Create app.py

app.py

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+import gradio as gr
+import torch
+from ultralytics import YOLO
+import cv2
+
+# Load the YOLOv8 model
+model = YOLO("best.pt")
+
+# Inference function
+def predict(image):
+    # Run prediction
+    results = model(image)
+
+    # Annotated image
+    annotated_img = results[0].plot()
+
+    # Prepare detection summary
+    detections = results[0].boxes
+    output_text = ""
+
+    if detections is not None and len(detections.cls) > 0:
+        output_text += "Prediction Summary:\n\n"
+        for i, box in enumerate(detections):
+            cls_id = int(box.cls.item())
+            conf = float(box.conf.item())
+            label = model.names[cls_id]
+            health_status = "Diseased" if label.lower() != "healthy" else "Healthy"
+
+            output_text += f"Status: {health_status}\n"
+            output_text += f"Disease: {label}\n"
+            output_text += f"Confidence: {conf:.2f}\n\n"
+    else:
+        output_text = "No disease detected. The cow appears to be healthy."
+
+    return annotated_img, output_text
+
+# Gradio Interface
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=[
+        gr.Image(type="pil", label="Annotated Image"),
+        gr.Textbox(label="Prediction Details")
+    ],
+    title="CowSense - Livestock Disease Detection",
+    description="Upload an image of a cow to detect health status and disease type using a trained YOLOv8 model."
+)
+
+iface.launch()