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import gradio as gr
import torch
from PIL import Image
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}")
except Exception as e:
print(f"Error loading model: {e}")
model = None
# --- Prediction Function for Gradio ---
def predict(image):
if model is None or image is None:
return None
try:
img = Image.fromarray(image).convert('RGB')
results = model(img)
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)})
# 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")
return img
except Exception as e:
return f"Error during prediction: {e}"
# --- Gradio Interface ---
iface = gr.Interface(
fn=predict,
inputs=gr.Image(label="Upload an Image"),
outputs=gr.Image(label="Image with Predictions"),
title="YOLO Object Detection",
description="Upload an image to see object detection predictions using a YOLO model.",
)
iface.launch() |