import gradio as gr
import sqlite3
import bcrypt
import pandas as pd
import numpy as np
import joblib
from sklearn.ensemble import RandomForestRegressor
from sklearn.preprocessing import OneHotEncoder, LabelEncoder, StandardScaler
from sklearn.compose import ColumnTransformer
from sklearn.neighbors import NearestNeighbors
import plotly.express as px
import time
import threading
import random
import requests
import os  # Import the os module to access environment variables

# Hugging Face API configuration
HUGGINGFACE_API_URL =  "https://api-inference.huggingface.co/models/google/flan-t5-large"

HUGGINGFACE_API_KEY = os.environ["HUGGINGFACE_API_KEY"]  # Access the API key from environment variables
def huggingface_chatbot(user_input):
    try:
        headers = {
            "Authorization": f"Bearer {HUGGINGFACE_API_KEY}",
            "Content-Type": "application/json"
        }
        data = {
            "inputs": f"Answer the following question clearly and concisely:\n{user_input}",
            "parameters": {
                "max_length": 150,
                "temperature": 0.3  # Less randomness
            }
        }
        response = requests.post(HUGGINGFACE_API_URL, headers=headers, json=data)
        response.raise_for_status()
        return response.json()[0]["generated_text"]
    except Exception as e:
        return f"Error: {str(e)}"
def huggingface_chatbot(user_input):
    try:
        headers = {
            "Authorization": f"Bearer {HUGGINGFACE_API_KEY}",
            "Content-Type": "application/json"
        }
        data = {
            "inputs": f"Answer the following question clearly and concisely:\n{user_input}",
            "parameters": {
                "max_length": 150,
                "temperature": 0.3  # Less randomness
            }
        }
        response = requests.post(HUGGINGFACE_API_URL, headers=headers, json=data)
        response.raise_for_status()
        return response.json()[0]["generated_text"]
    except Exception as e:
        return f"Error: {str(e)}"

# Hugging Face Chatbot Function
def huggingface_chatbot(user_input):
    try:
        headers = {
            "Authorization": f"Bearer {HUGGINGFACE_API_KEY}",
            "Content-Type": "application/json"
        }
        data = {
            "inputs": user_input,
            "parameters": {
                "max_length": 100,  # Adjust as needed
                "temperature": 0.7  # Adjust as needed
            }
        }
        response = requests.post(HUGGINGFACE_API_URL, headers=headers, json=data)
        response.raise_for_status()  # Raise an error for bad status codes
        return response.json()[0]["generated_text"]
    except Exception as e:
        return f"Error: {str(e)}"

# Database setup for user authentication
def init_db():
    conn = sqlite3.connect("users.db")
    cursor = conn.cursor()
    cursor.execute("""
        CREATE TABLE IF NOT EXISTS users (
            id INTEGER PRIMARY KEY AUTOINCREMENT,
            username TEXT UNIQUE,
            password TEXT
        )
    """)
    conn.commit()
    conn.close()

init_db()

def register(username, password):
    conn = sqlite3.connect("users.db")
    cursor = conn.cursor()
    hashed_pw = bcrypt.hashpw(password.encode(), bcrypt.gensalt())
    try:
        cursor.execute("INSERT INTO users (username, password) VALUES (?, ?)", (username, hashed_pw))
        conn.commit()
        return "✅ Registration Successful! You can now log in."
    except sqlite3.IntegrityError:
        return "⚠️ Username already exists. Try another."
    finally:
        conn.close()

def login(username, password):
    conn = sqlite3.connect("users.db")
    cursor = conn.cursor()
    cursor.execute("SELECT password FROM users WHERE username = ?", (username,))
    result = cursor.fetchone()
    conn.close()
    if result and bcrypt.checkpw(password.encode(), result[0]):
        return "✅ Login Successful! Welcome to the marketplace."
    else:
        return "❌ Incorrect username or password. Try again."

# Load dataset for product lifecycle prediction
df_lifecycle = pd.read_csv("ecommerce_product_dataset.csv")  # Update this path with the correct one

# Preprocessing for product lifecycle prediction
categorical_features_lifecycle = ['Category']
numeric_features_lifecycle = ['Price', 'Rating', 'NumReviews', 'StockQuantity', 'Discount']

preprocessor_lifecycle = ColumnTransformer([
    ('cat', OneHotEncoder(handle_unknown='ignore'), categorical_features_lifecycle),
    ('num', 'passthrough', numeric_features_lifecycle)
])

# Fit the preprocessor on training data
X_lifecycle = df_lifecycle[["Category", "ProductName", "Price", "Rating", "NumReviews", "StockQuantity", "Discount"]]
y_lifecycle = df_lifecycle["Sales"]  # Target variable

X_transformed_lifecycle = preprocessor_lifecycle.fit_transform(X_lifecycle)

# Train the model
model_lifecycle = RandomForestRegressor(n_estimators=100, random_state=42)
model_lifecycle.fit(X_transformed_lifecycle, y_lifecycle)

# Save the fitted preprocessor & model
joblib.dump(preprocessor_lifecycle, "preprocessor_lifecycle.pkl")
joblib.dump(model_lifecycle, "product_lifecycle_model.pkl")

# Load trained model and fitted preprocessor
model_lifecycle = joblib.load("product_lifecycle_model.pkl")
preprocessor_lifecycle = joblib.load("preprocessor_lifecycle.pkl")

def preprocess_input_lifecycle(Category, ProductName, Price, Rating, NumReviews, StockQuantity, Discount):
    input_df = pd.DataFrame([[Category, ProductName, Price, Rating, NumReviews, StockQuantity, Discount]],
                            columns=["Category", "ProductName", "Price", "Rating", "NumReviews", "StockQuantity", "Discount"])
    input_processed = preprocessor_lifecycle.transform(input_df)
    return input_processed

def predict_lifecycle(Category, ProductName, Price, Rating, NumReviews, StockQuantity, Discount):
    try:
        input_data = preprocess_input_lifecycle(Category, ProductName, Price, Rating, NumReviews, StockQuantity, Discount)
        prediction = model_lifecycle.predict(input_data)[0]
        return f"Predicted Product Lifecycle: {round(prediction, 2)} years"
    except Exception as e:
        return f"Error: {str(e)}"

# Load dataset for dynamic pricing
df_pricing = pd.read_csv("dynamic_pricing_data_5000.csv")  # Update this path with the correct one

# Encode categorical variables for dynamic pricing
label_encoders = {}
for col in ["Product Name", "Category", "Demand", "Season"]:
    le = LabelEncoder()
    df_pricing[col] = le.fit_transform(df_pricing[col])
    label_encoders[col] = le

# Scale numerical features for dynamic pricing
scaler = StandardScaler()
num_cols = ["Base Price", "Competitor Price", "Stock", "Reviews", "Rating", "Discount"]
df_pricing[num_cols] = scaler.fit_transform(df_pricing[num_cols])

# Save label encoders and scaler
joblib.dump(label_encoders, "label_encoders.pkl")
joblib.dump(scaler, "scaler.pkl")

# Train model for dynamic pricing
X_pricing = df_pricing.drop(columns=["Final Price"])
y_pricing = df_pricing["Final Price"]

model_pricing = RandomForestRegressor(n_estimators=100, random_state=42)
model_pricing.fit(X_pricing, y_pricing)

# Save the trained model
joblib.dump(model_pricing, "dynamic_pricing_model.pkl")

# Load trained model, scaler, and label encoders
model_pricing = joblib.load("dynamic_pricing_model.pkl")
scaler = joblib.load("scaler.pkl")
label_encoders = joblib.load("label_encoders.pkl")

def predict_price(product_name, category, base_price, competitor_price, demand, stock, reviews, rating, season, discount):
    # Encode categorical features
    category = label_encoders["Category"].transform([category])[0]
    demand = label_encoders["Demand"].transform([demand])[0]
    season = label_encoders["Season"].transform([season])[0]
    product_name = label_encoders["Product Name"].transform([product_name])[0]

    # Scale numerical features
    features = np.array([base_price, competitor_price, stock, reviews, rating, discount]).reshape(1, -1)
    features = scaler.transform(features)

    # Combine features
    final_features = np.concatenate((features.flatten(), [category, demand, season, product_name])).reshape(1, -1)

    # Predict
    predicted_price = model_pricing.predict(final_features)[0]
    return f"Optimal Price: ₹{round(predicted_price, 2)}"

# Load dataset for product recommendation
df_recommendation = pd.read_csv("synthetic_product_data_5000.csv")  # Update this path with the correct one

# Preprocessing for product recommendation
categorical_features_recommendation = ['product_condition', 'category']
numeric_features_recommendation = ['price']

preprocessor_recommendation = ColumnTransformer(
    transformers=[
        ('cat', OneHotEncoder(), categorical_features_recommendation),
        ('num', 'passthrough', numeric_features_recommendation)
    ])

product_features = preprocessor_recommendation.fit_transform(df_recommendation[['product_condition', 'price', 'category']])

# Fit NearestNeighbors model
knn = NearestNeighbors(n_neighbors=5)
knn.fit(product_features)

def recommend_products(category):
    filtered_df = df_recommendation[df_recommendation['category'] == category]
    if filtered_df.empty:
        return "No products found in this category."
    random_product = random.choice(filtered_df.index)
    product = product_features[random_product].reshape(1, -1)
    _, indices = knn.kneighbors(product)
    recommended = df_recommendation.iloc[indices[0]]
    recommended = recommended[recommended['category'] == category]
    return recommended[['product_id', 'product_condition', 'price', 'category']]

# Circular Economy Analytics Dashboard
def load_data():
    return pd.read_csv("synthetic_marketplace_data_2000.csv")

def update_live_data():
    df = load_data()
    new_entry = {
        "Category": np.random.choice(["Electronics", "Plastic", "Metal", "Wood", "Composite"]),
        "LifecycleYears": round(np.random.uniform(1, 20), 2),
        "Price": round(np.random.uniform(10, 500), 2),
        "NumReviews": np.random.randint(0, 1000)
    }
    df = df.append(new_entry, ignore_index=True)
    df.to_csv("synthetic_marketplace_data_2000.csv", index=False)

def generate_dashboard():
    df = load_data()
    lifecycle_fig = px.bar(df.groupby('Category')['LifecycleYears'].mean().reset_index(),
                            x='Category', y='LifecycleYears', title='Average Product Lifecycle by Category')
    price_trend_fig = px.line(df.groupby('Category')['Price'].mean().reset_index(),
                               x='Category', y='Price', title='Average Price Trends by Category')
    engagement_fig = px.bar(df.groupby('Category')['NumReviews'].sum().reset_index(),
                             x='Category', y='NumReviews', title='Total User Reviews per Category')
    df['Sustainability Score'] = np.random.uniform(0, 100, len(df))
    sustainability_fig = px.scatter(df, x='Price', y='Sustainability Score', color='Category',
                                    title='Sustainability Score vs. Product Price')
    return lifecycle_fig, price_trend_fig, engagement_fig, sustainability_fig

# Gradio Interfaces
with gr.Blocks() as app:
    # Add a logo or banner image
    gr.Markdown("""
    <div style="text-align: center;">
        <img src="https://via.placeholder.com/800x200.png?text=Circular+Economy+Marketplace" alt="Banner" style="width: 100%; max-width: 800px;">
    </div>
    """)
    gr.Markdown("# 🔐 Circular Economy Marketplace")
    
    # Login/Register Tab
    with gr.Tab("Login/Register"):
        with gr.Tab("Register"):
            reg_username = gr.Textbox(label="Username")
            reg_password = gr.Textbox(label="Password", type="password")
            reg_btn = gr.Button("Register")
            reg_output = gr.Textbox()
            reg_btn.click(register, inputs=[reg_username, reg_password], outputs=reg_output)
        with gr.Tab("Login"):
            log_username = gr.Textbox(label="Username")
            log_password = gr.Textbox(label="Password", type="password")
            log_btn = gr.Button("Login")
            log_output = gr.Textbox()
            log_btn.click(login, inputs=[log_username, log_password], outputs=log_output)

    # Product Lifecycle Prediction Tab
    with gr.Tab("Product Lifecycle Prediction"):
        lifecycle_inputs = [
            gr.Dropdown(["Plastic", "Metal", "Wood", "Composite", "Electronics"], label="Category"),
            gr.Textbox(label="Product Name"),
            gr.Number(label="Price"),
            gr.Number(label="Rating"),
            gr.Number(label="NumReviews"),
            gr.Number(label="StockQuantity"),
            gr.Number(label="Discount")
        ]
        lifecycle_output = gr.Textbox(label="Prediction")
        lifecycle_btn = gr.Button("Predict")
        lifecycle_btn.click(predict_lifecycle, inputs=lifecycle_inputs, outputs=lifecycle_output)

    # Dynamic Pricing Tab
    with gr.Tab("Dynamic Pricing"):
        pricing_inputs = [
            gr.Dropdown(["iPhone 13", "Nike Shoes", "Samsung TV", "Adidas Jacket", "Dell Laptop", "Sony Headphones", "Apple Watch",
                         "LG Refrigerator", "HP Printer", "Bose Speaker"], label="Product Name"),
            gr.Dropdown(["Electronics", "Fashion", "Home Appliances"], label="Category"),
            gr.Number(label="Base Price"),
            gr.Number(label="Competitor Price"),
            gr.Dropdown(["Low", "Medium", "High"], label="Demand"),
            gr.Number(label="Stock"),
            gr.Number(label="Reviews"),
            gr.Number(label="Rating"),
            gr.Dropdown(["Holiday", "Summer", "Winter", "Off-season"], label="Season"),
            gr.Number(label="Discount (%)")
        ]
        pricing_output = gr.Textbox(label="Predicted Price")
        pricing_btn = gr.Button("Predict")
        pricing_btn.click(predict_price, inputs=pricing_inputs, outputs=pricing_output)

    # Product Recommendation Tab
    with gr.Tab("Product Recommendation"):
        recommendation_input = gr.Dropdown(choices=df_recommendation['category'].unique().tolist(), label="Select Product Category")
        recommendation_output = gr.Dataframe()
        recommendation_btn = gr.Button("Recommend")
        recommendation_btn.click(recommend_products, inputs=recommendation_input, outputs=recommendation_output)

    # Circular Economy Analytics Tab
    with gr.Tab("Circular Economy Analytics"):
        dashboard_outputs = [
            gr.Plot(label="Product Lifecycle Analytics"),
            gr.Plot(label="Dynamic Pricing Insights"),
            gr.Plot(label="User Engagement Trends"),
            gr.Plot(label="Sustainability & Recycling Insights")
        ]
        dashboard_btn = gr.Button("Generate Dashboard")
        dashboard_btn.click(generate_dashboard, inputs=[], outputs=dashboard_outputs)

    # AI Chatbot Tab
    with gr.Tab("AI Chatbot"):
        gr.Markdown("""
        <div style="text-align: center;">
            <img src="https://via.placeholder.com/400x200.png?text=AI+Chatbot" alt="Chatbot" style="width: 100%; max-width: 400px;">
        </div>
        """)
        chatbot_input = gr.Textbox(label="Ask me anything about circular economy, product lifecycle, dynamic pricing, and recommendations!")
        chatbot_output = gr.Textbox(label="AI Response")
        chatbot_btn = gr.Button("Ask")
        chatbot_btn.click(huggingface_chatbot, inputs=chatbot_input, outputs=chatbot_output)

# Simulate real-time data updates
def live_update():
    while True:
        update_live_data()
        time.sleep(5)

threading.Thread(target=live_update, daemon=True).start()

# Launch the app
app.launch()