app.py

# app.py

# Import necessary libraries
import torch
from PIL import Image
from torchvision import transforms
import gradio as gr
import os

# Download the labels file if not present
os.system("wget https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt")

# Load ResNet model
model = torch.hub.load('pytorch/vision:v0.10.0', 'resnet18', pretrained=True).eval()

# Function for model inference
def inference(input_image):
    try:
        # Preprocess the input image
        preprocess = transforms.Compose([
            transforms.Resize(256),
            transforms.CenterCrop(224),
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
        ])

        input_tensor = preprocess(input_image)
        input_batch = input_tensor.unsqueeze(0)  # Create a mini-batch as expected by the model

        # Move the input and model to GPU for speed if available
        if torch.cuda.is_available():
            input_batch = input_batch.to('cuda')
            model.to('cuda')

        with torch.no_grad():
            output = model(input_batch)

        # The output has unnormalized scores. To get probabilities, run a softmax on it.
        probabilities = torch.nn.functional.softmax(output[0], dim=0)

        # Read the categories from the labels file
        with open("imagenet_classes.txt", "r") as f:
            categories = [s.strip() for s in f.readlines()]

        # Show top categories per image
        top5_prob, top5_catid = torch.topk(probabilities, 5)
        result = {categories[top5_catid[i]]: top5_prob[i].item() for i in range(top5_prob.size(0))}

        return result
    except Exception as e:
        return {"error": str(e)}

# Gradio Interface setup
inputs = gr.Image(type='pil', label="Upload Image")
outputs = gr.Label(num_top_classes=5, label="Predictions")  # Removed 'type' parameter

# Launch Gradio Interface
gr.Interface(inference, inputs, outputs, title="DeepLensExplorer", description="Classify images using ResNet", analytics_enabled=False).launch()