import torch
import gradio as gr
import pandas as pd
import matplotlib.pyplot as plt

# Use a pipeline as a high-level helper
from transformers import pipeline

#analyzer = pipeline("text-classification", model="distilbert/distilbert-base-uncased-finetuned-sst-2-english")
analyzer = pipeline("text-classification", model="lxyuan/distilbert-base-multilingual-cased-sentiments-student")

def sentiment_analyzer(review):
    sentiment = analyzer(review)
    return sentiment[0]['label']

def sentiment_bar_chart(df):
    sentiment_counts = df['Sentiment'].value_counts()

    # Create a bar chart
    fig, ax = plt.subplots()
    sentiment_counts.plot(kind='pie', ax=ax, autopct='%1.1f%%', color=['green', 'red'])
    ax.set_title('Reviews')
    ax.set_xlabel('Stimmung')
    ax.set_ylabel('Anzahl')
    # ax.set_xticklabels(['Positive', 'Negative'], rotation=0)

    # Return the figure object
    return fig


def read_reviews_and_analyze_sentiment(file_object):
    # Load the Excel file into a DataFrame
    df = pd.read_excel(file_object)
    print(df.columns)
    # Check if 'Review' column is in the DataFrame
    #for col in df.columns:
        #print(f"col={col}")
    if 'Reviews' not in df.columns:
        raise ValueError("Die Excel-Datei muss eine Spalte 'Reviews' enthalten.")

    # Apply the get_sentiment function to each review in the DataFrame
    df['Sentiment'] = df['Reviews'].apply(sentiment_analyzer)
    chart_object = sentiment_bar_chart(df)
    return  chart_object, df

demo = gr.Interface(fn=read_reviews_and_analyze_sentiment,
                    inputs=[gr.File(file_types=["xlsx"], label="Laden Sie Ihre xls-Review-Datei hoch")],
                    outputs=[ gr.Plot(label="Stimmungsanalyse"), gr.Dataframe(label="Stimmungen")],
                    title="Project 3: Stimmung-Analysator",
                    description="DIESE ANWENDUNG WIRD VERWENDET, UM DIE STIMMUNG AUF DER GRUNDLAGE DER HOCHGELADENEN DATEI ZU ANALYSIEREN.",
                    allow_flagging="never",
                    submit_btn="Übermitteln",
                    clear_btn="Bereinigen",)
demo.launch()