import pandas as pd
import matplotlib.pyplot as plt
from transformers import pipeline
import gradio as gr
import os

# Define the default file path for the example reviews.txt
DEFAULT_FILE_PATH = "reviews.txt"

# Initialize the sentiment analyzer pipeline
sentiment_Analyzer = pipeline("text-classification", model="distilbert/distilbert-base-uncased-finetuned-sst-2-english")

# Function to read reviews from a text file and convert to a pandas DataFrame
def read_reviews_to_dataframe(file_path):
    reviews = []
    with open(file_path, 'r', encoding='utf-8') as file:
        for line in file:
            reviews.append(line.strip())
    df = pd.DataFrame(reviews, columns=['Review'])
    return df

# Analyzer function to apply sentiment analysis on each review
def analyzer(text):
    output = sentiment_Analyzer(text)[0]
    return output['label'], output['score']

# Function to add an 'Evaluation' column to the DataFrame based on sentiment analysis
def evaluate_reviews(df):
    df['Evaluation'] = df['Review'].apply(lambda x: analyzer(x))
    df[['Sentiment', 'Score']] = pd.DataFrame(df['Evaluation'].tolist(), index=df.index)
    df.drop(columns=['Evaluation'], inplace=True)
    return df

# Function to create a pie chart showing the percentage of positive and negative reviews
def create_pie_chart(df):
    sentiment_counts = df['Sentiment'].value_counts()
    labels = sentiment_counts.index
    sizes = sentiment_counts.values
    colors = ['#ff9999', '#66b3ff', '#99ff99', '#ffcc99']
    fig, ax = plt.subplots()
    ax.pie(sizes, labels=labels, colors=colors, autopct='%1.1f%%', startangle=90)
    ax.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.
    plt.title('Sentiment Analysis of Reviews')
    chart_path = 'sentiment_pie_chart.png'
    plt.savefig(chart_path)
    plt.close(fig)
    return chart_path

# Function to process the file
def process_reviews(file):
    file_path = file.name
    df = read_reviews_to_dataframe(file_path)
    df = evaluate_reviews(df)
    chart_path = create_pie_chart(df)
    return df, chart_path

# Gradio interface function
def gradio_interface(file):
    return process_reviews(file)

# Create the Gradio interface
interface = gr.Interface(
    fn=gradio_interface,
    inputs=gr.File(label="Upload a text file with reviews"),
    outputs=[
        gr.Dataframe(label="Reviews with Evaluation"),
        gr.Image(label="Sentiment Pie Chart")
    ],
    title="Sentiment Analyzer",
    description="Upload a text file with reviews to analyze the sentiment and visualize the results.",
    examples=[[DEFAULT_FILE_PATH]],
    allow_flagging="never"
)

# Launch the Gradio interface
interface.launch()