import streamlit as st
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from datetime import datetime, timedelta
from sklearn.preprocessing import MinMaxScaler
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from transformers import pipeline
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
import shap
import praw
from googleapiclient.discovery import build
import warnings
warnings.filterwarnings('ignore')

np.random.seed(42)

st.set_page_config(page_title="Sentiment Pulse", layout="wide")
st.markdown("<h1 style='text-align: center; color: #7B68EE;'>Sentiment Pulse: Multi-Platform Analysis</h1>", unsafe_allow_html=True)

# API credentials
REDDIT_CLIENT_ID = "S7pTXhj5JDFGDb3-_zrJEA"
REDDIT_CLIENT_SECRET = "QP3NYN4lrAKVLrBamzLGrpFywiVg8w"
REDDIT_USER_AGENT = "SoundaryaR_Bot/1.0"
YOUTUBE_API_KEY = "AIzaSyAChqXPaiNE9hKhApkgjgonzdgiCCOo"

reddit = praw.Reddit(client_id=REDDIT_CLIENT_ID, client_secret=REDDIT_CLIENT_SECRET, user_agent=REDDIT_USER_AGENT)
youtube = build('youtube', 'v3', developerKey=YOUTUBE_API_KEY)
bert_classifier = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
vader_analyzer = SentimentIntensityAnalyzer()

@st.cache_data
def load_twitter_data():
    try:
        df = pd.read_csv("twitter_dataset.csv", encoding='latin-1', 
                         names=['sentiment', 'id', 'date', 'query', 'user', 'text'])
        df['date'] = pd.to_datetime(df['date'], errors='coerce')
        df['sentiment'] = df['sentiment'].map({0: 'negative', 4: 'positive'})
        return df.sample(10000, random_state=42)
    except FileNotFoundError:
        st.error("twitter_dataset.csv not found. Please ensure the file is in the working directory.")
        return pd.DataFrame()

def fetch_reddit_data(keyword):
    try:
        subreddit = reddit.subreddit("all")
        posts = subreddit.search(keyword, limit=100)
        return pd.DataFrame([{'date': datetime.fromtimestamp(post.created_utc), 'text': post.title + " " + post.selftext}iety for post in posts])
    except Exception as e:
        st.error(f"Error fetching Reddit data: {e}")
        return pd.DataFrame()

def fetch_youtube_data(keyword):
    try:
        request = youtube.search().list(q=keyword, part="snippet", maxResults=50, type="video")
        response = request.execute()
        return pd.DataFrame([{
            'date': datetime.strptime(item['snippet']['publishedAt'], "%Y-%m-%dT%H:%M:%SZ"),
            'text': item['snippet']['title'] + " " + item['snippet']['description']
        } for item in response['items']])
    except Exception as e:
        st.error(f"Error fetching YouTube data: {e}")
        return pd.DataFrame()

def get_bert_sentiment(text):
    try:
        result = bert_classifier(text[:512])[0]
        return 1 if result['label'] == 'POSITIVE' else 0, result['score']
    except:
        return 0, 0.5

def get_vader_sentiment(text):
    score = vader_analyzer.polarity_scores(text)['compound']
    return 1 if score > 0 else 0, score

def combined_sentiment(text):
    bert_label, bert_score = get_bert_sentiment(text)
    vader_label, vader_score = get_vader_sentiment(text)
    avg_score = (bert_score + abs(vader_score)) / 2
    return 1 if avg_score > 0.5 else 0, avg_score

st.sidebar.title("Keyword Search")
keyword = st.sidebar.text_input("Enter a keyword (e.g., 'happy')", value="happy")

twitter_df = load_twitter_data()
twitter_filtered = twitter_df[twitter_df['text'].str.contains(keyword, case=False, na=False)]
reddit_df = fetch_reddit_data(keyword)
youtube_df = fetch_youtube_data(keyword)

platforms = {'Twitter': twitter_filtered, 'Reddit': reddit_df, 'YouTube': youtube_df}
valid_platforms = {k: v for k, v in platforms.items() if not v.empty}

if not valid_platforms:
    st.error(f"Error: '{keyword}' is not a valid keyword. No matching data found across Twitter, Reddit, or YouTube.")
else:
    for platform, df in valid_platforms.items():
        st.subheader(f"{platform} Analysis for '{keyword}'")
        st.write(f"{platform} Data Preview:", df.head())

        with st.spinner(f"Analyzing {platform} sentiments..."):
            df['bert_sentiment'], df['bert_score'] = zip(*df['text'].apply(get_bert_sentiment))
            df['vader_sentiment'], df['vader_score'] = zip(*df['text'].apply(get_vader_sentiment))
            df['combined_sentiment'], df['combined_score'] = zip(*df['text'].apply(combined_sentiment))

        st.write(df[['text', 'combined_sentiment', 'combined_score']].head())

        daily_sentiment = df.groupby(df['date'].dt.date)['combined_score'].mean().reset_index()
        daily_sentiment['date'] = pd.to_datetime(daily_sentiment['date'])
        daily_sentiment['tweet_count'] = df.groupby(df['date'].dt.date).size().values

        if len(daily_sentiment) < 2:
            st.warning(f"Not enough {platform} data for prediction.")
            fig, ax = plt.subplots()
            ax.plot(daily_sentiment['date'], daily_sentiment['combined_score'], label='Historical')
            ax.legend()
            st.pyplot(fig)
            continue

        scaler = MinMaxScaler()
        daily_sentiment['scaled_score'] = scaler.fit_transform(daily_sentiment[['combined_score']])

        # Prepare features: use lagged sentiment scores and tweet counts
        X = pd.DataFrame({
            'lag1_score': daily_sentiment['scaled_score'].shift(1),
            'tweet_count': daily_sentiment['tweet_count']
        }).dropna()
        y = daily_sentiment['scaled_score'][1:]  # Align with lagged features

        if len(X) < 5:  # Minimum data for meaningful split
            st.warning(f"Not enough {platform} data points for prediction after lagging.")
            fig, ax = plt.subplots()
            ax.plot(daily_sentiment['date'], daily_sentiment['combined_score'], label='Historical')
            ax.legend()
            st.pyplot(fig)
            continue

        # Split data for validation
        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

        # Train Logistic Regression (using regression mode with continuous output)
        lr_model = LogisticRegression(max_iter=1000)
        lr_model.fit(X_train, (y_train > 0.5).astype(int))  # Binary classification for validation
        lr_pred_train = lr_model.predict_proba(X_train)[:, 1]
        lr_mse = mean_squared_error(y_train, lr_pred_train)

        # Train Random Forest
        rf_model = RandomForestRegressor(n_estimators=100, random_state=42)
        rf_model.fit(X_train, y_train)
        rf_pred_train = rf_model.predict(X_train)
        rf_mse = mean_squared_error(y_train, rf_pred_train)

        # Weighted ensemble based on inverse MSE
        total_mse = lr_mse + rf_mse
        lr_weight = (1 - lr_mse / total_mse) if total_mse > 0 else 0.5
        rf_weight = (1 - rf_mse / total_mse) if total_mse > 0 else 0.5

        # Predict 30 days into the future
        last_data = X.iloc[-1:].copy()
        predictions = []
        future_dates = [daily_sentiment['date'].iloc[-1] + timedelta(days=i) for i in range(1, 31)]

        for _ in range(30):
            lr_pred = lr_model.predict_proba(last_data)[:, 1][0]
            rf_pred = rf_model.predict(last_data)[0]
            ensemble_pred = lr_weight * lr_pred + rf_weight * rf_pred
            predictions.append(ensemble_pred)
            last_data['lag1_score'] = ensemble_pred  # Update lag for next prediction

        predictions = scaler.inverse_transform(np.array(predictions).reshape(-1, 1)).flatten()

        st.subheader(f"{platform} 30-Day Prediction (Ensemble: LR + RF)")
        fig, ax = plt.subplots()
        ax.plot(daily_sentiment['date'], daily_sentiment['combined_score'], 'g-', label='Historical')
        ax.plot(future_dates, predictions, 'b--', label=f'Predicted (LR: {lr_weight:.2f}, RF: {rf_weight:.2f})')
        ax.legend()
        st.pyplot(fig)

        st.subheader(f"{platform} Random Forest SHAP")
        explainer = shap.TreeExplainer(rf_model)
        shap_values = explainer.shap_values(X)
        shap.summary_plot(shap_values, X, show=False)
        st.pyplot(plt.gcf())