Create app.py

app.py

@@ -1,76 +1,37 @@
 import gradio as gr
-import cv2
-import numpy as np
-import tempfile
-import os
-import pandas as pd
-from inference import InferencePipeline
-
-def run_inference(video_file):
-    if video_file is None:
-        return None, None, "Please upload a video file."
-
-    temp_video_path = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4").name
-    with open(temp_video_path, "wb") as f:
-        f.write(video_file.read())
-
-    annotated_frames = []
-    prediction_log = []
-
-    def on_prediction(result, frame):
-        if result.get("output_image") is not None:
-            image_np = result["output_image"].numpy_image
-            annotated_frames.append(image_np)
-
-        if result.get("predictions"):
-            for pred in result["predictions"].get("predictions", []):
-                prediction_log.append({
-                    "Class": pred["class"],
-                    "Confidence": round(pred["confidence"], 2),
-                    "Box": f'({pred["x"]}, {pred["y"]}, {pred["width"]}, {pred["height"]})'
-                })
-
-    pipeline = InferencePipeline.init_with_workflow(
-        api_key="dxkgGGHSZ3DI8XzVn29U",
-        workspace_name="naveen-kumar-hnmil",
-        workflow_id="detect-count-and-visualize-8",
-        video_reference=temp_video_path,
-        max_fps=5,
-        on_prediction=on_prediction
-    )
-
-    pipeline.start()
-    pipeline.join()
-
-    os.unlink(temp_video_path)
-
-    if not annotated_frames:
-        return None, None, "No faults detected or output frames returned."
-
-    # Save annotated video
-    height, width, _ = annotated_frames[0].shape
-    output_path = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4").name
-    out = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*"mp4v"), 5, (width, height))
-    for frame in annotated_frames:
-        out.write(cv2.cvtColor(frame, cv2.COLOR_RGB2BGR))
-    out.release()
-
-    video_bytes = open(output_path, "rb").read()
-    os.unlink(output_path)
-
-    # Prepare dataframe as CSV text for Gradio table
-    df = pd.DataFrame(prediction_log)
-
-    return video_bytes, df, "Inference completed successfully."
-
-with gr.Blocks() as demo:
-    gr.Markdown("# Solar Panel Fault Detection (Roboflow + Gradio)")
-    video_input = gr.Video(label="Upload Thermal Video", source="upload", type="file")
-    output_video = gr.Video(label="Annotated Output Video")
-    output_table = gr.Dataframe(headers=["Class", "Confidence", "Box"], interactive=False)
-    status = gr.Textbox(label="Status", interactive=False)
-
-    run_btn = gr.Button("Run Inference")
-    run_btn.click(fn=run_inference, inputs=video_input, outputs=[output_video, output_table, status])
-
-demo.launch()
+from transformers import pipeline
+from PIL import Image
+
+# 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."
+)
+
+if __name__ == "__main__":
+    demo.launch()