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| import os | |
| import gradio as gr | |
| import openai | |
| import torch | |
| from torchvision import transforms | |
| from PIL import Image | |
| import requests | |
| from io import BytesIO | |
| # Set your OpenAI API key here or use environment variables | |
| openai.api_key = os.getenv("OPENAI_API_KEY") | |
| # Load a pre-trained model for leaf disease detection | |
| # For demonstration, we'll use a generic ResNet model fine-tuned for classification | |
| # Replace the model path with your specific model trained for leaf diseases | |
| model = torch.hub.load('pytorch/vision:v0.10.0', 'resnet50', pretrained=True) | |
| model.eval() | |
| # Define image transformations | |
| preprocess = transforms.Compose([ | |
| transforms.Resize(256), | |
| transforms.CenterCrop(224), | |
| transforms.ToTensor(), | |
| transforms.Normalize( | |
| mean=[0.485, 0.456, 0.406], # Standard ImageNet means | |
| std=[0.229, 0.224, 0.225] # Standard ImageNet stds | |
| ) | |
| ]) | |
| # Load class labels (You should replace this with your specific disease classes) | |
| # For demonstration, we'll use ImageNet labels | |
| LABELS_URL = "https://s3.amazonaws.com/outcome-blog/imagenet/labels.json" | |
| response = requests.get(LABELS_URL) | |
| labels = {int(key): value for key, value in response.json().items()} | |
| def detect_disease(image): | |
| # Preprocess the image | |
| img = preprocess(image).unsqueeze(0) # Add batch dimension | |
| # Perform inference | |
| with torch.no_grad(): | |
| outputs = model(img) | |
| _, predicted = torch.max(outputs, 1) | |
| class_id = predicted.item() | |
| disease = labels.get(class_id, "Unknown Disease") | |
| if disease == "Unknown Disease": | |
| return disease, "Sorry, the disease could not be identified. Please consult a local agricultural extension office." | |
| # Generate remedies using OpenAI's ChatGPT | |
| prompt = f"The following disease has been detected on a plant leaf: {disease}. Please provide detailed remedies and treatment options for this disease." | |
| try: | |
| completion = openai.ChatCompletion.create( | |
| model="gpt-4", | |
| messages=[ | |
| {"role": "system", "content": "You are a helpful agricultural expert."}, | |
| {"role": "user", "content": prompt} | |
| ], | |
| temperature=0.7, | |
| max_tokens=500 | |
| ) | |
| remedies = completion.choices[0].message.content.strip() | |
| except Exception as e: | |
| remedies = "An error occurred while fetching remedies. Please try again later." | |
| return disease, remedies | |
| # Define the Gradio interface | |
| iface = gr.Interface( | |
| fn=detect_disease, | |
| inputs=gr.inputs.Image(type="pil", label="Upload Leaf Image"), | |
| outputs=[ | |
| gr.outputs.Textbox(label="Detected Disease"), | |
| gr.outputs.Textbox(label="Remedies") | |
| ], | |
| title="Leaf Disease Detector", | |
| description="Upload an image of a leaf, and the system will detect the disease and provide remedies." | |
| ) | |
| if __name__ == "__main__": | |
| iface.launch() | |