updated python

app.py

@@ -7,6 +7,7 @@ from PIL import Image
 import base64
 import uvicorn
 import os
+import json
 
 from langchain_openai import ChatOpenAI
 from langchain.prompts import ChatPromptTemplate
@@ -20,9 +21,9 @@ os.makedirs("receiptimages", exist_ok=True)
 # Load trained YOLOv8n model
 model = YOLO("receipt-scanner/models/best.pt")
 
-# Init LangChain with GPT-5.1-nano
+# Init LangChain with OpenAI (✅ fixed model name)
 llm = ChatOpenAI(
-    model="gpt-5-nano",
+    model="gpt-4o-mini",  # use a valid model
     temperature=0,
 )
 
@@ -39,7 +40,7 @@ Return your answer strictly as JSON with keys: venue, date, total, category.
 """)
 
 def run_langchain_with_image(image_path: str):
-    """Send a cropped receipt to GPT-5.1-nano via LangChain."""
+    """Send a cropped receipt to GPT via LangChain and log details."""
     with open(image_path, "rb") as f:
         b64_image = base64.b64encode(f.read()).decode("utf-8")
 
@@ -49,6 +50,12 @@ def run_langchain_with_image(image_path: str):
             {"type": "image_url", "image_url": f"data:image/jpeg;base64,{b64_image}"}
         ]
     })
+
+    # 🔥 Debug logging
+    print(f"🖼️ Sent {len(b64_image)} base64 chars to GPT for {image_path}")
+    print(f"📤 GPT raw response: {result}")
+    print(f"📄 Extracted content: {result.content}")
+
     return result.content
 
 @app.post("/process-receipt")
@@ -82,6 +89,9 @@ async def process_receipt(file: UploadFile = File(...)):
                 "extracted": llm_result
             })
 
+    # 🔥 Log the full outputs for debugging
+    print("📦 Final API response:", json.dumps(outputs, indent=2))
+
     return {"status": "success", "receipts": outputs}
 
 if __name__ == "__main__":