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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)
# 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) |