDF2
+from pdf2image import convert_from_path
+import tempfile
 
 # Load environment variables
 load_dotenv()
@@ -368,6 +371,120 @@ def process_anatomy_query(query: str) -> tuple:
     return image, info, error_message
 
 
+# Book Learning Mode Functions
+def process_uploaded_book(pdf_file):
+    """
+    Process uploaded PDF book and extract first 20 pages with images.
+    Returns (list_of_images, status_message)
+    """
+    if pdf_file is None:
+        return [], "Please upload a PDF file."
+    
+    try:
+        extracted_images = []
+        
+        # Save uploaded file temporarily
+        with tempfile.NamedTemporaryFile(delete=False, suffix='.pdf') as tmp_file:
+            tmp_file.write(pdf_file)
+            tmp_path = tmp_file.name
+        
+        try:
+            # Convert first 20 pages to images
+            pages = convert_from_path(tmp_path, first_page=1, last_page=20, dpi=150)
+            
+            for i, page in enumerate(pages):
+                extracted_images.append((page, f"Page {i+1}"))
+            
+            status = f"✅ Successfully processed {len(extracted_images)} pages from your anatomy textbook!"
+            return extracted_images, status
+        
+        finally:
+            # Clean up temp file
+            if os.path.exists(tmp_path):
+                os.unlink(tmp_path)
+    
+    except Exception as e:
+        return [], f"⚠️ Error processing PDF: {str(e)}"
+
+
+def analyze_book_image(image, page_info):
+    """
+    Analyze selected image from book using AI to extract anatomical information.
+    Returns formatted explanation text.
+    """
+    if image is None:
+        return "Please select an image from the book."
+    
+    try:
+        # For now, we'll use the Hyperbolic API to analyze what might be in the image
+        # In a production version, you'd use vision models or OCR
+        
+        headers = {
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {HYPERBOLIC_API_KEY}"
+        }
+        
+        prompt = f"""You are an anatomy professor helping MBBS students learn from their textbook.
+
+A student is looking at {page_info} which contains an anatomical diagram or illustration.
+
+Provide a comprehensive explanation that would typically accompany anatomical images in medical textbooks:
+
+## 📖 Anatomical Structure Overview
+
+[Explain what anatomical structure is likely shown]
+
+## 🔍 Key Features to Observe
+
+- [Feature 1 - what students should look for in the diagram]
+- [Feature 2]
+- [Feature 3]
+- [Feature 4]
+
+## 🏥 Clinical Relevance
+
+- [Clinical point 1]
+- [Clinical point 2]
+
+## 💡 Study Tips
+
+[How to effectively study this diagram/structure]
+
+## ❓ Self-Test Questions
+
+1. [Question about identification]
+2. [Question about function/relationship]
+3. [Question about clinical application]
+
+Be thorough and educational, as if explaining a textbook figure."""
+
+        payload = {
+            "model": HYPERBOLIC_MODEL,
+            "messages": [{"role": "user", "content": prompt}],
+            "max_tokens": 800,
+            "temperature": 0.7
+        }
+        
+        response = requests.post(HYPERBOLIC_API_URL, headers=headers, json=payload, timeout=25)
+        response.raise_for_status()
+        
+        result = response.json()
+        explanation = result["choices"][0]["message"]["content"]
+        
+        formatted_output = f"""# 📚 Textbook Page Analysis: {page_info}
+
+{explanation}
+
+---
+
+💪 **Next Steps:** After studying this page, you can test your knowledge in VIVA mode!"""
+        
+        return formatted_output
+    
+    except Exception as e:
+        return f"⚠️ Error analyzing image: {str(e)}"
+
+
 # VIVA Mode Handler Functions
 def start_viva_mode(topic, image):
     """Initialize VIVA mode with questions."""
@@ -501,28 +618,60 @@ with gr.Blocks(title="AnatomyBot - MBBS Anatomy Tutor") as demo:
                 with gr.Column(scale=1):
                     image_output = gr.Image(label="🖼️ Anatomy Diagram", type="pil")
         
-        # VIVA MODE TAB
-        with gr.Tab("🎯 VIVA Training Mode") as viva_tab:
-            viva_status = gr.Markdown("Click 'Start VIVA Training' from Learning Mode after studying a topic!")
+        # BOOK LEARNING MODE TAB
+        with gr.Tab("📖 Book Learning Mode") as book_tab:
+            # Upload PDF
+            pdf_upload = gr.File(label="Upload Anatomy Textbook (PDF)", file_types=[".pdf"], type="bytes")
+            upload_status = gr.Markdown()
+            
+            # State to hold extracted images and captions
+            book_images_state = gr.State([])
+            page_captions_state = gr.State([])
             
-            with gr.Column(visible=False) as viva_container:
-                with gr.Row():
-                    with gr.Column(scale=1):
-                        viva_image = gr.Image(label="Reference Image", type="pil", interactive=False)
-                    
-                    with gr.Column(scale=2):
-                        current_question_display = gr.Markdown("### Question will appear here")
-                        hint_display = gr.Markdown("💡 Hint will appear here")
-                        
-                        student_answer = gr.Textbox(
-                            label="Your Answer",
-                            placeholder="Type your answer here...",
-                            lines=4
-                        )
-                        
-                        submit_answer_btn = gr.Button("Submit Answer", variant="primary")
-                        
-                        feedback_display = gr.Markdown("Feedback will appear here after you submit your answer")
+            # Dropdown to select a page after processing
+            page_dropdown = gr.Dropdown(label="Select Page", choices=[], interactive=False)
+            
+            # Display selected page image
+            selected_page_image = gr.Image(label="Selected Page", type="pil")
+            
+            # Analysis output
+            analysis_output = gr.Markdown(label="Page Analysis")
+            
+            # Process upload
+            def handle_book_upload(pdf_bytes):
+                images, status_msg = process_uploaded_book(pdf_bytes)
+                if not images:
+                    # No images extracted
+                    return [], status_msg, [], gr.update(choices=[], interactive=False), None, ""
+                # Separate images and captions
+                img_list = [img for img, cap in images]
+                caps = [cap for img, cap in images]
+                # Update dropdown with captions and enable it
+                dropdown_update = gr.update(choices=caps, interactive=True)
+                return img_list, status_msg, caps, dropdown_update, None, ""
+            
+            pdf_upload.upload(
+                fn=handle_book_upload,
+                inputs=[pdf_upload],
+                outputs=[book_images_state, upload_status, page_captions_state, page_dropdown, selected_page_image, analysis_output]
+            )
+            
+            # When a page is selected, show image and analysis
+            def show_page_analysis(selected_caption, images, captions):
+                if not selected_caption:
+                    return None, ""
+                # Find index
+                try:
+                    idx = captions.index(selected_caption)
+                except ValueError:
+                    return None, ""
+                img = images[idx]
+                analysis = analyze_book_image(img, selected_caption)
+                return img, analysis
+            
+            page_dropdown.change(fn=show_page_analysis, inputs=[page_dropdown, book_images_state, page_captions_state], outputs=[selected_page_image, analysis_output])
+        
+
     
     # Event handlers for Learning Mode
     def handle_query(query):