import streamlit as st
import pandas as pd
from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer
import time
import matplotlib.pyplot as plt
import os

st.title("Sentiment Analysis App")
st.write("Upload a CSV or Excel file containing text data for sentiment analysis.")

# File uploader
uploaded_file = st.file_uploader("Upload a CSV or Excel file", type=["csv", "xlsx"])

# Load sentiment analysis model
try:
    sentiment_pipeline = pipeline(
        "sentiment-analysis", 
        model="distilbert-base-uncased-finetuned-sst-2-english"
    )
    st.success("Sentiment analysis model loaded successfully!")
except Exception as e:
    st.error(f"Error loading model: {e}")
    st.stop()

if uploaded_file:
    # Check file type
    if uploaded_file.name.endswith('.csv'):
        df = pd.read_csv(uploaded_file)
    elif uploaded_file.name.endswith('.xlsx'):
        df = pd.read_excel(uploaded_file)
    else:
        st.error("Unsupported file format.")
        st.stop()

    st.write("Data Preview:", df.head())

    # Check for 'text' column
    if 'text' not in df.columns:
        text_column = st.text_input("Enter the name of the column containing text values:")
        if text_column not in df.columns:
            st.error(f"Column '{text_column}' not found in the file.")
            st.stop()
        else:
            df.rename(columns={text_column: 'text'}, inplace=True)
    else:
        text_column = 'text'

    if st.button("Run Sentiment Analysis"):
        # Progress bar
        progress_bar = st.progress(0)

        sentiments = []
        for i, text in enumerate(df[text_column]):
            try:
                result = sentiment_pipeline(text)[0]
                sentiments.append(result['label'])
            except Exception as e:
                sentiments.append("Error")
                st.error(f"Error processing text at row {i + 1}: {e}")

            progress_bar.progress((i + 1) / len(df))
            time.sleep(0.1)  # Simulating processing time

        df['Sentiment'] = sentiments
        st.write("Sentiment Analysis Output:", df[['text', 'Sentiment']])

        # Pie chart
        sentiment_counts = df['Sentiment'].value_counts()
        fig, ax = plt.subplots()
        ax.pie(sentiment_counts, labels=sentiment_counts.index, autopct='%1.1f%%', startangle=90)
        ax.axis('equal')
        st.pyplot(fig)

        # Clear progress bar
        progress_bar.empty()