import streamlit as st
import requests
import pandas as pd
import time
import json
import re
import sys
import subprocess
from datetime import datetime, date, timedelta
from urllib.parse import urlencode
from typing import Dict, List, Optional
import google.generativeai as genai
import plotly.express as px

st.set_page_config(
    page_title="Steam App Reviews - Themes Analysis",
    page_icon="🎮",
    layout="wide",
    initial_sidebar_state="expanded"
)

# Custom CSS to improve UI
st.markdown("""
<style>
    .main-header {
        font-size: 2.5rem !important;
        color: #1e88e5;
    }
    .theme-card {
        background-color: #f5f5f5;
        border-radius: 10px;
        padding: 1.5rem;
        margin-bottom: 1rem;
        border-left: 5px solid #1e88e5;
    }
    .theme-title {
        font-size: 1.2rem;
        font-weight: bold;
        color: #1e88e5;
    }
    .theme-desc {
        color: #424242;
        margin: 0.5rem 0;
    }
    .theme-count {
        font-size: 0.9rem;
        color: #616161;
    }
    .sentiment-positive {
        background-color: #D5EAD8;
        color: #2E8B57;
        padding: 3px 8px;
        border-radius: 10px;
    }
    .sentiment-negative {
        background-color: #FFE4E1;
        color: #CD5C5C;
        padding: 3px 8px;
        border-radius: 10px;
    }
    .sentiment-mixed {
        background-color: #FFF8DC;
        color: #DAA520;
        padding: 3px 8px;
        border-radius: 10px;
    }
    .app-info {
        background-color: #f0f8ff;
        border-radius: 10px;
        padding: 1rem;
        margin-bottom: 1rem;
    }
</style>
""", unsafe_allow_html=True)

# Title and description
st.markdown('<h1 class="main-header">🎮 Steam App Reviews - Themes Analysis</h1>', unsafe_allow_html=True)
st.markdown("""
This tool analyzes user reviews for Steam games to identify common themes, sentiments, and feedback patterns.
Upload your Google Gemini API key, enter a Steam App ID, select a date range, and get valuable insights from user reviews.
""")

# Initialize session state variables
if 'reviews_data' not in st.session_state:
    st.session_state['reviews_data'] = None
if 'themes_df' not in st.session_state:
    st.session_state['themes_df'] = None
if 'app_info' not in st.session_state:
    st.session_state['app_info'] = None

# Sidebar inputs for user interaction
st.sidebar.header("User Input Parameters")

# User input for Google Gemini API key
api_key_input = st.sidebar.text_input(
    "Enter your Google Gemini API Key:",
    type="password",
    help="Your API key will be used to access the Google Gemini API for theme extraction.",
)

# Initialize Google Gemini client
gemini_client = None
if api_key_input:
    try:
        genai.configure(api_key=api_key_input)
        model = genai.GenerativeModel(model_name='gemini-1.5-pro')
        gemini_client = model
        st.sidebar.success("Gemini API connection established!")
    except Exception as e:
        st.sidebar.error(f"Error initializing Gemini API: {str(e)}")
else:
    st.sidebar.warning("Please enter your Google Gemini API Key to proceed.")

# User input for App ID
appid = st.sidebar.text_input(
    "Enter the Steam App ID:", 
    value="1782120",
    help="Find the App ID in the URL of the game's Steam page."
)

# Validate App ID
def is_valid_app_id(app_id: str) -> bool:
    if not app_id or not app_id.isdigit():
        return False
    return True

if not is_valid_app_id(appid):
    st.sidebar.error("Please enter a valid Steam App ID (numeric only).")

# Date input for selecting a range
st.sidebar.write("Select the date range for reviews:")
start_date = st.sidebar.date_input(
    "Start Date", 
    value=datetime.today() - timedelta(days=7)
)
end_date = st.sidebar.date_input(
    "End Date", 
    value=datetime.today()
)

# Validate date range
if start_date and end_date:
    today = date.today()
    
    # Check if end date is in the future
    if end_date > today:
        st.sidebar.error("Error: End date cannot be in the future.")
        st.stop()
    
    # Check if start date is after end date
    if start_date > end_date:
        st.sidebar.error("Error: Start date must be before end date.")
        st.stop()
    
    # Check if date range is too large
    date_range = (end_date - start_date).days
    if date_range > 365:
        st.sidebar.warning("Warning: Large date ranges may result in incomplete data due to Steam API limitations.")
    elif date_range < 0:
        st.sidebar.error("Error: Invalid date range selected.")
        st.stop()

# Maximum reviews to fetch
max_reviews = st.sidebar.slider(
    "Maximum reviews to fetch:",
    min_value=50,
    max_value=500,
    value=200,
    step=50,
    help="Higher values may take longer to process."
)

# Language filter
language_filter = st.sidebar.multiselect(
    "Filter by languages:",
    options=["english", "spanish", "french", "german", "italian", "russian", "all"],
    default=["english"],
    help="Select 'all' to include all languages or choose specific languages."
)

# Advanced options
advanced_options = st.sidebar.expander("Advanced Analysis Options")
with advanced_options:
    include_sentiment = st.checkbox(
        "Include sentiment analysis",
        value=True,
        help="Analyze the sentiment of each review and theme."
    )
    
    cluster_similar_themes = st.checkbox(
        "Cluster similar themes",
        value=True,
        help="Group themes that are semantically similar."
    )
    
    min_mention_threshold = st.slider(
        "Minimum reviews per theme:",
        min_value=1,
        max_value=10,
        value=2,
        help="Only show themes mentioned in at least this many reviews."
    )

# Function to fetch app information
@st.cache_data(ttl=3600, show_spinner=False)
def get_app_info(app_id: str) -> Optional[Dict]:
    """
    Fetches information about a Steam game using its App ID.
    """
    try:
        url = f"https://store.steampowered.com/api/appdetails?appids={app_id}"
        response = requests.get(url, timeout=10)
        response.raise_for_status()
        data = response.json()
        
        if data.get(app_id, {}).get('success', False):
            app_data = data[app_id]['data']
            return {
                'name': app_data.get('name', 'Unknown Game'),
                'header_image': app_data.get('header_image', ''),
                'release_date': app_data.get('release_date', {}).get('date', 'Unknown'),
                'developers': app_data.get('developers', ['Unknown']),
                'publishers': app_data.get('publishers', ['Unknown'])
            }
        return None
    except Exception as e:
        st.sidebar.error(f"Error fetching app info: {str(e)}")
        return None

# Function to fetch reviews
@st.cache_data(ttl=1800, show_spinner=False)
def fetch_reviews(app_id: str, start_timestamp: int, end_timestamp: int, 
                max_reviews: int = 1000, language_filter: List[str] = ["english"]) -> Optional[List]:
    """
    Fetches Steam reviews for the specified app within the given date range.
    Implements batch processing and caching for efficient handling of large volumes.
    """
    # Define the base API URL
    base_url = f"https://store.steampowered.com/appreviews/{app_id}?json=1"

    # Normalize language filter and handle 'all' case
    normalized_language_filter = [lang.lower() for lang in language_filter]
    use_all_languages = "all" in normalized_language_filter
    
    # Calculate day range dynamically based on start and end timestamps
    day_range = min(365, (end_timestamp - start_timestamp) // 86400 + 1)
    
    # Define initial API parameters with optimized batch size
    params = {
        "filter": "updated",  # Use 'updated' to get all reviews in date range
        "language": "all" if use_all_languages else ",".join(normalized_language_filter),
        "day_range": str(day_range),
        "review_type": "all",
        "purchase_type": "all",
        "num_per_page": "100",  # Maximum allowed by Steam API
        "cursor": "*",
        "filter_offtopic_activity": 0,
        "start_date": start_timestamp,
        "end_date": end_timestamp
    }
    
    # Initialize cache for review batches
    if 'review_cache' not in st.session_state:
        st.session_state.review_cache = {}
    cache_key = f"{app_id}_{start_timestamp}_{end_timestamp}_{language_filter}"
    
    # Check cache first
    if cache_key in st.session_state.review_cache:
        cached_reviews = st.session_state.review_cache[cache_key]
        if len(cached_reviews) >= max_reviews:
            return cached_reviews[:max_reviews]

    reviews_list = []
    request_count = 0
    max_requests = 100  # Increased limit for larger datasets
    retry_attempts = 3   # Number of retry attempts for failed requests
    batch_size = 100     # Size of each batch

    progress_bar = st.progress(0)
    status_text = st.empty()
    
    # Create a container for batch progress
    batch_container = st.empty()

    while True:
        # URL encode the cursor parameter
        params_encoded = params.copy()
        params_encoded["cursor"] = params["cursor"].replace("+", "%2B")

        # Construct the full URL with parameters
        url = base_url + "&" + urlencode(params_encoded)
        
        try:
            for attempt in range(retry_attempts):
                response = requests.get(url, timeout=15)
                response.raise_for_status()
                data = response.json()
                
                # Check if we have any reviews
                reviews = data.get('reviews')
                if not reviews:
                    status_text.warning("No reviews found for the specified date range and filters.")
                    return []
                
                # Process reviews
                for review in reviews:
                    timestamp = review.get("timestamp_created", 0)
                    review_language = review.get("language", "").lower()
                    
                    # Validate timestamp is within range
                    is_in_timerange = start_timestamp <= timestamp <= end_timestamp
                    
                    # Check language filter
                    is_valid_language = "all" in language_filter or review_language in [lang.lower() for lang in language_filter]
                    
                    if is_in_timerange and is_valid_language:
                        reviews_list.append(review)
                
                # Update progress
                progress = min(len(reviews_list) / max_reviews * 100, 100)
                progress_bar.progress(int(progress))
                status_text.text(f"Fetched {len(reviews_list)} reviews...")
                
                # Check if we've reached max reviews or earlier timestamp
                if len(reviews_list) >= max_reviews:
                    break
                if any(r.get("timestamp_created", 0) < start_timestamp for r in reviews):
                    break
                
                # Update cursor for next batch
                new_cursor = data.get("cursor")
                if new_cursor is None or params["cursor"] == new_cursor:
                    break
                params["cursor"] = new_cursor
                
                # Handle rate limiting
                if 'X-Rate-Limit-Remaining' in response.headers:
                    remaining_calls = int(response.headers['X-Rate-Limit-Remaining'])
                    time.sleep(0.5 if remaining_calls < 10 else 0.2)
                else:
                    time.sleep(0.2)
                
                # Update batch progress
                batch_container.text(f"Processing batch {request_count + 1} of {max_requests} (max)")
                
                # Check request limits
                request_count += 1
                if request_count >= max_requests:
                    status_text.warning("Reached maximum number of requests. Some reviews may not be fetched.")
                    break
                
                break  # Success - exit retry loop
                
        except requests.exceptions.RequestException as e:
            status_text.error(f"Steam API Error: {str(e)}")
            if attempt < retry_attempts - 1:
                time.sleep(1)  # Wait before retrying
                continue
            return None

        # Clean up progress indicators
        progress_bar.empty()
        status_text.empty()
        batch_container.empty()
        
        # Cache and return results
        st.session_state.review_cache[cache_key] = reviews_list
        return reviews_list
# Function to extract themes using Google Gemini 1.5 Pro
def extract_themes(df: pd.DataFrame,
                  include_sentiment: bool = True, 
                  cluster_similar_themes: bool = True,
                  min_mention_threshold: int = 2) -> Optional[pd.DataFrame]:
    """
    Uses Google Gemini 1.5 Pro to identify the most common themes in reviews.
    Implements batched processing and caching for large datasets.
    """
    if len(df) == 0:
        st.error("No reviews to analyze.")
        return None
        
    # Get counts of positive and negative reviews (if available)
    positive_count = 0 
    negative_count = 0
    
    if "Recommended" in df.columns:
        positive_count = df["Recommended"].sum()
        negative_count = len(df) - positive_count

    # Initialize theme cache
    if 'theme_cache' not in st.session_state:
        st.session_state.theme_cache = {}

    # Calculate cache key based on review content hash
    cache_key = hash(tuple(sorted(df['Review'].values)))
    
    # Check cache first
    if cache_key in st.session_state.theme_cache:
        return st.session_state.theme_cache[cache_key]

    # Process reviews in batches to handle large datasets
    batch_size = 200  # Optimal batch size for Gemini API
    total_batches = (len(df) + batch_size - 1) // batch_size
    
    all_themes = []
    progress_bar = st.progress(0)
    batch_status = st.empty()

    for batch_idx in range(total_batches):
        start_idx = batch_idx * batch_size
        end_idx = min(start_idx + batch_size, len(df))
        df_batch = df.iloc[start_idx:end_idx]
    
    # Combine reviews into a single string with IDs
    reviews_text = "\n\n".join([
        f"Review ID: {row['Review ID']}\nReview Text: {row['Review']}"
        for _, row in df.iterrows()
    ])
    
    # Prepare the prompt
    sentiment_instruction = "For each theme, analyze the sentiment (Positive, Negative, or Mixed)." if include_sentiment else ""
    clustering_instruction = "Cluster similar themes together." if cluster_similar_themes else ""
    
    # Fix the JSON template structure
    sentiment_field = '"Sentiment": "Positive/Negative/Mixed",' if include_sentiment else ""
    
    prompt = f"""
    Analyze these {len(df)} user reviews for a game with {positive_count} positive and {negative_count} negative reviews.
    
    Identify significant themes. {clustering_instruction}
    For each theme:
    1. Provide a concise, specific name
    2. Write a detailed description summarizing user feedback
    3. List the Review IDs where the theme is mentioned
    4. {sentiment_instruction}
    
    Only include themes mentioned in at least {min_mention_threshold} different reviews.
    
    Provide the output as a JSON array with the following structure:
    [
        {{
            "Theme": "theme_name",
            "Description": "detailed_description",
            "Review IDs": ["id1", "id2", ...],
            {sentiment_field}
        }},
        ...
    ]
    
    Reviews:
    {reviews_text}
    """
    
    # Call Google Gemini 1.5 Pro
    try:
        with st.spinner("Analyzing themes with Google Gemini 1.5 Pro..."):
            response = model.generate_content(prompt)
            
            # Extract text from the response
            if hasattr(response, 'text'):
                response_text = response.text
            elif hasattr(response, 'parts') and response.parts:
                response_text = response.parts[0].text
            else:
                response_text = str(response)
            
            # Clean and parse the response text
            # First try to extract JSON from code blocks
            json_pattern = r'```(?:json)?(.*?)```'
            json_matches = re.findall(json_pattern, response_text, re.DOTALL)
            
            if json_matches:
                # Use the first JSON block found
                json_str = json_matches[0].strip()
            else:
                # If no code blocks, try to use the entire response as JSON
                # Remove any markdown formatting or extra whitespace
                json_str = response_text.strip()
        
        # Parse the JSON output
        themes_data = json.loads(json_str)
        
        # Convert to DataFrame and add count column
        themes_df = pd.DataFrame(themes_data)
        themes_df["Count"] = themes_df["Review IDs"].apply(len)
        
        # Sort themes by count (descending)
        themes_df = themes_df.sort_values("Count", ascending=False).reset_index(drop=True)
        
        return themes_df

    except Exception as e:
        st.error(f"Error extracting themes: {str(e)}")
        st.error("Response from Gemini API:")
        if 'response' in locals():
            try:
                if hasattr(response, 'text'):
                    error_text = response.text
                elif hasattr(response, 'parts') and response.parts:
                    error_text = response.parts[0].text
                else:
                    error_text = str(response)
                st.error(error_text)
            except Exception as e:
                st.error(f"Error displaying response: {str(e)}")
        return None

# Function to create visualizations
def create_visualizations(themes_df: pd.DataFrame, reviews_df: pd.DataFrame):
    """
    Creates visualizations for the theme analysis.
    """
    col1, col2 = st.columns(2)
    
    # Theme distribution chart
    with col1:
        theme_counts = themes_df[["Theme", "Count"]]
        fig = px.bar(
            theme_counts,
            x="Count", y="Theme", orientation="h", 
            title="Theme Distribution",
  
        )
        fig.update_layout(height=400)
        st.plotly_chart(fig, use_container_width=True)
    
    # Sentiment analysis chart (if available)
    with col2:
        if "Sentiment" in themes_df.columns:
            sentiment_counts = themes_df["Sentiment"].value_counts().reset_index()
            sentiment_counts.columns = ["Sentiment", "Count"]
            fig = px.pie(
                sentiment_counts,
                values="Count", names="Sentiment",
                title="Theme Sentiment Distribution",
                color="Sentiment",
                color_discrete_map={"Positive": "#2E8B57", "Negative": "#CD5C5C", "Mixed": "#DAA520"},
            )
            fig.update_layout(height=400)
            st.plotly_chart(fig, use_container_width=True)
    
    # Review timeline (if timestamp available)
    if "Timestamp" in reviews_df.columns:
        # Convert timestamp to datetime
        reviews_df["Date"] = pd.to_datetime(reviews_df["Timestamp"], unit='s')
        
        # Group by date and count
        reviews_by_date = reviews_df.groupby(reviews_df["Date"].dt.date).size().reset_index()
        reviews_by_date.columns = ["Date", "Count"]
        
        # Create timeline chart
        fig = px.line(
            reviews_by_date,
            x="Date", y="Count",
            title="Reviews Timeline",
            markers=True
        )
        st.plotly_chart(fig, use_container_width=True)

# Validate inputs before processing
if start_date > end_date:
    st.error("Error: End date must fall after start date.")
elif not api_key_input:
    st.info("Please input your Google Gemini API Key to proceed.")
elif not is_valid_app_id(appid):
    st.error("Please enter a valid Steam App ID.")
else:
    # Fetch app info
    if st.session_state['app_info'] is None or st.session_state.get('current_appid') != appid:
        st.session_state['app_info'] = get_app_info(appid)
        st.session_state['current_appid'] = appid
    
    # Display app info if available
    if st.session_state['app_info']:
        app_info = st.session_state['app_info']
        col1, col2 = st.columns([1, 3])
        
        with col1:
            st.image(app_info['header_image'], width=200)
        
        with col2:
            st.markdown(f"""
            <div class='app-info'>
                <h2>{app_info['name']}</h2>
                <p><strong>Release Date:</strong> {app_info['release_date']}</p>
                <p><strong>Developers:</strong> {', '.join(app_info['developers'])}</p>
                <p><strong>Publishers:</strong> {', '.join(app_info['publishers'])}</p>
            </div>
            """, unsafe_allow_html=True)
    
    # Fetch reviews button
    if st.button("Fetch and Analyze Reviews", type="primary"):
        # Convert dates to timestamps
        start_timestamp = int(time.mktime(start_date.timetuple()))
        end_timestamp = int(time.mktime((end_date + timedelta(days=1)).timetuple())) - 1  # Include the entire end date

        # Fetch the reviews
        with st.spinner("Fetching reviews from Steam..."):
            reviews_data = fetch_reviews(
                appid, 
                start_timestamp, 
                end_timestamp, 
                max_reviews=max_reviews,
                language_filter=language_filter
            )
            st.session_state['reviews_data'] = reviews_data

        # Check if reviews were fetched
        if reviews_data:
            st.success(f"Fetched {len(reviews_data)} reviews from App ID {appid}.")

            # Create a DataFrame from the review data
            df = pd.DataFrame(
                [
                    {
                        "Review ID": str(review.get("recommendationid")),
                        "Author SteamID": review.get("author", {}).get("steamid"),
                        "Language": review.get("language"),
                        "Review": review.get("review"),
                        "Recommended": review.get("voted_up", False),
                        "Votes Helpful": review.get("votes_up", 0),
                        "Timestamp": review.get("timestamp_created", 0),
                        "Posted On": datetime.fromtimestamp(
                            review.get("timestamp_created", 0)
                        ).strftime("%Y-%m-%d %H:%M:%S"),
                    }
                    for review in reviews_data
                ]
            )

            # Extract themes using Google Gemini 1.5 Pro
            themes_df = extract_themes(
                df, 
                include_sentiment=include_sentiment,
                cluster_similar_themes=cluster_similar_themes,
                min_mention_threshold=min_mention_threshold
            )
            st.session_state['themes_df'] = themes_df

            if themes_df is not None:
                # Show summary statistics
                col1, col2, col3, col4 = st.columns(4)
                with col1:
                    st.metric("Total Reviews", len(df))
                with col2:
                    positive_count = df["Recommended"].sum()
                    positive_percent = (positive_count / len(df)) * 100 if len(df) > 0 else 0
                    st.metric("Positive Reviews", f"{positive_count} ({positive_percent:.1f}%)")
                with col3:
                    negative_count = len(df) - positive_count
                    negative_percent = (negative_count / len(df)) * 100 if len(df) > 0 else 0
                    st.metric("Negative Reviews", f"{negative_count} ({negative_percent:.1f}%)")
                with col4:
                    st.metric("Themes Identified", len(themes_df))
                
                # Create visualizations
                create_visualizations(themes_df, df)
                
                # Show themes analysis
                st.markdown("## 📊 Theme Analysis")
                st.dataframe(themes_df)
                
                # Display detailed theme information
                st.markdown("## 🔍 Detailed Theme Analysis")
                
                for index, row in themes_df.iterrows():
                    theme = row["Theme"]
                    description = row["Description"]
                    review_ids = row["Review IDs"]
                    count = row["Count"]
                    sentiment = row.get("Sentiment", "Not analyzed")
                    
                    # Create a sentiment badge with appropriate styling
                    sentiment_class = ""
                    if sentiment == "Positive":
                        sentiment_class = "sentiment-positive"
                    elif sentiment == "Negative":
                        sentiment_class = "sentiment-negative"
                    elif sentiment == "Mixed":
                        sentiment_class = "sentiment-mixed"
                    
                    # Display theme card with enhanced formatting
                    sentiment_html = f'<span class="{sentiment_class}">{sentiment}</span>' if sentiment != "Not analyzed" else ""
                    
                    st.markdown(f"""
                    <div class="theme-card">
                        <div class="theme-title">{theme} {sentiment_html}</div>
                        <p class="theme-desc">{description}</p>
                        <div class="theme-count">Mentioned in {count} reviews</div>
                    </div>
                    """, unsafe_allow_html=True)
                    
                    with st.expander(f"View reviews mentioning '{theme}'"):
                        # Get the reviews that mention the theme
                        try:
                            reviews_with_theme = df[df["Review ID"].isin(review_ids)][["Review ID", "Review", "Posted On", "Recommended"]]
                            st.dataframe(reviews_with_theme, use_container_width=True)
                        except Exception as e:
                            st.error(f"Error displaying reviews for theme '{theme}': {str(e)}")
                
                # Export options
                st.markdown("## 📥 Export Results")
                col1, col2 = st.columns(2)
                
                with col1:
                    # Export reviews as CSV
                    reviews_csv = df.to_csv(index=False).encode('utf-8')
                    st.download_button(
                        label="Download Reviews CSV",
                        data=reviews_csv,
                        file_name=f"steam_reviews_{appid}_{start_date}_to_{end_date}.csv",
                        mime="text/csv"
                    )
                
                with col2:
                    # Export themes as CSV
                    themes_csv = themes_df.to_csv(index=False).encode('utf-8')
                    st.download_button(
                        label="Download Themes Analysis CSV",
                        data=themes_csv,
                        file_name=f"steam_themes_{appid}_{start_date}_to_{end_date}.csv",
                        mime="text/csv"
                    )
            else:
                st.warning("Failed to extract themes. Please try again or adjust parameters.")
        else:
            st.warning("No reviews found for the specified date range and filters.")

# Display the raw reviews data if available
if st.session_state['reviews_data'] is not None:
    with st.expander("View Raw Reviews Data"):
        reviews_df = pd.DataFrame(
            [
                {
                    "Review ID": str(review.get("recommendationid")),
                    "Author SteamID": review.get("author", {}).get("steamid"),
                    "Language": review.get("language"),
                    "Review": review.get("review"),
                    "Recommended": review.get("voted_up", False),
                    "Votes Helpful": review.get("votes_up", 0),
                    "Posted On": datetime.fromtimestamp(
                        review.get("timestamp_created", 0)
                    ).strftime("%Y-%m-%d %H:%M:%S"),
                }
                for review in st.session_state['reviews_data']
            ]
        )
        st.dataframe(reviews_df, use_container_width=True)