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)