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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 | |
| import base64 | |
| from io import BytesIO | |
| # Define the number of classes | |
| num_classes = 2 | |
| # Download model from Hugging Face | |
| def download_model(): | |
| try: | |
| model_path = hf_hub_download(repo_id="jays009/Restnet50", filename="pytorch_model.bin") | |
| return model_path | |
| except Exception as e: | |
| print(f"Error downloading model: {e}") | |
| return None | |
| # Load the model from Hugging Face | |
| def load_model(model_path): | |
| try: | |
| 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 | |
| except Exception as e: | |
| print(f"Error loading model: {e}") | |
| return None | |
| # Download the model and load it | |
| model_path = download_model() | |
| model = load_model(model_path) if model_path else None | |
| # 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]), | |
| ]) | |
| def predict(image): | |
| try: | |
| print(f"Received image input: {image}") | |
| # Check if the input is a PIL Image type (Gradio automatically provides a PIL image) | |
| if not isinstance(image, Image.Image): | |
| return json.dumps({"error": "Invalid image format received. Please provide a valid image."}) | |
| # Apply transformations to the image | |
| image = transform(image).unsqueeze(0) | |
| print(f"Transformed image tensor: {image.shape}") | |
| # Move the image to the correct device | |
| image = image.to(torch.device("cuda" if torch.cuda.is_available() else "cpu")) | |
| # Make predictions | |
| with torch.no_grad(): | |
| outputs = model(image) | |
| predicted_class = torch.argmax(outputs, dim=1).item() | |
| print(f"Prediction output: {outputs}, Predicted class: {predicted_class}") | |
| # Return the result based on the predicted class | |
| if predicted_class == 0: | |
| return json.dumps({"result": "The photo you've sent is of fall army worm with problem ID 126."}) | |
| elif predicted_class == 1: | |
| return json.dumps({"result": "The photo you've sent is of a healthy maize image."}) | |
| else: | |
| return json.dumps({"error": "Unexpected class prediction."}) | |
| except Exception as e: | |
| print(f"Error processing image: {e}") | |
| return json.dumps({"error": f"Error processing image: {e}"}) | |
| # Create the Gradio interface | |
| iface = gr.Interface( | |
| fn=predict, | |
| inputs=gr.Image(type="pil", label="Upload an image or provide a URL or local path"), | |
| outputs=gr.Textbox(label="Prediction Result"), | |
| live=True, | |
| title="Maize Anomaly Detection", | |
| description="Upload an image of maize to detect anomalies like disease or pest infestation. You can provide local paths, URLs, or base64-encoded images." | |
| ) | |
| # Launch the Gradio interface | |
| iface.launch(share=True, show_error=True) | |