pranit144/fertilizer_recommendation_usage

1from flask import Flask, render_template, request2import joblib3import pandas as pd4import google.generativeai as genai5import os6 7 8app = Flask(__name__)9 10# Load the trained Random Forest models11rf_ferti_name = joblib.load('rf_ferti_name.pkl')12rf_ferti_value = joblib.load('rf_ferti_value.pkl')13 14# Manually define the encodings based on the provided dictionaries15soil_type_encodings = {'Black': 0, 'Clayey': 1, 'Loamy': 2, 'Red': 3, 'Sandy': 4}16crop_type_encodings = {'Barley': 0, 'Cotton': 1, 'Ground Nuts': 2, 'Maize': 3, 'Millets': 4,17                       'Oil seeds': 5, 'Other Variety': 6, 'Paddy': 7, 'Pulses': 8, 'Sugarcane': 9,18                       'Tobacco': 10, 'Wheat': 11}19fertilizer_name_encodings = {'10-26-26': 0, '14-35-14': 1, '15-15-15': 2, '17-17-17': 3, '20-20': 4,20                             '20-20-20': 5, '28-28': 6, 'Ammonium sulfate': 7, 'Biofertilizer (e.g., Rhizobium)': 8,21                             'Calcium nitrate': 9, 'DAP': 10, 'Ferrous sulfate': 11, 'Magnesium sulfate': 12,22                             'Potassium chloride/Muriate of potash (MOP)': 13, 'Potassium sulfate/Sulfate of potash (SOP)': 14,23                             'Rock phosphate (RP)': 15, 'Single superphosphate (SSP)': 16, 'Triple superphosphate (TSP)': 17,24                             'Urea': 18, 'Zinc sulfate': 19}25 26 27# AI configuration28api_key=os.getenv('GEMINI_API')29genai.configure(api_key=api_key)30model = genai.GenerativeModel("gemini-1.5-flash")31 32def generate_ai_suggestions(pred_fertilizer_name):33    prompt = (34        f"For {pred_fertilizer_name} fertlizer, generate 3-4  sentences each on a new line, note text shoudl be jsutidied should not contian anyu special character"35    )36    response = model.generate_content(prompt)37    return response.text38 39 40 41@app.route('/', methods=['GET', 'POST'])42def index():43    if request.method == 'POST':44        # Retrieve form data45        temperature = float(request.form['temperature'])46        humidity = float(request.form['humidity'])47        moisture = float(request.form['moisture'])48        soil_type = request.form['soil_type']49        crop_type = request.form['crop_type']50        nitrogen = float(request.form['nitrogen'])51        potassium = float(request.form['potassium'])52        phosphorous = float(request.form['phosphorous'])53 54        # Encode categorical data55        soil_type_encoded = soil_type_encodings.get(soil_type, -1)56        crop_type_encoded = crop_type_encodings.get(crop_type, -1)57 58        # Create a DataFrame for the input59        user_input = pd.DataFrame({60            'Temperature': [temperature],61            'Humidity': [humidity],62            'Moisture': [moisture],63            'Nitrogen': [nitrogen],64            'Potassium': [potassium],65            'Phosphorous': [phosphorous],66            'Soil Type': [soil_type_encoded],67            'Crop Type': [crop_type_encoded]68        })69 70        # Predict Fertilizer Name71        pred_fertilizer_name = rf_ferti_name.predict(user_input)[0]72        pred_fertilizer_name = [name for name, value in fertilizer_name_encodings.items() if value == pred_fertilizer_name][0]73 74        # Predict Fertilizer Quantity75        pred_fertilizer_qty = rf_ferti_value.predict(user_input)[0]76        pred_info = generate_ai_suggestions(pred_fertilizer_name)77 78        return render_template('index.html', prediction=True, fertilizer_name=pred_fertilizer_name,79                               fertilizer_qty=pred_fertilizer_qty, optimal_usage=pred_fertilizer_qty,pred_info=pred_info)80    return render_template('index.html', prediction=False)81 82if __name__ == '__main__':83    app.run(port=7860,host='0.0.0.0')84