Upload 2 files

app.py

@@ -1,58 +1,56 @@
-from clearml import Model
-from ultralytics import YOLO
-from PIL import Image, ImageDraw
-import gradio as gr
-
-# fetching the model from the ClearML 
-def fetch_model_from_clearml(model_id):
-    try:
-        # Load the model from ClearML
-        model = Model(model_id=model_id)
-        model_path = model.get_local_copy()
-        print(f"Model successfully fetched: {model_path}")
-        return model_path
-    except Exception as e:
-        print(f"Failed to fetch model from ClearML: {e}")
-        raise
-
-model_id = "65ca2d5aa3384a68b13333f792b147a3"  
-model_path = fetch_model_from_clearml(model_id)
-
-model = YOLO(model_path)
-
-
-def predict_and_visualize(image, confidence_threshold=0.3):
-    results = model.predict(image, conf=confidence_threshold)
-
-    boxes = results[0].boxes.xyxy.cpu().numpy()  # Bounding box coordinates
-    scores = results[0].boxes.conf.cpu().numpy()  # Confidence scores
-
-    # Draw bounding boxes on the image
-    draw = ImageDraw.Draw(image)
-    for box, score in zip(boxes, scores):
-        x_min, y_min, x_max, y_max = box
-        draw.rectangle([x_min, y_min, x_max, y_max], outline="red", width=3)
-        draw.text((x_min, y_min), f"{score:.2f}", fill="red")
-
-    return image
-
-
-# Gradio interface function
-def gradio_app(image):
-    result_image = predict_and_visualize(image)
-    return result_image
-
-
-# Create Gradio app
-interface = gr.Interface(
-    fn=gradio_app,
-    inputs=[
-        gr.Image(type="pil"),  # Input image
-    ],
-    outputs=gr.Image(type="pil"),  # Output image
-    title="Object Detection with model of Group G",
-    description="Upload an image to detect the objects.",
-)
-
-# Launch the Gradio app
-interface.launch(share=True)
+from clearml import Model
+from ultralytics import YOLO
+from PIL import Image, ImageDraw
+from huggingface_hub import hf_hub_download
+import gradio as gr
+
+
+# fetching the model from the ClearML 
+def fetch_model_from_HG():
+    try:
+        hg_model = hf_hub_download(repo_id="manvi23/GroupG", filename="best.pt")
+        return hg_model
+    except Exception as e:
+        print(f"Failed to fetch model from HuggingFace: {e}")
+        raise
+ 
+model_path = fetch_model_from_HG()
+model = YOLO(model_path)
+
+
+def predict_and_visualize(image, confidence_threshold=0.3):
+    results = model.predict(image, conf=confidence_threshold)
+
+    boxes = results[0].boxes.xyxy.cpu().numpy()  # Bounding box coordinates
+    scores = results[0].boxes.conf.cpu().numpy()  # Confidence scores
+
+    # Draw bounding boxes on the image
+    draw = ImageDraw.Draw(image)
+    for box, score in zip(boxes, scores):
+        x_min, y_min, x_max, y_max = box
+        draw.rectangle([x_min, y_min, x_max, y_max], outline="red", width=3)
+        draw.text((x_min, y_min), f"{score:.2f}", fill="red")
+
+    return image
+
+
+# Gradio interface function
+def gradio_app(image):
+    result_image = predict_and_visualize(image)
+    return result_image
+
+
+# Create Gradio app
+interface = gr.Interface(
+    fn=gradio_app,
+    inputs=[
+        gr.Image(type="pil"),  # Input image
+    ],
+    outputs=gr.Image(type="pil"),  # Output image
+    title="Object Detection with model of Group G",
+    description="Upload an image to detect the objects.",
+)
+
+# Launch the Gradio app
+interface.launch(share=True)
+

requirements.txt

@@ -4,4 +4,4 @@ clearml==1.16.4
 ultralytics==8.3.49
 numpy==1.26.4
 pillow==11.0.0
-
+huggingface_hub