Update app.py

app.py

@@ -7,10 +7,15 @@ from huggingface_hub import hf_hub_download
 from PIL import Image
 import requests
 from io import BytesIO
+from fastapi import FastAPI
+from gradio.routes import App
 
 # Define the number of classes
 num_classes = 2
 
+# In-memory storage for results
+results_cache = {}
+
 # Download model from Hugging Face
 def download_model():
     model_path = hf_hub_download(repo_id="jays009/Restnet50", filename="pytorch_model.bin")
@@ -79,7 +84,7 @@ def predict_from_url(url):
         print(f"Error during URL processing: {e}")
         return {"error": f"Failed to process the URL: {str(e)}"}
 
-# Gradio interface with logging
+# Main prediction function with caching
 def predict(image, url):
     try:
         if image:
@@ -89,27 +94,66 @@ def predict(image, url):
         else:
             result = {"error": "No input provided. Please upload an image or provide a URL."}
         
+        # Generate and store the event ID
+        event_id = id(result)  # Use Python's id() function to generate a unique identifier
+        results_cache[event_id] = result
+        
         # Log the result
-        event_id = id(result)  # Generate a simple event ID for logging purposes
         print(f"Event ID: {event_id}, Result: {result}")
-        return result
+        return {"event_id": event_id, "result": result}
 
     except Exception as e:
         print(f"Error in prediction function: {e}")
         return {"error": str(e)}
 
+# Function to retrieve result by event_id
+def get_result(event_id):
+    try:
+        # Convert event_id from string to int
+        event_id = int(event_id)
+        result = results_cache.get(event_id)
+        if result:
+            return result
+        else:
+            return {"error": "No result found for the provided event ID."}
+    except Exception as e:
+        return {"error": f"Invalid event ID: {str(e)}"}
+
+# Create a FastAPI app for handling the GET request
+app = FastAPI()
+
+@app.get("/result/{event_id}")
+def get_result_api(event_id: int):
+    return get_result(event_id)
+
 # Gradio interface setup
-iface = gr.Interface(
-    fn=predict,
-    inputs=[
-        gr.Image(type="pil", label="Upload an Image"),
-        gr.Textbox(label="Or Enter an Image URL", placeholder="Provide a valid image URL"),
-    ],
-    outputs=gr.JSON(label="Prediction Result"),
-    live=True,
-    title="Maize Anomaly Detection",
-    description="Upload an image or provide a URL to detect anomalies in maize crops.",
-)
-
-# Launch the interface
-iface.launch(share=True, show_error=True)
+iface = gr.Blocks()
+
+with iface:
+    gr.Markdown("# Maize Anomaly Detection")
+    with gr.Row():
+        image_input = gr.Image(type="pil", label="Upload an Image")
+        url_input = gr.Textbox(label="Or Enter an Image URL", placeholder="Provide a valid image URL")
+    output = gr.JSON(label="Prediction Result")
+    submit_button = gr.Button("Submit")
+    
+    submit_button.click(
+        fn=predict,
+        inputs=[image_input, url_input],
+        outputs=output
+    )
+    
+    # Event ID retrieval section
+    with gr.Row():
+        event_id_input = gr.Textbox(label="Event ID", placeholder="Enter Event ID")
+        event_output = gr.JSON(label="Retrieved Result")
+        retrieve_button = gr.Button("Get Result")
+        
+        retrieve_button.click(
+            fn=get_result,
+            inputs=[event_id_input],
+            outputs=event_output
+        )
+
+# Launch the Gradio interface
+iface.launch(share=True, show_error=True, server_name="0.0.0.0", server_port=7860)