app.py

import gradio as gr
import torch
from torch import nn
from torchvision import models, transforms
from huggingface_hub import hf_hub_download
from PIL import Image
import os
import logging
import requests
from io import BytesIO

# Setup logging
logging.basicConfig(level=logging.INFO)

# Define the number of classes
num_classes = 2

# Download model from Hugging Face
def download_model():
    model_path = hf_hub_download(repo_id="jays009/Restnet50", filename="pytorch_model.bin")
    return model_path

# Load the model from Hugging Face
def load_model(model_path):
    model = models.resnet50(pretrained=False)
    model.fc = nn.Linear(model.fc.in_features, num_classes)
    model.load_state_dict(torch.load(model_path, map_location=torch.device("cpu")))
    model.eval()
    return model

# Download the model and load it
model_path = download_model()
model = load_model(model_path)

# Define the transformation for the input image
transform = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])

# Prediction function for an uploaded image
def predict_from_image(image_url):
    try:
        # Download the image from the provided URL
        response = requests.get(image_url)
        response.raise_for_status()  # Check if the request was successful
        image = Image.open(BytesIO(response.content))

        # Apply transformations
        image_tensor = transform(image).unsqueeze(0)  # Add batch dimension

        # Perform prediction
        with torch.no_grad():
            outputs = model(image_tensor)
            predicted_class = torch.argmax(outputs, dim=1).item()

        # Interpret the result
        if predicted_class == 0:
            return {"result": "The photo is of fall army worm with problem ID 126."}
        elif predicted_class == 1:
            return {"result": "The photo is of a healthy maize image."}
        else:
            return {"error": "Unexpected class prediction."}

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


demo = gr.Interface(
    fn=predict_from_image,
    inputs="text",
    outputs="json",
    title="Image Processing",
    description="Enter a URL to an image",
)

if __name__ == "__main__":
    demo.launch()