| import streamlit as st | |
| import tensorflow as tf | |
| from PIL import Image | |
| import numpy as np | |
| import os | |
| import io | |
| import google.generativeai as genai | |
| def import_and_predict(image_data, model, class_labels): | |
| size = (256, 256) | |
| if image_data is not None: | |
| image = Image.open(io.BytesIO(image_data.read())) | |
| image = image.resize(size) | |
| image = np.array(image) | |
| img_reshape = image / 255.0 | |
| img_reshape = np.expand_dims(img_reshape, axis=0) | |
| prediction = model.predict(img_reshape) | |
| st.image(image, width=300) | |
| predictions_label = class_labels[np.argmax(prediction[0])] | |
| return predictions_label | |
| else: | |
| st.warning("Please upload an image.") | |
| return None | |
| def get_info_from_gemini(prompt): | |
| genai.configure(api_key=os.environ.get('gemini_api')) | |
| model = genai.GenerativeModel('gemini-pro') | |
| response = model.generate_content(f"{prompt}") | |
| return response | |
| st.title("Plant Disease Detection") | |
| uploaded_image = st.file_uploader(f"Upload an image", type=["jpg", "jpeg", "png"]) | |
| models_path = ['./best_model_100_subset.h5',] | |
| CLASS_LABELS = ['Tomato Early blight', 'Tomato Leaf Mold', 'Tomato YellowLeaf Curl Virus', | |
| 'Tomato mosaic virus', 'Tomato healthy'] | |
| model = tf.keras.models.load_model(models_path[0]) | |
| prediction = import_and_predict(uploaded_image, model, CLASS_LABELS) | |
| st.write("disease name: ", prediction) | |
| if prediction != None: | |
| new_title = '<p style="font-size: 38px">Measures you can take to control: </p>' | |
| st.markdown(new_title, unsafe_allow_html=True) | |
| if prediction == CLASS_LABELS[4]: | |
| st.write("Plant is healthy take good care of it") | |
| response = get_info_from_gemini(f"cure for the disease {prediction} tell in bulletpoints and estimated cost in inr at last give summary of each measures estimated cost") | |
| st.write(response.text) |