Update app.py

app.py

@@ -1,37 +1,11 @@
 import gradio as gr
-from transformers import pipeline
-from PIL import Image
+from inference import predict
 
-# Load the model from Hugging Face Hub
-model = pipeline("object-detection", model="NaveenKumar5/Solar_panel_fault_detection")
-
-def detect_faults(image):
-    results = model(image)
-    annotated_image = image.copy()
-    
-    for result in results:
-        box = result["box"]
-        label = result["label"]
-        score = result["score"]
-
-        annotated_image = annotated_image.convert("RGB")
-        draw = ImageDraw.Draw(annotated_image)
-        draw.rectangle(
-            [box["xmin"], box["ymin"], box["xmax"], box["ymax"]],
-            outline="red",
-            width=3
-        )
-        draw.text((box["xmin"], box["ymin"] - 10), f"{label} ({score:.2f})", fill="red")
-
-    return annotated_image
-
-demo = gr.Interface(
-    fn=detect_faults,
-    inputs=gr.Image(type="pil"),
-    outputs=gr.Image(type="pil"),
-    title="Solar Panel Fault Detection",
-    description="Upload an image of a solar panel to detect faults using a custom-trained model."
-)
+iface = gr.Interface(fn=predict, 
+                     inputs=gr.Image(type="pil"), 
+                     outputs="image",
+                     title="Solar Panel Fault Detection",
+                     description="Upload a thermal image of a solar panel to detect faults using YOLOv5/Roboflow model.")
 
 if __name__ == "__main__":
-    demo.launch()
+    iface.launch()