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import streamlit as st
from firebase_admin import firestore
import pdd1
def app():
if 'db' not in st.session_state:
st.session_state.db = ''
db=firestore.client()
st.session_state.db=db
# st.title(' :violet[pdd] :sunglasses:')
ph = ''
if st.session_state.username=='':
st.subheader('Login to use Model')
else:
pdd1.app()
# import streamlit as st
#def app():
#st.markdown('<a href="https://prj2106-plantd.hf.space/">Go to Another Link</a>', unsafe_allow_html=True)
# 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 app():
# 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)
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