Fix: Import ImageDraw for drawing

.gradio/flagged/dataset1.csv

@@ -0,0 +1,2 @@
+Upload an Image,Image with Predictions,timestamp
+.gradio\flagged\Upload an Image\4b70d3af99abf0d653ef\egfb.jpg,.gradio\flagged\Image with Predictions\19fe55b15859fae38b71\image.webp,2025-04-01 08:34:58.485687

app.py

@@ -1,11 +1,12 @@
 import gradio as gr
 import torch
-from PIL import Image
+from PIL import Image, ImageDraw, ImageFont  # Import ImageFont for better labels
 import io
 from ultralytics import YOLO
 
 # --- Load YOLO Model ---
 MODEL_PATH = 'model/char.pt'
+
 try:
     model = YOLO(MODEL_PATH)
     print(f"Model loaded successfully from: {MODEL_PATH}")
@@ -15,30 +16,29 @@ except Exception as e:
 
 # --- Prediction Function for Gradio ---
 def predict(image):
-    if model is None or image is None:
-        return None
+    if model is None:
+        return "Model is not loaded properly."
 
     try:
-        img = Image.fromarray(image).convert('RGB')
-        results = model(img)
+        img = Image.fromarray(image).convert('RGB')  # Convert to PIL Image
+        results = model(img)  # Perform inference
+
+        draw = ImageDraw.Draw(img)
+        font = ImageFont.load_default()  # Load a default font for text
 
-        predictions = []
         for result in results:
-            for box in result.boxes:
-                x1, y1, x2, y2 = map(int, box.xyxy[0])
-                label = model.model.names[int(box.cls)]
-                confidence = float(box.conf[0])
-                predictions.append({'label': label, 'confidence': confidence, 'bbox': (x1, y1, x2, y2)})
+            if hasattr(result, 'boxes') and result.boxes is not None:
+                for box in result.boxes:
+                    x1, y1, x2, y2 = map(int, box.xyxy[0])  # Bounding box coordinates
+                    label = model.model.names[int(box.cls)]  # Get class label
+                    confidence = float(box.conf[0])  # Get confidence score
 
-        # Draw bounding boxes on the image
-        draw = ImageDraw.Draw(img)
-        for pred in predictions:
-            x1, y1, x2, y2 = pred['bbox']
-            label = f"{pred['label']} ({pred['confidence']:.2f})"
-            draw.rectangle([x1, y1, x2, y2], outline="green", width=2)
-            draw.text((x1, y1 - 10), label, fill="red")
+                    # Draw bounding box and text
+                    draw.rectangle([x1, y1, x2, y2], outline="green", width=3)
+                    text = f"{label} ({confidence:.2f})"
+                    draw.text((x1, y1 - 10), text, fill="red", font=font)
 
-        return img
+        return img  # Return the image with drawn boxes
 
     except Exception as e:
         return f"Error during prediction: {e}"
@@ -52,4 +52,4 @@ iface = gr.Interface(
     description="Upload an image to see object detection predictions using a YOLO model.",
 )
 
-iface.launch()
+iface.launch()