from flask import Flask, render_template, request
import numpy as np
import pandas as pd
import pickle
import os
from config import IMAGE_BASE_URL
from image_utils import get_base64_images

app = Flask(__name__)

# Load images once at startup
IMAGES = get_base64_images()

# Add this function
@app.context_processor
def utility_processor():
    def get_image_url(path):
        return IMAGE_BASE_URL + path
    return dict(get_image_url=get_image_url, images=IMAGES)

# Load and prepare data for scalers
try:
    # Try to load the models first
    with open('model.pkl', 'rb') as f:
        model = pickle.load(f)
    with open('standscaler.pkl', 'rb') as f:
        sc = pickle.load(f)
    with open('minmaxscaler.pkl', 'rb') as f:
        ms = pickle.load(f)
    print("Models loaded successfully")
except Exception as e:
    print(f"Error loading models: {e}")
    try:
        # Load the dataset and fit scalers
        data = pd.read_csv('Crop_recommendation.csv')
        features = ['N', 'P', 'K', 'temperature', 'humidity', 'ph', 'rainfall']
        X = data[features].values
        y = data['label'].values

        # Create and fit scalers with actual data
        from sklearn.preprocessing import StandardScaler, MinMaxScaler
        ms = MinMaxScaler()
        ms.fit(X)
        
        # Transform with MinMaxScaler first
        X_minmax = ms.transform(X)
        
        sc = StandardScaler()
        sc.fit(X_minmax)
        
        # Create and fit model with transformed data
        from sklearn.ensemble import RandomForestClassifier
        model = RandomForestClassifier(n_estimators=100, random_state=42)
        model.fit(sc.transform(X_minmax), y)
        print("Models fitted successfully with dataset")
        
        # Save the fitted models
        with open('model.pkl', 'wb') as f:
            pickle.dump(model, f)
        with open('standscaler.pkl', 'wb') as f:
            pickle.dump(sc, f)
        with open('minmaxscaler.pkl', 'wb') as f:
            pickle.dump(ms, f)
            
    except Exception as e:
        print(f"Error fitting models: {e}")
        # Create and fit default scalers with sample data as fallback
        sample_data = np.array([[90, 40, 40, 20, 80, 7, 200],
                              [20, 30, 10, 25, 60, 6, 100]])
        
        ms = MinMaxScaler()
        ms.fit(sample_data)
        
        sc = StandardScaler()
        sc.fit(sample_data)
        
        model = RandomForestClassifier()
        model.fit(sample_data, [1, 2])
        print("Using default models")

# Define routes
@app.route('/')
def home():
    return render_template('home.html')

@app.route('/info')
def info():
    return render_template('info.html')

@app.route('/recommendation', methods=['GET', 'POST'])
def recommendation():
    result = None
    N = P = K = temp = humidity = ph = rainfall = None

    if request.method == 'POST':
        try:
            # Get form data
            N = float(request.form['Nitrogen'])
            P = float(request.form['Phosporus'])
            K = float(request.form['Potassium'])
            temp = float(request.form['Temperature'])
            humidity = float(request.form['Humidity'])
            ph = float(request.form['Ph'])
            rainfall = float(request.form['Rainfall'])

            # Prepare features
            feature_list = [N, P, K, temp, humidity, ph, rainfall]
            single_pred = np.array(feature_list).reshape(1, -1)

            # Scale features
            try:
                scaled_features = ms.transform(single_pred)
                final_features = sc.transform(scaled_features)
                
                # Make prediction
                prediction = model.predict(final_features)
                
                # Map prediction to crop name
                crop_dict = {1: "Rice", 2: "Maize", 3: "Jute", 4: "Cotton", 5: "Coconut", 6: "Papaya", 7: "Orange",
                            8: "Apple", 9: "Muskmelon", 10: "Watermelon", 11: "Grapes", 12: "Mango", 13: "Banana",
                            14: "Pomegranate", 15: "Lentil", 16: "Blackgram", 17: "Mungbean", 18: "Mothbeans",
                            19: "Pigeonpeas", 20: "Kidneybeans", 21: "Chickpea", 22: "Coffee"}

                if prediction[0] in crop_dict:
                    crop = crop_dict[prediction[0]]
                    result = "{} is the best crop to be cultivated right there".format(crop)
                else:
                    result = "Sorry, we could not determine the best crop to be cultivated with the provided data."
                    
            except Exception as e:
                print(f"Error in prediction: {e}")
                result = "An error occurred during prediction. Please try again."
                
        except Exception as e:
            print(f"Error processing form data: {e}")
            result = f"An error occurred: {str(e)}"

    return render_template('recommendation.html', result=result, 
                         N=N, P=P, K=K, temp=temp, 
                         humidity=humidity, ph=ph, rainfall=rainfall)

if __name__ == "__main__":
    app.run(host="0.0.0.0", port=7860)