Update app.py

app.py

@@ -4,10 +4,10 @@ import numpy as np
 
 app = Flask(__name__)
 
-# Load trained model
+# Load the trained model
 model = pickle.load(open("crop.pkl", "rb"))
 
-# Load trained LabelEncoder
+# Load the SAME LabelEncoder used during training
 le = pickle.load(open("label_encoder.pkl", "rb"))
 
 @app.route("/")
@@ -27,8 +27,7 @@ def predict():
     features = np.array([[N, P, K, temperature, humidity, ph, rainfall]])
     pred = model.predict(features)
 
-    # Convert numeric → crop name
     crop = le.inverse_transform(pred)[0]
 
-    return render_template("result.html", prediction_text=f"Recommended Crop: {crop}")
+    return render_template('result.html', prediction_text=f"Recommended Crop: {crop}")