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| import gradio as gr | |
| from transformers import AutoImageProcessor, AutoModelForImageClassification | |
| from PIL import Image | |
| import torch | |
| import numpy as np | |
| import logging | |
| import requests | |
| import os | |
| # Configure Logging | |
| logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") | |
| # Load Model & Processor | |
| model_name = "linkanjarad/mobilenet_v2_1.0_224-plant-disease-identification" | |
| try: | |
| processor = AutoImageProcessor.from_pretrained(model_name, use_fast=True) | |
| model = AutoModelForImageClassification.from_pretrained(model_name) | |
| logging.info("✅ Model and processor loaded successfully.") | |
| except Exception as e: | |
| logging.error(f"❌ Failed to load model: {str(e)}") | |
| raise RuntimeError("Failed to load the model. Please check the logs for details.") | |
| # Gemini API Key (Replace with your actual key) | |
| GEMINI_API_KEY = os.getenv("AIzaSyCiRL0ES-zsJGJYsY03xmpwqcggDGcL2Fk", "AIzaSyCiRL0ES-zsJGJYsY03xmpwqcggDGcL2Fk") | |
| # Function to Get AI-Powered Treatment Suggestions | |
| def get_treatment_suggestions(disease_name): | |
| url = f"https://generativelanguage.googleapis.com/v1beta/models/gemini-pro:generateText?key={GEMINI_API_KEY}" | |
| headers = {"Content-Type": "application/json"} | |
| data = { | |
| "prompt": { "text": f"Provide detailed organic and chemical treatment options, including dosage and preventive care, for {disease_name} in crops." }, | |
| "temperature": 0.7, | |
| "candidate_count": 1 | |
| } | |
| try: | |
| response = requests.post(url, headers=headers, json=data) | |
| if response.status_code == 200: | |
| result = response.json() | |
| treatment = result.get("candidates", [{}])[0].get("content", "No treatment suggestions found.") | |
| return treatment | |
| else: | |
| logging.error(f"API Error: {response.status_code} - {response.text}") | |
| return f"API Error: {response.status_code}" | |
| except Exception as e: | |
| logging.error(f"Error fetching treatment suggestions: {str(e)}") | |
| return "Error retrieving treatment details." | |
| # Define Prediction Function | |
| def predict(image): | |
| try: | |
| image = Image.fromarray(np.uint8(image)).convert("RGB") | |
| inputs = processor(images=image, return_tensors="pt") | |
| with torch.no_grad(): | |
| outputs = model(**inputs) | |
| logits = outputs.logits | |
| predicted_class_idx = logits.argmax(-1).item() | |
| predicted_label = model.config.id2label[predicted_class_idx] | |
| # Get AI-generated treatment suggestions | |
| treatment = get_treatment_suggestions(predicted_label) | |
| return f"Predicted Disease: {predicted_label}\nTreatment: {treatment}" | |
| except Exception as e: | |
| logging.error(f"Prediction failed: {str(e)}") | |
| return f"❌ Prediction failed: {str(e)}" | |
| # Gradio Interface | |
| iface = gr.Interface( | |
| fn=predict, | |
| inputs=gr.Image(type="numpy", label="Upload or capture plant image"), | |
| outputs=gr.Textbox(label="Result"), | |
| title="AI-Powered Plant Disease Detector", | |
| description="Upload a plant leaf image to detect diseases and get AI-powered treatment suggestions.", | |
| allow_flagging="never", | |
| ) | |
| # Launch Gradio App | |
| iface.launch() | |