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from flask import Flask, render_template, request, jsonify
import joblib
import google.generativeai as genai
import os
# Initialize the Flask app
app = Flask(__name__)
# Load the trained model
gbm_model = joblib.load('gbm_model.pkl')
api_key=os.getenv('GEMINI_API')
# Configure Gemini AI
genai.configure(api_key=api_key)
model = genai.GenerativeModel("gemini-1.5-flash")
# Mapping for class decoding
class_mapping = {
0: 'BANANA', 1: 'BLACKGRAM', 2: 'CHICKPEA', 3: 'COCONUT', 4: 'COFFEE',
5: 'COTTON', 6: 'JUTE', 7: 'KIDNEYBEANS', 8: 'LENTIL', 9: 'MAIZE',
10: 'MANGO', 11: 'MOTHBEANS', 12: 'MUNGBEAN', 13: 'MUSKMELON',
14: 'ORANGE', 15: 'PAPAYA', 16: 'PIGEONPEAS', 17: 'POMEGRANATE',
18: 'RICE', 19: 'WATERMELON'
}
# AI suggestions from Gemini
def generate_ai_suggestions(pred_crop_name, parameters):
prompt = (
f"For the crop {pred_crop_name} based on the input parameters {parameters}, "
f"Give descritpion of provided crop in justified 3-4 line sparagraph."
f"After that spacing of one to two lines"
f"**in the next line** recokemnd foru other crops based on parpameeters as Other recommended crops : crop names in numbvered order. dont include any special character not bold,italic."
)
response = model.generate_content(prompt)
return response.text
@app.route('/')
def index():
return render_template('index.html')
@app.route('/predict', methods=['POST'])
def predict():
# Get input values from the form
nitrogen = float(request.form['nitrogen'])
phosphorus = float(request.form['phosphorus'])
potassium = float(request.form['potassium'])
temperature = float(request.form['temperature'])
humidity = float(request.form['humidity'])
ph = float(request.form['ph'])
rainfall = float(request.form['rainfall'])
location = request.form['location']
# Prepare the features for the model
features = [[nitrogen, phosphorus, potassium, temperature, humidity, ph, rainfall]]
predicted_crop_encoded = gbm_model.predict(features)[0]
predicted_crop = class_mapping[predicted_crop_encoded]
# Get AI suggestions from Gemini
parameters = {
"Nitrogen": nitrogen, "Phosphorus": phosphorus, "Potassium": potassium,
"Temperature": temperature, "Humidity": humidity, "pH": ph, "Rainfall": rainfall,
"Location": location
}
ai_suggestions = generate_ai_suggestions(predicted_crop, parameters)
return jsonify({
'predicted_crop': predicted_crop,
'ai_suggestions': ai_suggestions,
'location': location
})
if __name__ == '__main__':
app.run(port=7860,host='0.0.0.0')
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