Update app.py

app.py

@@ -1,14 +1,42 @@
 import gradio as gr
-from roboflow_model import predict_image
+from inference_sdk import InferenceHTTPClient
+from PIL import Image
+import requests
 
-def infer(image):
-    output = predict_image(image)
-    return output  # Gradio automatically handles NumPy images
+# Initialize the Roboflow client
+client = InferenceHTTPClient(
+    api_url="https://detect.roboflow.com",
+    api_key="dxkgGGHSZ3DI8XzVn29U"
+)
 
-gr.Interface(
-    fn=infer,
-    inputs=gr.Image(type="filepath"),
+# Prediction function
+def predict(image: Image.Image):
+    # Convert PIL image to bytes
+    image.save("temp.jpg")  # Save locally for upload
+    with open("temp.jpg", "rb") as f:
+        result = client.run_workflow(
+            workspace_name="naveen-kumar-hnmil",
+            workflow_id="detect-count-and-visualize-5",
+            images={"image": f},
+            use_cache=True
+        )
+    
+    # Get the annotated image URL from the result
+    annotated_url = result.get("visualizations", {}).get("image")
+    if annotated_url:
+        response = requests.get(annotated_url, stream=True)
+        return Image.open(response.raw)
+    else:
+        return "No annotated image returned."
+
+# Gradio UI
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
     outputs="image",
-    title="Roboflow Fault Detector",
-    description="Upload an image to detect faults using Roboflow workflow."
-).launch()
+    title="Solar Panel Fault Detection",
+    description="Upload an image and get predictions from your Roboflow workflow."
+)
+
+demo.launch()
+