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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
num_classes = 2
def download_model():
model_path = hf_hub_download(repo_id="jays009/Restnet50", filename="pytorch_model.bin")
return model_path
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
model_path = download_model()
model = load_model(model_path)
transform = transforms.Compose([
transforms.Resize(256), # Resize the image to 256x256
transforms.CenterCrop(224), # Crop the image to 224x224
transforms.ToTensor(), # Convert the image to a Tensor
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])
def predict(image):
image = transform(image).unsqueeze(0)
image = image.to(torch.device("cuda" if torch.cuda.is_available() else "cpu"))
with torch.no_grad():
outputs = model(image)
predicted_class = torch.argmax(outputs, dim=1).item()
if predicted_class == 0:
return "The photo you've sent is of fall army worm with problem ID 126."
elif predicted_class == 1:
return "The photo you've sent is of a healthy wheat image."
else:
return "Unexpected class prediction."
iface = gr.Interface(
fn=predict,
inputs=gr.Image(type="pil"),
outputs=gr.Textbox(),
live=True,
title="Maize Anomaly Detection",
description="Upload an image of maize to detect anomalies like disease or pest infestation."
)
iface.launch()
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