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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
import os
import logging
import requests
# Setup logging
logging.basicConfig(level=logging.INFO)
# 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]),
])
# Prediction function for an uploaded image
def predict_from_image(image_url):
try:
# Download the image from the provided URL
response = requests.get(image_url)
image = Image.open(BytesIO(response.content))
# Process the image...
return {"result": "Image processed successfully"}
except Exception as e:
return {"error": str(e)}
demo = gr.Interface(
fn=predict_from_image,
inputs="text",
outputs="json",
title="Image Processing",
description="Enter a URL to an image",
)
if __name__ == "__main__":
demo.launch() |