app.py

import gradio as gr
import json
import torch
from torch import nn
from torchvision import models, transforms
from PIL import Image
import os

# 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 transformation for image processing
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]),
])

# Function to load and preprocess image
def load_image_from_path(image_path):
    if not os.path.exists(image_path):
        raise FileNotFoundError(f"Image file not found at {image_path}")
    image = Image.open(image_path)
    image = transform(image).unsqueeze(0)  # Convert to tensor and add batch dimension
    return image

# Load the model (Example: ResNet50)
def load_model():
    model = models.resnet50(pretrained=True)
    model.fc = nn.Linear(model.fc.in_features, num_classes)
    model.load_state_dict(torch.load("model.pth"))
    model.eval()
    return model

# Predict from image tensor
def predict(image_tensor):
    with torch.no_grad():
        outputs = model(image_tensor)
        predicted_class = torch.argmax(outputs, dim=1).item()
    return predicted_class

# Initialize model
model = load_model()

# Define the Gradio interface function
def predict_from_file(file_path):
    try:
        # Load image from path
        image_tensor = load_image_from_path(file_path)
        # Get prediction
        predicted_class = predict(image_tensor)
        result = {"result": "Fall armyworm" if predicted_class == 0 else "Healthy maize"}
        return result
    except Exception as e:
        return {"error": str(e)}

# Gradio Interface
iface = gr.Interface(
    fn=predict_from_file,
    inputs=gr.Textbox(label="Image Path (Local)"),
    outputs=gr.JSON(),
    live=True,
    title="Maize Anomaly Detection",
    description="Send a local file path via POST request to trigger prediction.",
)

# Launch the Gradio app
iface.launch(share=True, server_name="0.0.0.0", server_port=7860)