Update app.py

app.py

@@ -1,81 +1,83 @@
-from flask import Flask, render_template, request
-import joblib
-import pandas as pd
-import google.generativeai as genai
-
-
-app = Flask(__name__)
-
-# Load the trained Random Forest models
-rf_ferti_name = joblib.load('rf_ferti_name.pkl')
-rf_ferti_value = joblib.load('rf_ferti_value.pkl')
-
-# Manually define the encodings based on the provided dictionaries
-soil_type_encodings = {'Black': 0, 'Clayey': 1, 'Loamy': 2, 'Red': 3, 'Sandy': 4}
-crop_type_encodings = {'Barley': 0, 'Cotton': 1, 'Ground Nuts': 2, 'Maize': 3, 'Millets': 4,
-                       'Oil seeds': 5, 'Other Variety': 6, 'Paddy': 7, 'Pulses': 8, 'Sugarcane': 9,
-                       'Tobacco': 10, 'Wheat': 11}
-fertilizer_name_encodings = {'10-26-26': 0, '14-35-14': 1, '15-15-15': 2, '17-17-17': 3, '20-20': 4,
-                             '20-20-20': 5, '28-28': 6, 'Ammonium sulfate': 7, 'Biofertilizer (e.g., Rhizobium)': 8,
-                             'Calcium nitrate': 9, 'DAP': 10, 'Ferrous sulfate': 11, 'Magnesium sulfate': 12,
-                             'Potassium chloride/Muriate of potash (MOP)': 13, 'Potassium sulfate/Sulfate of potash (SOP)': 14,
-                             'Rock phosphate (RP)': 15, 'Single superphosphate (SSP)': 16, 'Triple superphosphate (TSP)': 17,
-                             'Urea': 18, 'Zinc sulfate': 19}
-
-
-# AI configuration
-genai.configure(api_key='AIzaSyBtXV2xJbrWVV57B5RWy_meKXOA59HFMeY')
-model = genai.GenerativeModel("gemini-1.5-flash")
-
-def generate_ai_suggestions(pred_fertilizer_name):
-    prompt = (
-        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"
-    )
-    response = model.generate_content(prompt)
-    return response.text
-
-
-
-@app.route('/', methods=['GET', 'POST'])
-def index():
-    if request.method == 'POST':
-        # Retrieve form data
-        temperature = float(request.form['temperature'])
-        humidity = float(request.form['humidity'])
-        moisture = float(request.form['moisture'])
-        soil_type = request.form['soil_type']
-        crop_type = request.form['crop_type']
-        nitrogen = float(request.form['nitrogen'])
-        potassium = float(request.form['potassium'])
-        phosphorous = float(request.form['phosphorous'])
-
-        # Encode categorical data
-        soil_type_encoded = soil_type_encodings.get(soil_type, -1)
-        crop_type_encoded = crop_type_encodings.get(crop_type, -1)
-
-        # Create a DataFrame for the input
-        user_input = pd.DataFrame({
-            'Temperature': [temperature],
-            'Humidity': [humidity],
-            'Moisture': [moisture],
-            'Nitrogen': [nitrogen],
-            'Potassium': [potassium],
-            'Phosphorous': [phosphorous],
-            'Soil Type': [soil_type_encoded],
-            'Crop Type': [crop_type_encoded]
-        })
-
-        # Predict Fertilizer Name
-        pred_fertilizer_name = rf_ferti_name.predict(user_input)[0]
-        pred_fertilizer_name = [name for name, value in fertilizer_name_encodings.items() if value == pred_fertilizer_name][0]
-
-        # Predict Fertilizer Quantity
-        pred_fertilizer_qty = rf_ferti_value.predict(user_input)[0]
-        pred_info = generate_ai_suggestions(pred_fertilizer_name)
-
-        return render_template('index.html', prediction=True, fertilizer_name=pred_fertilizer_name,
-                               fertilizer_qty=pred_fertilizer_qty, optimal_usage=pred_fertilizer_qty,pred_info=pred_info)
-    return render_template('index.html', prediction=False)
-
-if __name__ == '__main__':
-    app.run(debug=True)
+from flask import Flask, render_template, request
+import joblib
+import pandas as pd
+import google.generativeai as genai
+import os
+
+
+app = Flask(__name__)
+
+# Load the trained Random Forest models
+rf_ferti_name = joblib.load('rf_ferti_name.pkl')
+rf_ferti_value = joblib.load('rf_ferti_value.pkl')
+
+# Manually define the encodings based on the provided dictionaries
+soil_type_encodings = {'Black': 0, 'Clayey': 1, 'Loamy': 2, 'Red': 3, 'Sandy': 4}
+crop_type_encodings = {'Barley': 0, 'Cotton': 1, 'Ground Nuts': 2, 'Maize': 3, 'Millets': 4,
+                       'Oil seeds': 5, 'Other Variety': 6, 'Paddy': 7, 'Pulses': 8, 'Sugarcane': 9,
+                       'Tobacco': 10, 'Wheat': 11}
+fertilizer_name_encodings = {'10-26-26': 0, '14-35-14': 1, '15-15-15': 2, '17-17-17': 3, '20-20': 4,
+                             '20-20-20': 5, '28-28': 6, 'Ammonium sulfate': 7, 'Biofertilizer (e.g., Rhizobium)': 8,
+                             'Calcium nitrate': 9, 'DAP': 10, 'Ferrous sulfate': 11, 'Magnesium sulfate': 12,
+                             'Potassium chloride/Muriate of potash (MOP)': 13, 'Potassium sulfate/Sulfate of potash (SOP)': 14,
+                             'Rock phosphate (RP)': 15, 'Single superphosphate (SSP)': 16, 'Triple superphosphate (TSP)': 17,
+                             'Urea': 18, 'Zinc sulfate': 19}
+
+
+# AI configuration
+api_key=os.getenv('GEMINI_API')
+genai.configure(api_key=api_key)
+model = genai.GenerativeModel("gemini-1.5-flash")
+
+def generate_ai_suggestions(pred_fertilizer_name):
+    prompt = (
+        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"
+    )
+    response = model.generate_content(prompt)
+    return response.text
+
+
+
+@app.route('/', methods=['GET', 'POST'])
+def index():
+    if request.method == 'POST':
+        # Retrieve form data
+        temperature = float(request.form['temperature'])
+        humidity = float(request.form['humidity'])
+        moisture = float(request.form['moisture'])
+        soil_type = request.form['soil_type']
+        crop_type = request.form['crop_type']
+        nitrogen = float(request.form['nitrogen'])
+        potassium = float(request.form['potassium'])
+        phosphorous = float(request.form['phosphorous'])
+
+        # Encode categorical data
+        soil_type_encoded = soil_type_encodings.get(soil_type, -1)
+        crop_type_encoded = crop_type_encodings.get(crop_type, -1)
+
+        # Create a DataFrame for the input
+        user_input = pd.DataFrame({
+            'Temperature': [temperature],
+            'Humidity': [humidity],
+            'Moisture': [moisture],
+            'Nitrogen': [nitrogen],
+            'Potassium': [potassium],
+            'Phosphorous': [phosphorous],
+            'Soil Type': [soil_type_encoded],
+            'Crop Type': [crop_type_encoded]
+        })
+
+        # Predict Fertilizer Name
+        pred_fertilizer_name = rf_ferti_name.predict(user_input)[0]
+        pred_fertilizer_name = [name for name, value in fertilizer_name_encodings.items() if value == pred_fertilizer_name][0]
+
+        # Predict Fertilizer Quantity
+        pred_fertilizer_qty = rf_ferti_value.predict(user_input)[0]
+        pred_info = generate_ai_suggestions(pred_fertilizer_name)
+
+        return render_template('index.html', prediction=True, fertilizer_name=pred_fertilizer_name,
+                               fertilizer_qty=pred_fertilizer_qty, optimal_usage=pred_fertilizer_qty,pred_info=pred_info)
+    return render_template('index.html', prediction=False)
+
+if __name__ == '__main__':
+    app.run(debug=True)