<filename>python UI 2.0/server.py
from flask import Flask , render_template , request , redirect , url_for
app = Flask(__name__)

_code = ""
@app.route("/users/index", methods=["GET","POST"])
def main():
		return render_template('index.html')

@app.route("/users/graph", methods=["GET","POST"])
def login():
		return render_template('chart.html')

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

@app.route("/users/analyse/predict", methods=["GET","POST"])
def predict():
	import pandas as pd
	fertilizer_data = pd.read_excel("optimum2.xlsx", 'biofertilizer')
	X = fertilizer_data.drop("CLASS",axis=1)
	y = fertilizer_data.CLASS

	pred = pd.read_excel('optimum2.xlsx', 'Sheet3')
	pred = pred.drop(["pH" , "Temperature"] , axis=1)
	from sklearn.neighbors import KNeighborsClassifier
	clf = KNeighborsClassifier(n_neighbors=1)
	clf.fit(X,y)
	prediction1 = clf.predict(pred)
	print(prediction1)

	if(prediction1[0] == 1):
		return render_template("fertilizer.html" , fertilizer="Azotobacter	Bacillus_circulans	Pisolithus_sp")
	elif(prediction1[0] == 2):
		return render_template("fertilizer.html" , fertilizer="Azotobacter	Bacillus_circulans	Sclerocystis_sp")
	elif(prediction1[0] == 3):
		return render_template("fertilizer.html" , fertilizer="Azotobacter,	Bacillus_circulans,	Acaulospora_sp")
	elif(prediction1[0] == 4):
		return render_template("fertilizer.html" , fertilizer="Azotobacter, Pseudomonas_striata, Pisolithus_sp")
	elif(prediction1[0] == 5):
		return render_template("fertilizer.html" , fertilizer="Azotobacter,	Pseudomonas_striata, Sclerocystis_sp")
	elif(prediction1[0] == 6):
		return render_template("fertilizer.html" , fertilizer="Azotobacter,	Pseudomonas_striata, Acaulospora_sp")
	elif(prediction1[0] == 7):
		return render_template("fertilizer.html" , fertilizer="Azotobacter,	Penicillium_sp, Pisolithus_sp")
	elif(prediction1[0] == 8):
		return render_template("fertilizer.html" , fertilizer="Azotobacter,	Penicillium_sp,	Sclerocystis_sp")
	elif(prediction1[0] == 9):
		return render_template("fertilizer.html" , fertilizer="Azotobacter,	Penicillium_sp,	Acaulospora_sp")
	elif(prediction1[0] == 10):
		return render_template("fertilizer.html" , fertilizer="Frankia,	Bacillus_circulans,	Pisolithus_sp")
	elif(prediction1[0] == 11):
		return render_template("fertilizer.html" , fertilizer="Frankia,	Bacillus_circulans,	Sclerocystis_sp")
	elif(prediction1[0] == 12):
		return render_template("fertilizer.html" , fertilizer="Frankia,	Bacillus_circulans,	Acaulospora_sp")
	elif(prediction1[0] == 13):
		return render_template("fertilizer.html" , fertilizer="Frankia,	Pseudomonas_striata, Pisolithus_sp")
	elif(prediction1[0] == 14):
		return render_template("fertilizer.html" , fertilizer="Frankia,	Pseudomonas_striata, Sclerocystis_sp")
	elif(prediction1[0] == 15):
		return render_template("fertilizer.html" , fertilizer="Frankia,	Pseudomonas_striata, Acaulospora_sp")
	elif(prediction1[0] == 16):
		return render_template("fertilizer.html" , fertilizer="Frankia,	Penicillium_sp,	Pisolithus_sp")
	elif(prediction1[0] == 17):
		return render_template("fertilizer.html" , fertilizer="Frankia,	Penicillium_sp,	Sclerocystis_sp")
	elif(prediction1[0] == 18):
		return render_template("fertilizer.html" , fertilizer="Frankia,	Penicillium_sp,	Acaulospora_sp")
	elif(prediction1[0] == 19):
		return render_template("fertilizer.html" , fertilizer="Anabaena, Bacillus_circulans, Pisolithus_sp")
	elif(prediction1[0] == 20):
		return render_template("fertilizer.html" , fertilizer="Anabaena, Bacillus_circulans, Pisolithus_sp")
	elif(prediction1[0] == 21):
		return render_template("fertilizer.html" , fertilizer="Anabaena, Bacillus_circulans, Pisolithus_sp")
	elif(prediction1[0] == 22):
		return render_template("fertilizer.html" , fertilizer="Anabaena, Pseudomonas_striata, Pisolithus_sp")
	elif(prediction1[0] == 23):
		return render_template("fertilizer.html" , fertilizer="Anabaena, Pseudomonas_striata, Sclerocystis_sp")
	elif(prediction1[0] == 24):
		return render_template("fertilizer.html" , fertilizer="Anabaen,	Pseudomonas_striata, Acaulospora_sp")		
	elif(prediction1[0] == 25):
		return render_template("fertilizer.html" , fertilizer="Anabaena, Penicillium_sp, Pisolithus_sp")
	elif(prediction1[0] == 26):
		return render_template("fertilizer.html" , fertilizer="Anabaena, Penicillium_sp, Sclerocystis_sp")
	else:
		return render_template("fertilizer.html" , fertilizer="Anabaena, Penicillium_sp, Acaulospora_sp")

@app.route("/users/analyse", methods=["POST"])
def analyse():
	if(request.method == "POST"):
		import pandas as pd
		import numpy as np
		# import os
		optimum = pd.read_excel("optimum2.xlsx", 'newData')
		price = pd.read_excel("optimum2.xlsx", 'pricePerhr')
		optimum['N'] = optimum.N.astype(float)
		optimum['P'] = optimum.P.astype(float)
		optimum['K'] = optimum.K.astype(float)
		optimum['TEMPERATURE'] = optimum.TEMPERATURE.astype(float)
		X = optimum.drop("CLASS",axis=1)
		y = optimum.CLASS

		from sklearn.neighbors import KNeighborsClassifier
		clf = KNeighborsClassifier(n_neighbors=3)
		clf.fit(X,y)

		print(request.form.get('Potassium'))
		
		if(request.form.get('Potassium') == None):
			
			pred = pd.read_excel('optimum2.xlsx', 'Sheet3')
			
			prediction = clf.predict(pred)
			print(prediction)

			optimum = optimum[optimum['CLASS'] != prediction[0]]
			X = optimum.drop("CLASS",axis=1)
			y = optimum.CLASS
			clf = KNeighborsClassifier(n_neighbors=3)
			clf.fit(X,y)
			prediction1 = clf.predict(pred)
			print(prediction1)


			optimum = optimum[optimum['CLASS'] != prediction1[0]]
			X = optimum.drop("CLASS",axis=1)
			y = optimum.CLASS
			clf = KNeighborsClassifier(n_neighbors=3)
			clf.fit(X,y)
			prediction2 = clf.predict(pred)
			print(prediction2)
			p1 = prediction1[0]
			p2 = prediction2[0]
			p1 = p1 -1
			p2 = p2 -1
			# print()

			if(prediction == 7):
				return render_template('crops.html' , crop="TOMATO" , crop1=prediction1[0] , crop2=prediction2[0] , price=price["Price/hr"].iloc[6] ,price1=price["Price/hr"].iloc[p1] , price2=price["Price/hr"].iloc[p2]) 
			elif(prediction == 1):
				return render_template('crops.html' , crop="GARLIC" , crop1=prediction1[0] , crop2=prediction2[0] , price=price["Price/hr"].iloc[[0]] ,price1=price["Price/hr"].iloc[p1] , price2=price["Price/hr"].iloc[p2])
			elif(prediction == 2):
				return render_template('crops.html' , crop="ONION" , crop1=prediction1[0] , crop2=prediction2[0] , price=price["Price/hr"].iloc[[1]] , price1=price["Price/hr"].iloc[p1] , price2=price["Price/hr"].iloc[p2])
			elif(prediction == 3):
				return render_template('crops.html' , crop="ORANGE" , crop1=prediction1[0] , crop2=prediction2[0] , price=price["Price/hr"].iloc[[2]] , price1=price["Price/hr"].iloc[p1] , price2=price["Price/hr"].iloc[p2])
			elif(prediction == 4):
				return render_template('crops.html' , crop="PEAS" , crop1=prediction1[0] , crop2=prediction2[0] , price=price["Price/hr"].iloc[[3]] , price1=price["Price/hr"].iloc[p1] , price2=price["Price/hr"].iloc[p2])
			elif(prediction == 5):
				return render_template('crops.html' , crop="POTATO" , crop1=prediction1[0] , crop2=prediction2[0] , price=price["Price/hr"].iloc[[4]] , price1=price["Price/hr"].iloc[p1] , price2=price["Price/hr"].iloc[p2])
			elif(prediction == 6):
				return render_template('crops.html' , crop="RICE" , crop1=prediction1[0] , crop2=prediction2[0] , price=price["Price/hr"].iloc[[5]] , price1=price["Price/hr"].iloc[p1] , price2=price["Price/hr"].iloc[p2])
			elif(prediction == 8):
				return render_template('crops.html' , crop="SUGARCANE" , crop1=prediction1[0] , crop2=prediction2[0] , price=price["Price/hr"].iloc[[7]] , price1=price["Price/hr"].iloc[p1] , price2=price["Price/hr"].iloc[p2])		
			else:
				return "no"

		else:
			potassium = request.form.get('Potassium')
			phosphorous = request.form.get('Phosphorous')
			nitrogen = request.form.get('Nitrogen') 
			pH = request.form.get('pH')
			temperature = request.form.get('Temperature')

			columns = ['N','P','K','pH','TEMPERATURE'] 
			values = np.array([ nitrogen ,phosphorous ,potassium ,   pH , temperature])
			pred = pd.DataFrame(values.reshape(-1, len(values)),columns=columns)
			# print(pred.dtype)
			print(pred)

			prediction = clf.predict(pred)
			print(prediction)

			optimum = optimum[optimum['CLASS'] != prediction[0]]
			X = optimum.drop("CLASS",axis=1)
			y = optimum.CLASS
			clf = KNeighborsClassifier(n_neighbors=3)
			clf.fit(X,y)
			prediction1 = clf.predict(pred)
			print(prediction1)


			optimum = optimum[optimum['CLASS'] != prediction1[0]]
			X = optimum.drop("CLASS",axis=1)
			y = optimum.CLASS
			clf = KNeighborsClassifier(n_neighbors=3)
			clf.fit(X,y)
			prediction2 = clf.predict(pred)
			print(prediction2)

			p1 = prediction1[0]
			p2 = prediction2[0]
			p1 = p1 -1
			p2 = p2 -1
			# print()

			if(prediction == 7):
				return render_template('crops.html' , crop="TOMATO" , crop1=prediction1[0] , crop2=prediction2[0] , price=price["Price/hr"].iloc[6] ,price1=price["Price/hr"].iloc[p1] , price2=price["Price/hr"].iloc[p2]) 
			elif(prediction == 1):
				return render_template('crops.html' , crop="GARLIC" , crop1=prediction1[0] , crop2=prediction2[0] , price=price["Price/hr"].iloc[0] ,price1=price["Price/hr"].iloc[p1] , price2=price["Price/hr"].iloc[p2])
			elif(prediction == 2):
				return render_template('crops.html' , crop="ONION" , crop1=prediction1[0] , crop2=prediction2[0] , price=price["Price/hr"].iloc[1] , price1=price["Price/hr"].iloc[p1] , price2=price["Price/hr"].iloc[p2])
			elif(prediction == 3):
				return render_template('crops.html' , crop="ORANGE" , crop1=prediction1[0] , crop2=prediction2[0] , price=price["Price/hr"].iloc[2] , price1=price["Price/hr"].iloc[p1] , price2=price["Price/hr"].iloc[p2])
			elif(prediction == 4):
				return render_template('crops.html' , crop="PEAS" , crop1=prediction1[0] , crop2=prediction2[0] , price=price["Price/hr"].iloc[3] , price1=price["Price/hr"].iloc[p1] , price2=price["Price/hr"].iloc[p2])
			elif(prediction == 5):
				return render_template('crops.html' , crop="POTATO" , crop1=prediction1[0] , crop2=prediction2[0] , price=price["Price/hr"].iloc[4] , price1=price["Price/hr"].iloc[p1] , price2=price["Price/hr"].iloc[p2])
			elif(prediction == 6):
				return render_template('crops.html' , crop="RICE" , crop1=prediction1[0] , crop2=prediction2[0] , price=price["Price/hr"].iloc[5] , price1=price["Price/hr"].iloc[p1] , price2=price["Price/hr"].iloc[p2])
			elif(prediction == 8):
				return render_template('crops.html' , crop="SUGARCANE" , crop1=prediction1[0] , crop2=prediction2[0] , price=price["Price/hr"].iloc[7] , price1=price["Price/hr"].iloc[p1] , price2=price["Price/hr"].iloc[p2])		
			else:
				return "no"
	# render_template('index.html')
	else:
		return render_template('index.html')


		 
		

		
if (__name__ == "__main__"):	
	app.run(host='127.0.0.1', debug=True, port=8000)

<filename>python UI 2.0/server.py
from flask import Flask , render_template , request , redirect , url_for
app = Flask(__name__)

_code = ""
@app.route("/users/index", methods=["GET","POST"])
def main():
		return render_template('index.html')

@app.route("/users/graph", methods=["GET","POST"])
def login():
		return render_template('chart.html')

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

@app.route("/users/analyse/predict", methods=["GET","POST"])
def predict():
	import pandas as pd
	fertilizer_data = pd.read_excel("optimum2.xlsx", 'biofertilizer')
	X = fertilizer_data.drop("CLASS",axis=1)
	y = fertilizer_data.CLASS

	pred = pd.read_excel('optimum2.xlsx', 'Sheet3')
	pred = pred.drop(["pH" , "Temperature"] , axis=1)
	from sklearn.neighbors import KNeighborsClassifier
	clf = KNeighborsClassifier(n_neighbors=1)
	clf.fit(X,y)
	prediction1 = clf.predict(pred)
	print(prediction1)

	if(prediction1[0] == 1):
		return render_template("fertilizer.html" , fertilizer="Azotobacter	Bacillus_circulans	Pisolithus_sp")
	elif(prediction1[0] == 2):
		return render_template("fertilizer.html" , fertilizer="Azotobacter	Bacillus_circulans	Sclerocystis_sp")
	elif(prediction1[0] == 3):
		return render_template("fertilizer.html" , fertilizer="Azotobacter,	Bacillus_circulans,	Acaulospora_sp")
	elif(prediction1[0] == 4):
		return render_template("fertilizer.html" , fertilizer="Azotobacter, Pseudomonas_striata, Pisolithus_sp")
	elif(prediction1[0] == 5):
		return render_template("fertilizer.html" , fertilizer="Azotobacter,	Pseudomonas_striata, Sclerocystis_sp")
	elif(prediction1[0] == 6):
		return render_template("fertilizer.html" , fertilizer="Azotobacter,	Pseudomonas_striata, Acaulospora_sp")
	elif(prediction1[0] == 7):
		return render_template("fertilizer.html" , fertilizer="Azotobacter,	Penicillium_sp, Pisolithus_sp")
	elif(prediction1[0] == 8):
		return render_template("fertilizer.html" , fertilizer="Azotobacter,	Penicillium_sp,	Sclerocystis_sp")
	elif(prediction1[0] == 9):
		return render_template("fertilizer.html" , fertilizer="Azotobacter,	Penicillium_sp,	Acaulospora_sp")
	elif(prediction1[0] == 10):
		return render_template("fertilizer.html" , fertilizer="Frankia,	Bacillus_circulans,	Pisolithus_sp")
	elif(prediction1[0] == 11):
		return render_template("fertilizer.html" , fertilizer="Frankia,	Bacillus_circulans,	Sclerocystis_sp")
	elif(prediction1[0] == 12):
		return render_template("fertilizer.html" , fertilizer="Frankia,	Bacillus_circulans,	Acaulospora_sp")
	elif(prediction1[0] == 13):
		return render_template("fertilizer.html" , fertilizer="Frankia,	Pseudomonas_striata, Pisolithus_sp")
	elif(prediction1[0] == 14):
		return render_template("fertilizer.html" , fertilizer="Frankia,	Pseudomonas_striata, Sclerocystis_sp")
	elif(prediction1[0] == 15):
		return render_template("fertilizer.html" , fertilizer="Frankia,	Pseudomonas_striata, Acaulospora_sp")
	elif(prediction1[0] == 16):
		return render_template("fertilizer.html" , fertilizer="Frankia,	Penicillium_sp,	Pisolithus_sp")
	elif(prediction1[0] == 17):
		return render_template("fertilizer.html" , fertilizer="Frankia,	Penicillium_sp,	Sclerocystis_sp")
	elif(prediction1[0] == 18):
		return render_template("fertilizer.html" , fertilizer="Frankia,	Penicillium_sp,	Acaulospora_sp")
	elif(prediction1[0] == 19):
		return render_template("fertilizer.html" , fertilizer="Anabaena, Bacillus_circulans, Pisolithus_sp")
	elif(prediction1[0] == 20):
		return render_template("fertilizer.html" , fertilizer="Anabaena, Bacillus_circulans, Pisolithus_sp")
	elif(prediction1[0] == 21):
		return render_template("fertilizer.html" , fertilizer="Anabaena, Bacillus_circulans, Pisolithus_sp")
	elif(prediction1[0] == 22):
		return render_template("fertilizer.html" , fertilizer="Anabaena, Pseudomonas_striata, Pisolithus_sp")
	elif(prediction1[0] == 23):
		return render_template("fertilizer.html" , fertilizer="Anabaena, Pseudomonas_striata, Sclerocystis_sp")
	elif(prediction1[0] == 24):
		return render_template("fertilizer.html" , fertilizer="Anabaen,	Pseudomonas_striata, Acaulospora_sp")		
	elif(prediction1[0] == 25):
		return render_template("fertilizer.html" , fertilizer="Anabaena, Penicillium_sp, Pisolithus_sp")
	elif(prediction1[0] == 26):
		return render_template("fertilizer.html" , fertilizer="Anabaena, Penicillium_sp, Sclerocystis_sp")
	else:
		return render_template("fertilizer.html" , fertilizer="Anabaena, Penicillium_sp, Acaulospora_sp")

@app.route("/users/analyse", methods=["POST"])
def analyse():
	if(request.method == "POST"):
		import pandas as pd
		import numpy as np
		# import os
		optimum = pd.read_excel("optimum2.xlsx", 'newData')
		price = pd.read_excel("optimum2.xlsx", 'pricePerhr')
		optimum['N'] = optimum.N.astype(float)
		optimum['P'] = optimum.P.astype(float)
		optimum['K'] = optimum.K.astype(float)
		optimum['TEMPERATURE'] = optimum.TEMPERATURE.astype(float)
		X = optimum.drop("CLASS",axis=1)
		y = optimum.CLASS

		from sklearn.neighbors import KNeighborsClassifier
		clf = KNeighborsClassifier(n_neighbors=3)
		clf.fit(X,y)

		print(request.form.get('Potassium'))
		
		if(request.form.get('Potassium') == None):
			
			pred = pd.read_excel('optimum2.xlsx', 'Sheet3')
			
			prediction = clf.predict(pred)
			print(prediction)

			optimum = optimum[optimum['CLASS'] != prediction[0]]
			X = optimum.drop("CLASS",axis=1)
			y = optimum.CLASS
			clf = KNeighborsClassifier(n_neighbors=3)
			clf.fit(X,y)
			prediction1 = clf.predict(pred)
			print(prediction1)


			optimum = optimum[optimum['CLASS'] != prediction1[0]]
			X = optimum.drop("CLASS",axis=1)
			y = optimum.CLASS
			clf = KNeighborsClassifier(n_neighbors=3)
			clf.fit(X,y)
			prediction2 = clf.predict(pred)
			print(prediction2)
			p1 = prediction1[0]
			p2 = prediction2[0]
			p1 = p1 -1
			p2 = p2 -1
			# print()

			if(prediction == 7):
				return render_template('crops.html' , crop="TOMATO" , crop1=prediction1[0] , crop2=prediction2[0] , price=price["Price/hr"].iloc[6] ,price1=price["Price/hr"].iloc[p1] , price2=price["Price/hr"].iloc[p2]) 
			elif(prediction == 1):
				return render_template('crops.html' , crop="GARLIC" , crop1=prediction1[0] , crop2=prediction2[0] , price=price["Price/hr"].iloc[[0]] ,price1=price["Price/hr"].iloc[p1] , price2=price["Price/hr"].iloc[p2])
			elif(prediction == 2):
				return render_template('crops.html' , crop="ONION" , crop1=prediction1[0] , crop2=prediction2[0] , price=price["Price/hr"].iloc[[1]] , price1=price["Price/hr"].iloc[p1] , price2=price["Price/hr"].iloc[p2])
			elif(prediction == 3):
				return render_template('crops.html' , crop="ORANGE" , crop1=prediction1[0] , crop2=prediction2[0] , price=price["Price/hr"].iloc[[2]] , price1=price["Price/hr"].iloc[p1] , price2=price["Price/hr"].iloc[p2])
			elif(prediction == 4):
				return render_template('crops.html' , crop="PEAS" , crop1=prediction1[0] , crop2=prediction2[0] , price=price["Price/hr"].iloc[[3]] , price1=price["Price/hr"].iloc[p1] , price2=price["Price/hr"].iloc[p2])
			elif(prediction == 5):
				return render_template('crops.html' , crop="POTATO" , crop1=prediction1[0] , crop2=prediction2[0] , price=price["Price/hr"].iloc[[4]] , price1=price["Price/hr"].iloc[p1] , price2=price["Price/hr"].iloc[p2])
			elif(prediction == 6):
				return render_template('crops.html' , crop="RICE" , crop1=prediction1[0] , crop2=prediction2[0] , price=price["Price/hr"].iloc[[5]] , price1=price["Price/hr"].iloc[p1] , price2=price["Price/hr"].iloc[p2])
			elif(prediction == 8):
				return render_template('crops.html' , crop="SUGARCANE" , crop1=prediction1[0] , crop2=prediction2[0] , price=price["Price/hr"].iloc[[7]] , price1=price["Price/hr"].iloc[p1] , price2=price["Price/hr"].iloc[p2])		
			else:
				return "no"

		else:
			potassium = request.form.get('Potassium')
			phosphorous = request.form.get('Phosphorous')
			nitrogen = request.form.get('Nitrogen') 
			pH = request.form.get('pH')
			temperature = request.form.get('Temperature')

			columns = ['N','P','K','pH','TEMPERATURE'] 
			values = np.array([ nitrogen ,phosphorous ,potassium ,   pH , temperature])
			pred = pd.DataFrame(values.reshape(-1, len(values)),columns=columns)
			# print(pred.dtype)
			print(pred)

			prediction = clf.predict(pred)
			print(prediction)

			optimum = optimum[optimum['CLASS'] != prediction[0]]
			X = optimum.drop("CLASS",axis=1)
			y = optimum.CLASS
			clf = KNeighborsClassifier(n_neighbors=3)
			clf.fit(X,y)
			prediction1 = clf.predict(pred)
			print(prediction1)


			optimum = optimum[optimum['CLASS'] != prediction1[0]]
			X = optimum.drop("CLASS",axis=1)
			y = optimum.CLASS
			clf = KNeighborsClassifier(n_neighbors=3)
			clf.fit(X,y)
			prediction2 = clf.predict(pred)
			print(prediction2)

			p1 = prediction1[0]
			p2 = prediction2[0]
			p1 = p1 -1
			p2 = p2 -1
			# print()

			if(prediction == 7):
				return render_template('crops.html' , crop="TOMATO" , crop1=prediction1[0] , crop2=prediction2[0] , price=price["Price/hr"].iloc[6] ,price1=price["Price/hr"].iloc[p1] , price2=price["Price/hr"].iloc[p2]) 
			elif(prediction == 1):
				return render_template('crops.html' , crop="GARLIC" , crop1=prediction1[0] , crop2=prediction2[0] , price=price["Price/hr"].iloc[0] ,price1=price["Price/hr"].iloc[p1] , price2=price["Price/hr"].iloc[p2])
			elif(prediction == 2):
				return render_template('crops.html' , crop="ONION" , crop1=prediction1[0] , crop2=prediction2[0] , price=price["Price/hr"].iloc[1] , price1=price["Price/hr"].iloc[p1] , price2=price["Price/hr"].iloc[p2])
			elif(prediction == 3):
				return render_template('crops.html' , crop="ORANGE" , crop1=prediction1[0] , crop2=prediction2[0] , price=price["Price/hr"].iloc[2] , price1=price["Price/hr"].iloc[p1] , price2=price["Price/hr"].iloc[p2])
			elif(prediction == 4):
				return render_template('crops.html' , crop="PEAS" , crop1=prediction1[0] , crop2=prediction2[0] , price=price["Price/hr"].iloc[3] , price1=price["Price/hr"].iloc[p1] , price2=price["Price/hr"].iloc[p2])
			elif(prediction == 5):
				return render_template('crops.html' , crop="POTATO" , crop1=prediction1[0] , crop2=prediction2[0] , price=price["Price/hr"].iloc[4] , price1=price["Price/hr"].iloc[p1] , price2=price["Price/hr"].iloc[p2])
			elif(prediction == 6):
				return render_template('crops.html' , crop="RICE" , crop1=prediction1[0] , crop2=prediction2[0] , price=price["Price/hr"].iloc[5] , price1=price["Price/hr"].iloc[p1] , price2=price["Price/hr"].iloc[p2])
			elif(prediction == 8):
				return render_template('crops.html' , crop="SUGARCANE" , crop1=prediction1[0] , crop2=prediction2[0] , price=price["Price/hr"].iloc[7] , price1=price["Price/hr"].iloc[p1] , price2=price["Price/hr"].iloc[p2])		
			else:
				return "no"
	# render_template('index.html')
	else:
		return render_template('index.html')


		 
		

		
if (__name__ == "__main__"):	
	app.run(host='127.0.0.1', debug=True, port=8000)

# import os # print() # print(pred.dtype) # print() # render_template('index.html')