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README.md

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----
-title: EPECscan
-emoji: 🌖
-colorFrom: gray
-colorTo: pink
-sdk: gradio
-sdk_version: 4.36.1
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: EPECscan
+emoji: 🌖
+colorFrom: gray
+colorTo: pink
+sdk: gradio
+sdk_version: 4.36.1
+app_file: app.py
+pinned: false
+license: mit
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import os
+import time
+from ultralytics import YOLO
+import gradio as gr
+from PIL import Image
+
+# Load the trained model
+model = YOLO(r"C:\Users\ckkua\Documents\EPECscan\best.pt").cuda()
+
+# Output directory for processed images
+output_dir = r"C:\Users\ckkua\Documents\EPECscan"
+
+# Check if the directory exists, if not, create it
+if not os.path.exists(output_dir):
+    os.makedirs(output_dir)
+
+def process_image(image):
+    # Perform inference on the image
+    results = model(image)
+    predictions = []
+    
+    output_image_path = os.path.join(output_dir, "processed_image.jpg")
+    
+    for result in results:
+        # Save the processed image to the output directory
+        result.save(output_image_path)
+
+        # Extract predicted labels and confidence levels
+        if len(result.boxes) > 0:
+            for box in result.boxes:
+                label = result.names[box.cls.cpu().numpy()[0].astype(int)]
+                confidence = box.conf[0].cpu().numpy()
+                predictions.append((label, confidence))
+        else:
+            predictions.append(("None", 0.0))
+
+    return predictions, output_image_path
+
+def predict(image):
+    start_time = time.time()
+
+    # Save the uploaded image to a temporary file
+    temp_image_path = os.path.join(output_dir, "uploaded_image.jpg")
+    image.save(temp_image_path)
+
+    predictions, output_image_path = process_image(temp_image_path)
+    processing_time = time.time() - start_time
+
+    # Load the processed image for display
+    processed_image = Image.open(output_image_path)
+
+    return predictions, f"Processing Time: {processing_time:.2f} seconds", processed_image
+
+# Create the Gradio interface
+interface = gr.Interface(
+    fn=predict,
+    inputs=gr.inputs.Image(type="pil", label="Upload an Image"),
+    outputs=[
+        gr.outputs.JSON(label="Predictions"),
+        gr.outputs.Textbox(label="Processing Time"),
+        gr.outputs.Image(type="pil", label="Processed Image")
+    ],
+    title="Object Detection with Confidence Levels",
+    description="Upload an image to detect objects with confidence levels."
+)
+
+# Launch the interface
+interface.launch()
+
+

best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6c5ebf71b216f8f1d2ac2b0d27b8c101cece2b12f3aa2f3961f81429ed93b495
+size 52045761