Update app.py

app.py

@@ -1,71 +1,73 @@
-from flask import Flask, render_template, request, jsonify
-import joblib
-import google.generativeai as genai
-
-# Initialize the Flask app
-app = Flask(__name__)
-
-# Load the trained model
-gbm_model = joblib.load('gbm_model.pkl')
-
-# Configure Gemini AI
-genai.configure(api_key='AIzaSyBtXV2xJbrWVV57B5RWy_meKXOA59HFMeY')
-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(debug=True)
+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')