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	from config import crop_model, crop_pipeline_encoder, crop_label_encoder
	from config import fertilizer_model, fertilizer_pipeline_encoder, fertilizer_label_encoder
	from config import plant_diseases_classifier_model
	from utils import retrieve_image_by_name_from_mongodb, retrieve_data
	from flask import Flask, request, render_template, jsonify
	import requests
	import os
	import numpy as np
	import base64

	app = Flask(__name__)

	app.config['UPLOAD_FOLDER'] = 'static/uploaded_image'

	@app.route("/")
	@app.route("/home")
	def home():
	return render_template('index.html')

	@app.route('/crop_recommendation', methods=['GET', 'POST'])
	def crop_recommendation():
	return render_template('crop_recommendation_input.html')

	@app.route("/crop_recommendation_output", methods=['GET', 'POST'])
	def crop_recommendation_output():
	temperature = request.form.get("temperature")
	humidity = request.form.get("humidity")
	ph = request.form.get("ph")
	nitrogen = request.form.get("nitrogen")
	potassium = request.form.get("potassium")
	phosphorous = request.form.get("phosphorous")
	rain_fall = request.form.get("rain_fall")

	input_list = [nitrogen, phosphorous, potassium, temperature, humidity, ph, rain_fall]
	input_array = np.array(input_list).reshape(-1, 7).astype(int)

	transformed_data = crop_pipeline_encoder.transform(input_array)
	model_prediction = crop_model.predict(transformed_data).astype(int)

	label = crop_label_encoder.inverse_transform(model_prediction)
	print(label)

	# retrieving the image from mongodb dabase
	image_data = retrieve_image_by_name_from_mongodb(database_name=os.getenv("CROP_DB_NAME"),
	collection_name=os.getenv("CROP_IMAGE_COLLECTION_NAME"),
	file_name=str(label[0]))

	# encoding the byte data recieved from the mongodb
	image_data_base64 = base64.b64encode(image_data).decode('utf-8')

	# retrieving text data from mongodb
	crop_details = retrieve_data(database_name=os.getenv("CROP_DB_NAME"), collection_name= os.getenv("CROP_INFO_COLLECTION_NAME"), search_query=label[0])

	return render_template('crop_recommendation_output.html', image_data_base64=image_data_base64, input_file_name=label[0], crop_details=crop_details)


	@app.route('/fertilizer_recommendation', methods=['GET', 'POST'])
	def fertilizer_recommendation():
	return render_template('fertilizer_recommendation_input.html')

	@app.route('/fertilizer_recommendation_output', methods=['GET', 'POST'])
	def fertilizer_recommendation_output():
	temperature = request.form.get("temperature")
	humidity = request.form.get("humidity")
	moisture = request.form.get("moisture")
	nitrogen = request.form.get("nitrogen")
	potassium = request.form.get("potassium")
	phosphorous = request.form.get("phosphorous")
	soil_type = request.form.get("soil_type")
	crop_type = request.form.get("crop_type")

	input_data = [int(temperature), int(humidity), int(moisture), soil_type, crop_type, int(nitrogen), int(potassium), int(phosphorous)]
	input_array = np.array(input_data).reshape(-1, 8)

	transformed_data = fertilizer_pipeline_encoder.transform(input_array)
	model_prediction = fertilizer_model.predict(transformed_data).astype(int)

	label = fertilizer_label_encoder.inverse_transform(model_prediction)

	# retrieving the image from mongodb dabase
	image_data = retrieve_image_by_name_from_mongodb(database_name=os.getenv("FERTILIZER_DB_NAME"),
	collection_name=os.getenv("FERTILIZER_IMAGE_COLLECTION_NAME"),
	file_name=str(label[0]))

	# encoding the byte data recieved from the mongodb
	image_data_base64 = base64.b64encode(image_data).decode('utf-8')

	# retrieving text data from mongodb
	fertilizer_details = retrieve_data(database_name=os.getenv("FERTILIZER_DB_NAME"), collection_name= os.getenv("FERTILIZER_INFO_COLLECTION_NAME"), search_query=label[0])


	return render_template('fertilizer_recommendation_ouput.html', image_data_base64=image_data_base64, label= label[0], fertilizer_details=fertilizer_details)


	@app.route('/image_classification', methods=['GET', 'POST'])
	def image_classification():
	return render_template('image_classification_input.html')

	@app.route('/image_classification_output', methods=['GET', 'POST'])
	def image_classification_output():
	file = request.files['image_file']
	new_filename = "plant_image.JPG"
	file.save(os.path.join(app.config['UPLOAD_FOLDER'], new_filename))
	file_path = os.path.join(app.config['UPLOAD_FOLDER'], new_filename)

	# infercing the with the uploaded image
	results = plant_diseases_classifier_model(file_path)

	#fetching all the labels
	names_dict = results[0].names

	# fetching the probalility of each class
	probs = results[0].probs.data.tolist()

	# selecting class with maximum probability
	model_prediction= names_dict[np.argmax(probs)]

	diseases_details = retrieve_data(database_name=os.getenv("DISEASE_DB_NAME"),
	collection_name=os.getenv("DISEASE_INFO_COLLECTION_NAME"),
	search_query=model_prediction)

	return render_template("image_classification_output.html", model_prediction=model_prediction, diseases_details=diseases_details)


	@app.route('/market_price')
	def market_price():
	return render_template("market_price_input.html")

	@app.route('/market_price_output', methods=['POST'])
	def market_price_output():
	# input field name is 'selected_state'
	user_input = request.form.get('selected_state')
	api_key = os.getenv("COMMODITY_PRICE_API_KEY")

	# Make a request to the API with the user input
	api_url = f'https://api.data.gov.in/resource/9ef84268-d588-465a-a308-a864a43d0070?api-key={api_key}&format=json&filters%5Bstate%5D={user_input}'
	response = requests.get(api_url)

	if response.status_code == 200:
	data = response.json()
	data = data['records']
	# return render_template('market_price_output.html', data=data)
	if len(data) > 0:
	# Return the JSON data as a response
	return render_template('market_price_output.html', data=data)
	else:
	return render_template("market_price_no_data.html")
	else:
	return jsonify({'error': 'Unable to fetch data from the API'}), 400

	if __name__ == "__main__":
	app.run(debug=True, host="0.0.0.0", port=8000)