Update app.py

app.py

@@ -58,36 +58,59 @@ transform = transforms.Compose([
 ])
 
 # Prediction function for an uploaded image
-
 def predict_from_image_url(image_url):
     try:
         # Download the image from the provided URL
         response = requests.get(image_url)
         response.raise_for_status()
-        image = Image.open(BytesIO(response.content))
+        image = Image.open(BytesIO(response.content)).convert("RGB")  # Convert to RGB (3 channels)
 
         # Apply transformations
-        image_tensor = transform(image).unsqueeze(0)
+        image_tensor = transform(image).unsqueeze(0)  # Shape: [1, 3, 224, 224]
+        print(f"Input image tensor shape: {image_tensor.shape}")  # Debug: Should be [1, 3, 224, 224]
 
         # Perform prediction
         with torch.no_grad():
-            outputs = model(image_tensor)
+            outputs = model(image_tensor)  # Shape: [1, 3]
+            print(f"Model output shape: {outputs.shape}")  # Debug: Should be [1, 3]
+            probabilities = torch.softmax(outputs, dim=1)[0]  # Convert to probabilities
             predicted_class = torch.argmax(outputs, dim=1).item()
 
         # Interpret the result
         if predicted_class == 0:
-            return {"result": "The photo is of Fall Army Worm with problem ID 126."}
+            return {
+                "result": "The photo is of Fall Army Worm with problem ID 126.",
+                "probabilities": {
+                    "Fall Army Worm": f"{probabilities[0]*100:.2f}%",
+                    "Phosphorus Deficiency": f"{probabilities[1]*100:.2f}%",
+                    "Bacterial Leaf Blight": f"{probabilities[2]*100:.2f}%"
+                }
+            }
         elif predicted_class == 1:
-            return {"result": "The photo shows symptoms of Phosphorus Deficiency with Problem ID 142."}
+            return {
+                "result": "The photo shows symptoms of Phosphorus Deficiency with Problem ID 142.",
+                "probabilities": {
+                    "Fall Army Worm": f"{probabilities[0]*100:.2f}%",
+                    "Phosphorus Deficiency": f"{probabilities[1]*100:.2f}%",
+                    "Bacterial Leaf Blight": f"{probabilities[2]*100:.2f}%"
+                }
+            }
         elif predicted_class == 2:
-            return {"result": "The photo shows symptoms of Bacterial Leaf Blight with Problem ID 203."}
+            return {
+                "result": "The photo shows symptoms of Bacterial Leaf Blight with Problem ID 203.",
+                "probabilities": {
+                    "Fall Army Worm": f"{probabilities[0]*100:.2f}%",
+                    "Phosphorus Deficiency": f"{probabilities[1]*100:.2f}%",
+                    "Bacterial Leaf Blight": f"{probabilities[2]*100:.2f}%"
+                }
+            }
         else:
             return {"error": "Unexpected class prediction."}
 
     except Exception as e:
         return {"error": str(e)}
 
-
+# Gradio interface
 demo = gr.Interface(
     fn=predict_from_image_url,
     inputs="text",