import gradio as gr
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score

# Load dataset
data = pd.read_csv('knn.csv')

# Separate features and target
x = data.drop('price_range', axis=1).values  # Features
y = data['price_range'].values  # Target

# Train-test split
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=0)

# Train the KNN classifier
knnClassifier = KNeighborsClassifier(n_neighbors=5, metric='minkowski', p=1)
knnClassifier.fit(x_train, y_train)

# Define the prediction function for Gradio
def predict_price_range(battery_power, blue, clock_speed, dual_sim, fc, four_g, int_memory, m_dep, mobile_wt, n_cores, pc, px_height, px_width, ram, sc_h, sc_w, talk_time, three_g, touch_screen, wifi):
    # Prepare the input for prediction
    input_data = np.array([[battery_power, blue, clock_speed, dual_sim, fc, four_g, int_memory, m_dep, mobile_wt, n_cores, pc, px_height, px_width, ram, sc_h, sc_w, talk_time, three_g, touch_screen, wifi]])
    
    # Make prediction using the trained KNN model
    prediction = knnClassifier.predict(input_data)
    
    # Return the predicted price range
    price_ranges = ['Low', 'Medium', 'High', 'Very High']  # Modify this according to your dataset
    return f"Predicted Price Range: {price_ranges[prediction[0]]}"

# Create Gradio interface with all the features as inputs using the updated Gradio components
interface = gr.Interface(
    fn=predict_price_range,  # Function that does the prediction
    inputs=[
        gr.Slider(500, 2000, step=1, label="Battery Power"),
        gr.Checkbox(label="Bluetooth"),  # 1 for Yes, 0 for No
        gr.Slider(0.5, 3.0, step=0.1, label="Clock Speed"),
        gr.Checkbox(label="Dual SIM"),  # 1 for Yes, 0 for No
        gr.Slider(0, 20, step=1, label="Front Camera (FC)"),
        gr.Checkbox(label="4G"),  # 1 for Yes, 0 for No
        gr.Slider(4, 64, step=1, label="Internal Memory (GB)"),
        gr.Slider(0.1, 1.0, step=0.1, label="Mobile Depth (cm)"),
        gr.Slider(80, 250, step=1, label="Mobile Weight (g)"),
        gr.Slider(1, 8, step=1, label="Number of Cores"),
        gr.Slider(0, 20, step=1, label="Primary Camera (PC)"),
        gr.Slider(0, 1960, step=1, label="Pixel Height"),
        gr.Slider(500, 2000, step=1, label="Pixel Width"),
        gr.Slider(500, 4000, step=1, label="RAM"),
        gr.Slider(5, 20, step=1, label="Screen Height (cm)"),
        gr.Slider(0, 18, step=1, label="Screen Width (cm)"),
        gr.Slider(2, 20, step=1, label="Talk Time (hours)"),
        gr.Checkbox(label="3G"),  # 1 for Yes, 0 for No
        gr.Checkbox(label="Touch Screen"),  # 1 for Yes, 0 for No
        gr.Checkbox(label="WiFi"),  # 1 for Yes, 0 for No
    ],
    outputs=gr.Textbox(label="Predicted Price Range"),  # Output format
    title="Mobile Price Range Prediction",
    description="Predict the price range of a mobile based on its specifications."
)

# Launch the Gradio app
interface.launch()