Update app.py

app.py

@@ -3,23 +3,31 @@ import base64
 import httpx
 import os
 import asyncio
+# Import your backend FastAPI app from your src folder
+from src.main import app as fastapi_backend
 
 # Configuration
 API_URL = "http://0.0.0.0:7860/api/document-analyze"
 API_KEY = os.getenv("API_KEY", "sk_track2_987654321")
 
 async def process_pipeline(file):
-    # 1. Validate - Change 'return' to 'yield'
+    """
+    Handles the UI logic: 
+    1. Acknowledges the upload immediately.
+    2. Clears previous results.
+    3. Calls the AI backend and displays structured insights.
+    """
+    # 1. Validation - Yield error message if no file
     if file is None:
-        yield "### ⚠️ Please upload a file first.", gr.update(visible=False)
-        return  # This 'return' is now empty, which is allowed to stop the generator
+        yield "### ⚠️ Please upload a file first.", gr.update(visible=True)
+        return  # Stop the generator
     
-    # Pre-processing feedback
+    # 2. Instant Acknowledgment & Clearing Previous Results
     file_name = os.path.basename(file.name)
-    yield f"⏳ **Processing {file_name}...** Please wait.", gr.update(visible=True)
+    yield f"⏳ **Processing {file_name}...** Analysing content with AI models.", gr.update(visible=True)
     
     try:
-        # 2. Prepare Data
+        # 3. Prepare File Data
         ext = file_name.split('.')[-1].lower()
         file_type = "image" if ext in ['jpg', 'jpeg', 'png'] else ext
         
@@ -32,39 +40,41 @@ async def process_pipeline(file):
             "fileBase64": encoded_string
         }
         
-        # 3. Call Backend with Timeout for heavy AI tasks
-        async with httpx.AsyncClient(timeout=60.0) as client:
+        # 4. Call Backend API
+        async with httpx.AsyncClient(timeout=90.0) as client:
             headers = {"x-api-key": API_KEY}
             response = await client.post(API_URL, json=payload, headers=headers)
             res = response.json()
             
             if res.get("status") == "success":
-                # 4. Success Formatting
+                # 5. Success Report Formatting
                 report = f"""
-                # ✅ Analysis Complete: {res['fileName']}
-                
-                ### 🤖 AI Summary
-                {res['summary']}
-                
-                ---
-                ### 📊 Insights
-                - **Sentiment:** {res['sentiment'].upper()}
-                - **👤 Key Names:** {', '.join(res['entities']['names']) or 'None'}
-                - **🏢 Organizations:** {', '.join(res['entities']['organizations']) or 'None'}
-                - **📅 Dates:** {', '.join(res['entities']['dates']) or 'None'}
-                - **💰 Financials:** {', '.join(res['entities']['amounts']) or 'None'}
-                """
+# ✅ Analysis Complete: {res['fileName']}
+
+### 🤖 AI Summary
+{res['summary']}
+
+---
+### 📊 Intelligent Insights
+- **Sentiment:** **{res['sentiment'].upper()}**
+- **👤 Key Names:** {', '.join(res['entities']['names']) or 'None'}
+- **🏢 Organizations:** {', '.join(res['entities']['organizations']) or 'None'}
+- **📅 Dates:** {', '.join(res['entities']['dates']) or 'None'}
+- **💰 Financials/KPIs:** {', '.join(res['entities']['amounts']) or 'None'}
+
+*Note: OCR was used for image processing to extract text data.*
+"""
                 yield report, gr.update(visible=True)
             else:
                 yield f"### ❌ Backend Error\n{res.get('message')}", gr.update(visible=True)
 
     except Exception as e:
-        yield f"### ❌ System Error\n{str(e)}", gr.update(visible=True)
+        yield f"### ❌ System Error\nCould not connect to AI Engine: {str(e)}", gr.update(visible=True)
 
-# --- UI Layout ---
-with gr.Blocks(theme=gr.themes.Soft(primary_hue="indigo"), title="AI IDP System") as demo:
+# --- UI Layout Design ---
+with gr.Blocks(theme=gr.themes.Soft(primary_hue="indigo"), title="AI IDP Dashboard") as demo:
     gr.Markdown("# 🏢 Intelligent Document Processing (IDP) Dashboard")
-    gr.Markdown("Upload **PDFs, DOCX, or Images**  to extract structured AI insights.")
+    gr.Markdown("Extract structured intelligence from **PDFs, Word Docs, and Images** using multi-modal AI.")
     
     with gr.Row():
         with gr.Column(scale=1):
@@ -72,28 +82,36 @@ with gr.Blocks(theme=gr.themes.Soft(primary_hue="indigo"), title="AI IDP System"
                 label="Step 1: Upload Document", 
                 file_types=[".pdf", ".docx", ".jpg", ".png", ".jpeg"]
             )
-            submit_btn = gr.Button("🚀 Start AI Analysis", variant="primary")
-            clear_btn = gr.Button("🗑️ Clear All")
+            with gr.Row():
+                submit_btn = gr.Button("🚀 Analyze Document", variant="primary")
+                clear_btn = gr.Button("🗑️ Clear")
 
         with gr.Column(scale=2):
             output_panel = gr.Markdown(
-                value="*Results will appear here...*", 
+                value="*Upload a file and click analyze to see results...*", 
                 elem_id="results-box"
             )
 
-    # Functionality
+    # Functionality: Link Button to Pipeline
     submit_btn.click(
         fn=process_pipeline, 
         inputs=file_uploader, 
-        outputs=[output_panel, output_panel]
+        outputs=[output_panel, output_panel],
+        show_progress="full"
     )
     
-    # Reset UI logic
+    # Logic to Clear/Reset the UI
     def reset_ui():
         return None, "*Results will appear here...*"
     
     clear_btn.click(fn=reset_ui, outputs=[file_uploader, output_panel])
 
+# --- PRO DEPLOYMENT ---
+# This mounts your FastAPI backend onto the Gradio UI.
+# This makes both the API (for judges) and the Dashboard (for users) live on port 7860.
+app = gr.mount_gradio_app(fastapi_backend, demo, path="/")
+
 if __name__ == "__main__":
-    # If running standalone for testing
-    demo.launch()
+    import uvicorn
+    # Launch locally if needed
+    uvicorn.run(app, host="0.0.0.0", port=7860)