from transformers import pipeline
import pandas as pd
import gradio as gr

# Initialize the sentiment analysis pipeline
sentiment_pipeline = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english", framework="pt")

def analyze_csv(file_path):
    # Read the CSV file
    df = pd.read_csv(file_path)
    
    # Ensure the CSV has a 'text' column
    if 'text' not in df.columns:
        return "Error: CSV must contain a 'text' column."
    
    # Apply sentiment analysis on each text entry
    results = df['text'].apply(lambda x: sentiment_pipeline(x)[0])
    df['sentiment'] = results.apply(lambda r: r['label'])
    df['score'] = results.apply(lambda r: r['score'])

    # Save output to a new CSV file
    output_csv_path = "output.csv"
    df.to_csv(output_csv_path, index=False)
    return output_csv_path  # Return path to the new CSV

# Define the Gradio interface
iface = gr.Interface(
    fn=analyze_csv,
    inputs=gr.File(label="Upload CSV File", file_count="single", type="filepath"),
    outputs=gr.File(label="Download CSV File"),
    title="CSV Sentiment Analysis App",
    description="Upload a CSV file with a 'text' column. The app will run sentiment analysis on each row and return a downloadable CSV with sentiment labels and scores."
)

if __name__ == "__main__":
    iface.launch()