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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
# Define the number of classes
num_classes = 2 # Update with the actual number of classes in your dataset (e.g., 2 for healthy and anomalous)
# Download model from Hugging Face
def download_model():
model_path = hf_hub_download(repo_id="jays009/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) # Set pretrained=False because you're loading custom weights
model.fc = nn.Linear(model.fc.in_features, num_classes) # Adjust for the number of classes in your dataset
model.load_state_dict(torch.load(model_path, map_location=torch.device("cpu"))) # Load model on CPU for compatibility
model.eval() # Set to evaluation mode
return model
# Download the model and load it
model_path = download_model() # Downloads the model from Hugging Face Hub
model = load_model(model_path)
# Define the transformation for the input image
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]), # Normalize the image (ImageNet mean and std)
])
# Define the prediction function
def predict(image):
# Apply the necessary transformations to the image
image = transform(image).unsqueeze(0) # Add batch dimension
image = image.to(torch.device("cuda" if torch.cuda.is_available() else "cpu")) # Move to GPU if available
with torch.no_grad():
outputs = model(image) # Perform forward pass
predicted_class = torch.argmax(outputs, dim=1).item() # Get the predicted class
# Create a response based on the predicted class
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."
# Create the Gradio interface
iface = gr.Interface(
fn=predict, # Function for prediction
inputs=gr.Image(type="pil"), # Image input
outputs=gr.Textbox(), # Output: Predicted class
live=True, # Updates as the user uploads an image
title="Wheat Anomaly Detection",
description="Upload an image of wheat to detect anomalies like disease or pest infestation."
)
# Launch the Gradio interface
iface.launch()
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