Create app.py

app.py

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+import gradio as gr
+from ultralytics import YOLO
+import cv2
+import numpy as np
+from PIL import Image, ImageDraw, ImageFont
+import sqlite3
+import pandas as pd
+
+# Load YOLOv10n model
+model = YOLO("best.pt")
+
+# Define label mappings
+label_mapping = {0: 'immature', 1: 'mature', 2: 'normal'}
+inverse_label_mapping = {'immature': 0, 'mature': 1, 'normal': 2}
+
+# Function to perform prediction
+def predict_image(input_image, name, patient_id):
+    if input_image is None:
+        return None, "Please Input The Image"
+
+    # Convert Gradio input image (PIL Image) to numpy array
+    image_np = np.array(input_image)
+
+    # Ensure the image is in the correct format
+    if len(image_np.shape) == 2:  # grayscale to RGB
+        image_np = cv2.cvtColor(image_np, cv2.COLOR_GRAY2RGB)
+    elif image_np.shape[2] == 4:  # RGBA to RGB
+        image_np = cv2.cvtColor(image_np, cv2.COLOR_RGBA2RGB)
+
+    # Perform inference with YOLOv10n model
+    results = model(image_np)
+
+    # Draw bounding boxes on the image
+    image_with_boxes = image_np.copy()
+    raw_predictions = []
+
+    if results[0].boxes:
+        # Iterate through each detected object
+        for i in range(len(results[0].boxes)):
+            box = results[0].boxes[i]
+            predicted_class = int(box.cls.item())
+            confidence = box.conf.item()
+
+            # Apply confidence threshold
+            if confidence >= 0.5:
+                # Map the predicted class to the label
+                label = label_mapping[predicted_class]
+
+                # Get the bounding box coordinates
+                xmin, ymin, xmax, ymax = map(int, box.xyxy[0])
+
+                # Assign color for the label
+                color = (0, 255, 0) if label == 'normal' else (0, 255, 255) if label == 'immature' else (255, 0, 0)
+                
+                # Draw the bounding box
+                cv2.rectangle(image_with_boxes, (xmin, ymin), (xmax, ymax), color, 2)
+                
+                # Draw the label with confidence
+                cv2.putText(image_with_boxes, f'{label} {confidence:.2f}', (xmin, ymin - 10), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
+                
+                raw_predictions.append(f"Label: {label}, Confidence: {confidence:.2f}, Box: [{xmin}, {ymin}, {xmax}, {ymax}]")
+
+    # Convert to PIL image for Gradio output
+    pil_image_with_boxes = Image.fromarray(image_with_boxes)
+
+    return pil_image_with_boxes, "\n".join(raw_predictions)
+
+# Gradio Interface
+def interface(name, patient_id, input_image):
+    if input_image is None:
+        return "Please upload an image."
+
+    # Run prediction
+    output_image, raw_result = predict_image(input_image, name, patient_id)
+    
+    return output_image, raw_result
+
+# Gradio Blocks
+with gr.Blocks() as demo:
+    with gr.Column():
+        gr.Markdown("# Cataract Detection System")
+        gr.Markdown("Upload an image to detect cataract and add patient details.")
+    
+    with gr.Column():
+        name = gr.Textbox(label="Name")
+        patient_id = gr.Textbox(label="Patient ID")
+        input_image = gr.Image(type="pil", label="Upload an Image", image_mode="RGB")
+
+    with gr.Column():
+        submit_btn = gr.Button("Submit")
+        output_image = gr.Image(type="pil", label="Predicted Image")
+
+    with gr.Row():
+        raw_result = gr.Textbox(label="Raw Result", lines=5)
+
+    submit_btn.click(fn=interface, inputs=[name, patient_id, input_image], outputs=[output_image, raw_result])
+
+# Launch the Gradio app
+demo.launch()