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| from keras.models import load_model | |
| from fastapi import FastAPI | |
| import gradio as gr | |
| import requests | |
| import numpy as np | |
| from PIL import Image | |
| from tensorflow import keras | |
| from io import BytesIO | |
| import uvicorn | |
| from pyModel import Item | |
| app = FastAPI() | |
| def newPost(str): | |
| input_image = url_to_img(str) | |
| processed_image = process(input_image) | |
| crop = crop_predict(processed_image) | |
| disease = disease_predict(processed_image, crop) | |
| return [crop,disease] | |
| def url_to_img(image_url): | |
| # Load the image from the URL | |
| response = requests.get(image_url) | |
| input_image = Image.open(BytesIO(response.content)) | |
| return input_image | |
| def process(input_image): | |
| # Preprocess the image to match model's input dimensions | |
| input_image = input_image.resize((224, 224)) # Adjust the dimensions | |
| input_image = np.array(input_image) / 255.0 # Normalize pixel values to [0, 1] | |
| processed_image = np.expand_dims(input_image, axis=0) # Add batch dimension | |
| return processed_image | |
| def crop_predict(processed_image): | |
| # Load the trained model | |
| model = keras.models.load_model('./crop_model.h5') | |
| # Make predictions | |
| predictions = model.predict(processed_image) | |
| # Process the predictions (e.g., get class labels) | |
| class_labels = ["Corn", "Potato", "Rice", "Wheat"] | |
| predicted_class = np.argmax(predictions, axis=1) | |
| predicted_label = class_labels[predicted_class[0]] | |
| return predicted_label | |
| def disease_predict(processed_image, crop): | |
| if crop == "Potato": | |
| # Load the trained model | |
| model = keras.models.load_model('./potato_model.h5') | |
| # Process the predictions (e.g., get class labels) | |
| class_labels = ["Potato_Early_Blight", "Potato_Healthy", "Potato_Late_Blight"] | |
| elif crop == "Corn": | |
| # Load the trained model | |
| model = keras.models.load_model('./corn_model.h5') | |
| # Process the predictions (e.g., get class labels) | |
| class_labels = ["Corn_Common_Rust", "Corn_Gray_Leaf_Spot", "Corn_Healthy", "Corn_Northern_Leaf_Blight"] | |
| elif crop == "Rice": | |
| # Load the trained model | |
| model = keras.models.load_model('./rice_model.h5') | |
| # Process the predictions (e.g., get class labels) | |
| class_labels = ["Rice_Brown_Spot", "Rice_Healthy", "Rice_Leaf_Blast", "Rice_Neck_Blast"] | |
| elif crop == "Wheat": | |
| # Load the trained model | |
| model = keras.models.load_model('./wheat_model.h5') | |
| # Process the predictions (e.g., get class labels) | |
| class_labels = ["Wheat_Brown_Rust", "Wheat_Healthy", "Wheat_Yellow_Rust"] | |
| # Make predictions | |
| predictions = model.predict(processed_image) | |
| predicted_class = np.argmax(predictions, axis=1) | |
| predicted_label = class_labels[predicted_class[0]] | |
| return predicted_label | |
| # iface = gr.Interface(fn = newPost , inputs=['text'] , outputs=['text']) | |
| # iface.launch(share=True) |