Update main.py

main.py

@@ -5,9 +5,10 @@ from fastapi import FastAPI, File, UploadFile
 from fastapi.responses import JSONResponse
 from io import BytesIO
 from PIL import Image
-from tensorflow.keras.preprocessing.image import img_to_array, load_img
+from tensorflow.keras.preprocessing.image import img_to_array
 import tensorflow_addons as tfa
 import uvicorn
+import requests
 
 # Initialize FastAPI app
 app = FastAPI()
@@ -84,16 +85,19 @@ def predict_image(image_data):
     class_idx = int(np.argmax(predictions, axis=1)[0])  # Convert to int for JSON serialization
     confidence = float(predictions[0][class_idx])      # Convert to float for JSON serialization
     class_label = class_labels.get(class_idx, "Unknown")
-    if class_label is None:
-        return {
-            "class_index": class_idx,
-            "class_label": "Skipped/Invalid",
-            "confidence": confidence,
-        }
+
+    # Fetch additional data from external API
+    try:
+        response = requests.get(f"https://navpan2-sarva-ai-back.hf.space/kotlinback/{class_label}")
+        external_data = response.json() if response.status_code == 200 else {"error": "Failed to fetch external data"}
+    except Exception as e:
+        external_data = {"error": str(e)}
+
     return {
         "class_index": class_idx,
         "class_label": class_label,
         "confidence": confidence,
+        "external_data": external_data
     }
 
 # Route for health check
@@ -113,4 +117,4 @@ async def api_predict_image(file: UploadFile = File(...)):
 
 # Run the FastAPI app
 if __name__ == "__main__":
-    uvicorn.run(app, host="0.0.0.0", port=7860)
+    uvicorn.run(app, host="0.0.0.0", port=7860)