Update main.py

main.py

@@ -118,6 +118,32 @@ async def predict_plant_disease(plant_name: str, file: UploadFile = File(...)):
         JSON response with the predicted class and additional details from an external API.
     """
     # Ensure the plant name is valid
+    if len(plant_disease_dict.get(plant_name, [])) == 1:
+        single_disease = plant_disease_dict[plant_name][0]  # Get the only class available
+    
+        # 🔥 Fetch external data directly
+        try:
+            response = requests.get(f"https://navpan2-sarva-ai-back.hf.space/kotlinback/{single_disease}")
+            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 JSONResponse(content={
+            "plantName": external_data.get("plantName", plant_name),
+            "botanicalName": external_data.get("botanicalName", "Unknown"),
+            "diseaseDesc": {
+                "diseaseName": external_data.get("diseaseDesc", {}).get("diseaseName", single_disease),
+                "symptoms": external_data.get("diseaseDesc", {}).get("symptoms", "Not Available"),
+                "diseaseCauses": external_data.get("diseaseDesc", {}).get("diseaseCauses", "Not Available")
+            },
+            "diseaseRemedyList": [
+                {
+                    "title": remedy.get("title", "Unknown"),
+                    "diseaseRemedyShortDesc": remedy.get("diseaseRemedyShortDesc", "Not Available"),
+                    "diseaseRemedy": remedy.get("diseaseRemedy", "Not Available")
+                } for remedy in external_data.get("diseaseRemedyList", [])
+            ]
+        })
     if plant_name not in loaded_models:
         raise HTTPException(status_code=400, detail=f"Invalid plant name or model not loaded: {plant_name}")