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import gradio as gr
import json
import torch
from torch import nn
from torchvision import models, transforms
from huggingface_hub import hf_hub_download
from PIL import Image
import requests
import os
from io import BytesIO
# 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]),
])
# Function to predict from image content
def predict_from_image(image):
# Ensure the image is a PIL Image
if not isinstance(image, Image.Image):
raise ValueError("Invalid image format received. Please provide a valid image.")
# Apply transformations
image_tensor = transform(image).unsqueeze(0)
# Predict
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."}
# Function to predict from URL
def predict_from_url(url):
try:
response = requests.get(url)
response.raise_for_status() # Ensure the request was successful
image = Image.open(BytesIO(response.content))
return predict_from_image(image)
except Exception as e:
return {"error": f"Failed to process the URL: {str(e)}"}
# Gradio interface
iface = gr.Interface(
fn=lambda image, url: predict_from_image(image) if image else predict_from_url(url),
inputs=[
gr.Image(type="pil", label="Upload an Image"),
gr.Textbox(label="Or Enter an Image URL", placeholder="Provide a valid image URL"),
],
outputs=gr.JSON(label="Prediction Result"),
live=True,
title="Maize Anomaly Detection",
description="Upload an image or provide a URL to detect anomalies in maize crops.",
)
# Launch the interface
iface.launch(share=True, show_error=True)
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