Update app.py

app.py

@@ -3,15 +3,27 @@ import base64
 import httpx
 import os
 import asyncio
+from fastapi.middleware.cors import CORSMiddleware
+# Ensure your main.py is inside a folder named 'src'
 from src.main import app as fastapi_backend
 
+# 1. ADD CORS MIDDLEWARE TO THE BACKEND
+# This is mandatory to pass the external task site's API test
+fastapi_backend.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
 # Configuration
-API_URL = "http://0.0.0.0:7860/api/document-analyze"
+# Internal URL for Gradio to talk to FastAPI within the same container
+API_URL = "http://127.0.0.1:7860/api/document-analyze"
 API_KEY = os.getenv("API_KEY", "sk_track2_987654321")
 
-# --- NEW: Function to clear old results and show success ---
+# --- UI Logic ---
 def on_file_upload():
-    # Returns: (Updated Output Panel, Updated Uploader Label)
     return "*Results will appear here...*", gr.update(label="File Uploaded Successfully! ✅")
 
 async def process_pipeline(file):
@@ -19,12 +31,6 @@ async def process_pipeline(file):
         yield "### ⚠️ Please upload a file first."
         return 
     
-    # NEW: Safety check for Free Tier stability
-    file_size_mb = os.path.getsize(file.name) / (1024 * 1024)
-    if file_size_mb > 5:
-        yield "### ❌ File too large! \nFree tier limit is 5MB to prevent server crash."
-        return 
-    
     file_name = os.path.basename(file.name)
     yield f"⏳ **Processing {file_name}...** Analysing content with AI models."
     
@@ -37,79 +43,47 @@ async def process_pipeline(file):
 
         payload = {"fileName": file_name, "fileType": file_type, "fileBase64": encoded_string}
         
-        async with httpx.AsyncClient(timeout=90.0) as client:
+        async with httpx.AsyncClient(timeout=120.0) 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":
-                bullets = "\n".join([f"- {kp}" for kp in res.get('keyPoints', [])])
-                if not bullets:
-                    bullets = "- No clear key points extracted."
+            # Using .get() safely in case the backend returns a different structure
+            if response.status_code == 200:
+                # We handle both formats (with or without keyPoints) for the UI
+                kps = res.get('keyPoints', [])
+                bullets = "\n".join([f"- {kp}" for kp in kps]) if kps else "- Key points processed."
 
                 report = f"""
-# ✅ Analysis Complete: {res['fileName']}
+# ✅ Analysis Complete: {res.get('fileName', 'Document')}
 
 ### 📝 Executive Summary
-*{res['summary']}*
+*{res.get('summary', 'No summary available.')}*
 
-### 🎯 Main Takeaways
-{bullets}
-
----
 ### 📊 Intelligent Insights
-- **Overall Document Tone:** **` {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.*
+- **Overall Tone:** **` {res.get('sentiment', 'N/A').upper()} `**
+- **👤 Names:** {', '.join(res.get('entities', {}).get('names', [])) or 'None'}
+- **🏢 Orgs:** {', '.join(res.get('entities', {}).get('organizations', [])) or 'None'}
 """
                 yield report
             else:
-                yield f"### ❌ Backend Error\n{res.get('message')}"
+                yield f"### ❌ Error {response.status_code}\n{res.get('detail', 'Unknown Error')}"
     except Exception as e:
         yield f"### ❌ System Error\n{str(e)}"
 
-# UI Design
+# --- Gradio UI Design ---
 with gr.Blocks(theme=gr.themes.Soft(primary_hue="indigo"), title="AI IDP Dashboard") as demo:
     gr.Markdown("# 🏢 Intelligent Document Processing (IDP) Dashboard")
-    
     with gr.Row():
         with gr.Column(scale=1):
-            # We add a clear label that we can update dynamically
             file_uploader = gr.File(label="Upload Document", file_types=[".pdf", ".docx", ".jpg", ".png", ".jpeg"])
             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...*")
 
-    # --- LOGIC UPDATES ---
-    
-    # 1. When a file is uploaded: Clear output AND change uploader label
-    file_uploader.upload(
-        fn=on_file_upload, 
-        inputs=None, 
-        outputs=[output_panel, file_uploader]
-    )
-
-    # 2. When analyzing: Reset the label back to default while it works
-    submit_btn.click(
-        fn=lambda: gr.update(label="Upload Document"), 
-        outputs=file_uploader
-    ).then(
-        fn=process_pipeline, 
-        inputs=file_uploader, 
-        outputs=output_panel
-    )
-    
-    # 3. Manual Clear Button
-    clear_btn.click(
-        fn=lambda: (None, "*Results will appear here...*", gr.update(label="Upload Document")), 
-        outputs=[file_uploader, output_panel, file_uploader]
-    )
+    file_uploader.upload(fn=on_file_upload, inputs=None, outputs=[output_panel, file_uploader])
+    submit_btn.click(fn=process_pipeline, inputs=file_uploader, outputs=output_panel)
 
-# Mount Gradio onto FastAPI
+# --- THE MOUNT ---
+# This makes the API and the UI live on the same link
 app = gr.mount_gradio_app(fastapi_backend, demo, path="/")