Update app.py

app.py

@@ -1,163 +1,3 @@
-"""import gradio as gr
-import torch
-from PIL import Image
-from pathlib import Path
-import numpy as np
-
-# Load YOLO Model
-model = torch.hub.load('ultralytics/yolov5', 'custom', path='https://huggingface.co/ayoubsa/yolo_model/blob/main/best.pt')  # Replace 'model.pt' with your uploaded model's path
-
-# Function to make predictions
-def predict(image):
-    # Convert input to PIL image if it's not
-    if isinstance(image, np.ndarray):
-        image = Image.fromarray(image)
-    
-    # Run inference
-    results = model(image)
-    
-    # Extract predictions (bounding boxes, labels, confidence scores)
-    predictions = results.pandas().xyxy[0]  # Pandas dataframe
-    annotated_image = np.array(results.render()[0])  # Annotated image as NumPy array
-    
-    return annotated_image, predictions[['name', 'confidence']].to_dict(orient="records")
-
-# Create Gradio Interface
-image_input = gr.inputs.Image(type="numpy", label="Input Image")
-output_image = gr.outputs.Image(type="numpy", label="Annotated Image")
-output_text = gr.outputs.JSON(label="Predictions (Labels & Confidence)")
-
-interface = gr.Interface(
-    fn=predict,
-    inputs=image_input,
-    outputs=[output_image, output_text],
-    title="YOLO Object Detection",
-    description="Upload an image to detect objects using YOLO.",
-    examples=["example1.jpg", "example2.jpg", "example3.jpg"]  # Provide paths to example images
-)
-
-interface.launch()
-
-
-from datasets import load_dataset
-import gradio as gr
-import random
-import numpy as np
-from PIL import Image
-import torch
-
-# Load the YOLO model
-model = torch.hub.load('ultralytics/yolov5', 'custom', path='https://huggingface.co/ayoubsa/yolo_model/blob/main/best.pt')  # Replace with your uploaded model's path
-
-# Load your dataset from Hugging Face
-dataset = load_dataset("https://huggingface.co/datasets/ayoubsa/Sign_Road_Detection_Dataset/tree/main")  # Replace with your dataset's repository name on Hugging Face
-
-# Function to get random examples from the dataset
-def get_random_examples(dataset, num_examples=3):
-    images = dataset['test'][:]['image']  # Assuming the images are in the 'train' split and column 'image'
-    random_examples = random.sample(images, num_examples)
-    return random_examples
-
-# Define the prediction function
-def predict(image):
-    results = model(image)  # Perform object detection using YOLO
-    results.render()  # Render bounding boxes on the image
-    output_image = Image.fromarray(results.imgs[0])  # Convert to PIL image
-    return output_image
-
-# Get examples for Gradio app
-example_images = get_random_examples(dataset, num_examples=3)
-
-# Create the Gradio interface
-iface = gr.Interface(
-    fn=predict,
-    inputs=gr.inputs.Image(type="pil"),  # PIL Image for compatibility with YOLO
-    outputs=gr.outputs.Image(type="pil"),
-    examples=example_images  # Linking examples directly from Hugging Face dataset
-)
-
-# Launch the Gradio app
-iface.launch()
-
-import gradio as gr
-import torch
-from PIL import Image
-import zipfile
-import os
-import random
-
-# Define the paths for the model and dataset
-MODEL_PATH = 'https://huggingface.co/ayoubsa/yolo_model/resolve/main/best.pt'  # YOLO model file
-DATASET_PATH = 'test.zip'  # The name of the uploaded test dataset zip file
-
-# Load the YOLO model
-model = torch.hub.load('ultralytics/yolov5', 'custom', path=MODEL_PATH)
-
-# Unzip the dataset
-if not os.path.exists("unzipped_test"):
-    with zipfile.ZipFile(DATASET_PATH, 'r') as zip_ref:
-        zip_ref.extractall("unzipped_test")  # Extract images to this folder
-
-# Get all image paths from the unzipped folder
-image_folder = "unzipped_test"
-all_images = [os.path.join(image_folder, f) for f in os.listdir(image_folder) if f.endswith(('.jpg', '.png', '.jpeg'))]
-
-# Function to get random examples
-def get_random_examples(num_examples=3):
-    if len(all_images) >= num_examples:
-        return random.sample(all_images, num_examples)
-    else:
-        return all_images  # Return whatever is available if less than required
-
-# Define the prediction function
-def predict(image):
-    results = model(image)  # Perform object detection using YOLO
-    results.render()  # Render bounding boxes on the image
-    output_image = Image.fromarray(results.imgs[0])  # Convert to PIL image
-    return output_image
-
-# Get example images
-example_images = get_random_examples(num_examples=3)
-
-# Create the Gradio interface
-iface = gr.Interface(
-    fn=predict,
-    inputs=gr.inputs.Image(type="pil", label="Upload an Image"),
-    outputs=gr.outputs.Image(type="pil", label="Predicted Image with Bounding Boxes"),
-    examples=example_images  # Link the example images
-)
-
-# Launch the Gradio app
-iface.launch()
-
-import gradio as gr
-import torch
-from PIL import Image
-import numpy as np
-from ultralytics import YOLO
-
-# Load the YOLO model
-MODEL_URL= 'https://huggingface.co/ayoubsa/yolo_model/resolve/main/best.pt'
-model = YOLO(MODEL_URL)
-
-# Define the prediction function
-def predict(image):
-    results = model(image)  # Perform object detection using YOLO
-    results.render()  # Render bounding boxes on the image
-    output_image = Image.fromarray(results.imgs[0])  # Convert to PIL image
-    return output_image
-
-# Create the Gradio interface
-iface = gr.Interface(
-    fn=predict,
-    inputs=gr.Image(type="pil", label="Upload an Image"),  # Upload input as PIL Image
-    outputs=gr.Image(type="pil", label="Predicted Image with Bounding Boxes"),  # Output image
-    title="Object Detection App",
-    description="Upload an image, and the YOLO model will detect objects in it."
-)
-
-# Launch the Gradio app
-iface.launch()"""
 from PIL import Image
 import numpy as np
 from ultralytics import YOLO