Update app.py

app.py

@@ -37,6 +37,9 @@ transform = transforms.Compose([
     transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
 ])
 
+# Global variable to store the file path
+file_path = None
+
 # Function to predict from image content
 def predict_from_image(image):
     # Ensure the image is a PIL Image
@@ -59,38 +62,33 @@ def predict_from_image(image):
     else:
         return {"error": "Unexpected class prediction."}
 
-# Function to handle image from URL or file path
-def predict_from_url_or_path(url=None, path=None):
-    try:
-        # If URL is provided, fetch and process image
-        if url:
-            response = requests.get(url)
-            response.raise_for_status()  # Ensure the request was successful
-            image = Image.open(BytesIO(response.content))
-            return predict_from_image(image)
-        
-        # If path is provided, open the image from the local path
-        elif path:
-            if not os.path.exists(path):
-                return {"error": f"File not found at {path}"}
-            image = Image.open(path)
-            return predict_from_image(image)
-        else:
-            return {"error": "No valid input provided."}
-    except Exception as e:
-        return {"error": f"Failed to process the input: {str(e)}"}
+# Function to handle the file path sent via POST request
+def process_file_path(file_path_input):
+    global file_path
+    file_path = file_path_input  # Store the file path
+    if not os.path.exists(file_path):
+        return {"error": f"File not found at {file_path}"}
+    image = Image.open(file_path)
+    return predict_from_image(image)
+
+# Function to fetch the result (for the GET request)
+def fetch_result():
+    if file_path:
+        image = Image.open(file_path)
+        return predict_from_image(image)
+    else:
+        return {"error": "No file path available. Please send a POST request with a file path first."}
 
 # Gradio interface
 iface = gr.Interface(
-    fn=lambda url, path: predict_from_url_or_path(url=url, path=path),
+    fn=process_file_path,
     inputs=[
-        gr.Textbox(label="Enter Image URL", placeholder="Provide a valid image URL (optional)", optional=True),
-        gr.Textbox(label="Or Enter Local Image Path", placeholder="Provide the local image path (optional)", optional=True),
+        gr.Textbox(label="Enter Local Image Path", placeholder="Provide the local image path"),
     ],
     outputs=gr.JSON(label="Prediction Result"),
-    live=True,
+    live=False,
     title="Maize Anomaly Detection",
-    description="Provide either an image URL or a local file path to detect anomalies in maize crops.",
+    description="Provide a local file path via POST request to process an image.",
 )
 
 # Launch the interface