import streamlit as st
import pandas as pd
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
from textblob import TextBlob
from transformers import pipeline
import matplotlib.pyplot as plt
import os
from wordcloud import WordCloud

# Function to analyze sentiment using the custom Hugging Face pipeline
def analyze_sentiment_hf(text):
    hf_pipeline = pipeline("sentiment-analysis", "RohitBh/Sentimental_Analysis")
    if len(text) > 512:
        text = text[:511]
    sentiment_result = hf_pipeline(text)
    sentiment_label = sentiment_result[0]["label"]
    if sentiment_label == "LABEL_1":
        return "Positive"
    elif sentiment_label == "LABEL_0":
        return "Negative"
    else:
        return "Neutral"

# Function to analyze sentiment using VADER
def analyze_sentiment_vader(text):
    sentiment_analyzer = SentimentIntensityAnalyzer()
    sentiment_score = sentiment_analyzer.polarity_scores(text)["compound"]
    if sentiment_score > 0:
        return "Positive"
    elif sentiment_score == 0:
        return "Neutral"
    else:
        return "Negative"

# Function to analyze sentiment using TextBlob
def analyze_sentiment_textblob(text):
    sentiment_analysis = TextBlob(text)
    score = sentiment_analysis.sentiment.polarity
    if score > 0:
        return "Positive"
    elif score == 0:
        return "Neutral"
    else:
        return "Negative"

# Function to display DataFrame with sentiment
def display_results_dataframe(data_frame):
    st.write(data_frame)

# Function to display a pie chart of sentiment distribution
def create_pie_chart(data_frame, sentiment_column):
    sentiment_distribution = data_frame[sentiment_column].value_counts()
    fig, ax = plt.subplots()
    ax.pie(sentiment_distribution, labels=sentiment_distribution.index, autopct='%1.1f%%', startangle=90)
    ax.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.
    st.pyplot(fig)

# Function to display word cloud based on sentiment data
def create_word_cloud(sentiment_data):
    wordcloud_generator = WordCloud(width=800, height=400).generate(sentiment_data)
    fig, ax = plt.subplots(figsize=(10, 5))
    ax.imshow(wordcloud_generator, interpolation='bilinear')
    ax.axis('off')
    st.pyplot(fig)

# Main UI setup
st.set_page_config(page_title="Sentiment Analysis Tool", page_icon=":bar_chart:")
st.title("Sentiment Analysis Tool")

# Sidebar configuration for user input options
st.sidebar.title("Analysis Options")
input_type = st.sidebar.selectbox("Choose Input Type", ["Text Input", "CSV Upload"])
model_choice = st.sidebar.selectbox("Choose Sentiment Analysis Model", ["Hugging Face", "VADER", "TextBlob"])
display_type = st.sidebar.selectbox("Choose Display Type", ["DataFrame", "Pie Chart", "Word Cloud"])

# Process input based on user choice
if input_type == "Text Input":
    user_text = st.text_input("Enter text for sentiment analysis:")
    if st.button("Analyze Sentiment"):
        if user_text:
            # Analyzing sentiment based on selected model
            if model_choice == "Hugging Face":
                sentiment = analyze_sentiment_hf(user_text)
            elif model_choice == "VADER":
                sentiment = analyze_sentiment_vader(user_text)
            else:
                sentiment = analyze_sentiment_textblob(user_text)
            
            st.write("Detected Sentiment:", sentiment)
        else:
            st.warning("Please enter some text to analyze.")
elif input_type == "CSV Upload":
    uploaded_file = st.file_uploader("Upload CSV file for analysis", type="csv")
    if st.button("Start Analysis"):
        if uploaded_file is not None:
            data_frame = pd.read_csv(uploaded_file)
            # Assuming the CSV has a column named 'text' for analysis
            if 'text' in data_frame.columns:
                data_frame['Sentiment'] = data_frame['text'].apply(lambda x: analyze_sentiment_hf(x) if model_choice == "Hugging Face" else (analyze_sentiment_vader(x) if model_choice == "VADER" else analyze_sentiment_textblob(x)))
                
                if display_type == "DataFrame":
                    display_results_dataframe(data_frame)
                elif display_type == "Pie Chart":
                    create_pie_chart(data_frame, 'Sentiment')
                elif display_type == "Word Cloud":
                    combined_text = ' '.join(data_frame['text'])
                    create_word_cloud(combined_text)
            else:
                st.error("The uploaded CSV file must contain a 'text' column.")
        else:
            st.warning("Please upload a CSV file to proceed with analysis.")