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| import gradio as gr | |
| import torch | |
| from torch import nn | |
| from torchvision import models, transforms | |
| from PIL import Image | |
| import requests | |
| import base64 | |
| from io import BytesIO | |
| import os | |
| # Define the number of classes | |
| num_classes = 2 # Update with the actual number of classes in your dataset | |
| # Load the model (assuming you've already downloaded it) | |
| def load_model(): | |
| try: | |
| model = models.resnet50(pretrained=False) | |
| model.fc = nn.Linear(model.fc.in_features, num_classes) | |
| model.load_state_dict(torch.load("path_to_your_model.pth", map_location=torch.device("cpu"))) | |
| model.eval() | |
| return model | |
| except Exception as e: | |
| print(f"Error loading model: {e}") | |
| return None | |
| model = load_model() | |
| # 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 | |
| def process_image(data): | |
| try: | |
| # Check if the input contains a base64-encoded string | |
| if isinstance(data, dict): | |
| if "data" in data: | |
| # Base64 decoding | |
| image_data = base64.b64decode(data["data"]) | |
| image = Image.open(BytesIO(image_data)) | |
| elif "url" in data: | |
| # URL-based image loading | |
| response = requests.get(data["url"]) | |
| image = Image.open(BytesIO(response.content)) | |
| elif "path" in data: | |
| # Local path image loading | |
| image = Image.open(data["path"]) | |
| else: | |
| return "Invalid input data structure." | |
| # Validate image | |
| if not isinstance(image, Image.Image): | |
| return "Invalid image format received." | |
| # Apply transformations | |
| image = transform(image).unsqueeze(0) | |
| # Prediction | |
| 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 maize image." | |
| else: | |
| return "Unexpected class prediction." | |
| except Exception as e: | |
| return f"Error processing image: {e}" | |
| # Create the Gradio interface | |
| iface = gr.Interface( | |
| fn=process_image, | |
| inputs=gr.JSON(label="Upload an image (URL or Local Path)"), # Input: JSON to handle URL or path | |
| outputs=gr.Textbox(label="Prediction Result"), # Output: 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) | |