app.py

import gradio as gr
import torch
import torchvision.transforms as transforms
from torchvision.models import resnet50
from PIL import Image
from huggingface_hub import hf_hub_download

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = resnet50(pretrained=False)
model.fc = torch.nn.Linear(model.fc.in_features, 14)  # Adjust for 14 classes

model_path = hf_hub_download(repo_id="iamomtiwari/resnet50-crop-disease", filename="resnet50_model_hf.pt")
model.load_state_dict(torch.load(model_path, map_location=device))
model.to(device)
model.eval()

# Define image transformations
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

# Class labels
class_labels = [
    "Corn___Common_Rust", "Corn___Gray_Leaf_Spot", "Corn___Healthy", "Corn___Northern_Leaf_Blight",
    "Rice___Brown_Spot", "Rice___Healthy", "Rice___Leaf_Blast", "Rice___Neck_Blast",
    "Wheat___Brown_Rust", "Wheat___Healthy", "Wheat___Yellow_Rust",
    "Sugarcane__Red_Rot", "Sugarcane__Healthy", "Sugarcane__Bacterial Blight"
]

# Prediction function
def predict(image):
    try:
        image = transform(image).unsqueeze(0).to(device)
        with torch.no_grad():
            outputs = model(image)
            _, predicted_class = torch.max(outputs, 1)
            return class_labels[predicted_class.item()]
    except Exception as e:
        return f"Error: {str(e)}"

# Gradio interface
interface = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil"),
    outputs=gr.Label(num_top_classes=3),
    title="Crop Disease Classification",
    description="Upload an image to classify crop diseases using ResNet-50."
)

interface.launch()