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| import os | |
| from io import BytesIO | |
| import gradio as gr | |
| import google.generativeai as genai | |
| from PIL import Image | |
| from dotenv import load_dotenv | |
| # Load environment variables | |
| load_dotenv() | |
| genai.configure(api_key=os.getenv("GOOGLE_API_KEY")) | |
| input_prompt=""" | |
| "You are an expert in computer vision and agriculture who can easily predict the disease of the plant. " | |
| "Analyze the following image and provide 7 short outputs in a structured format: " | |
| "1. Crop : , " | |
| "2. Infected or Healthy : , " | |
| "3. Type of disease (if any), " | |
| "4. How confident out of 100% whether image is healthy or infected " | |
| "5. Reason for the disease such as whether it is happening due to fungus, bacteria, insect bite, poor nutrition, etc., " | |
| "6. Plant Growth Stage. " | |
| "7. Pest Life Stage." | |
| """ | |
| # Function to get a response from the Google Gemini Vision API | |
| def get_gemini_response(image): | |
| model = genai.GenerativeModel('gemini-1.5-pro') | |
| # Convert PIL image to bytes | |
| bytes_io = BytesIO() | |
| image.save(bytes_io, format='PNG') | |
| bytes_data = bytes_io.getvalue() | |
| response=model.generate_content([input_prompt,image]) | |
| # Placeholder logic for now: replace with actual API usage | |
| # You would pass the image bytes data to the API | |
| # Currently this is a mock response | |
| crop_name = "Wheat" # Example: Replace this with the actual API result | |
| disease_type = "Wheat Septoria" # Example: Replace this with the actual API result | |
| # Return disease info | |
| #return get_disease_info(crop_name, disease_type) | |
| return response.text | |
| # Function to handle the uploaded image and predict crop health | |
| def predict_crop_health(uploaded_image): | |
| # Pass the image to the Gemini API to get prediction | |
| return get_gemini_response(uploaded_image) | |
| # Define the Gradio interface: Inputs and Outputs | |
| inputs = gr.Image(type="pil", label="Upload Crop Image") | |
| outputs = gr.Markdown(label="Prediction Results") | |
| # Launch the Gradio interface | |
| gr.Interface( | |
| fn=predict_crop_health, | |
| inputs=inputs, | |
| outputs=gr.Textbox(label="Crop Disease Predictor"), | |
| title="Crop Disease Prediction App", | |
| description="Upload an image of a crop to predict its disease and get treatment suggestions.", | |
| live=False | |
| ).launch() | |