app.py

import gradio as gr
import pandas as pd
from wordcloud import WordCloud, STOPWORDS
from nltk.corpus import opinion_lexicon
from nltk.tokenize import word_tokenize
import nltk
import tempfile
import os

# Ensure NLTK data is downloaded
nltk.download('opinion_lexicon')
nltk.download('punkt')

# Load positive and negative words
positive_words = set(opinion_lexicon.positive())
negative_words = set(opinion_lexicon.negative())

class SimpleGroupedColorFunc(object):
    """Create a color function object which assigns specified colors to words based on the membership in predefined lists."""
    def __init__(self, color_to_words, default_color):
        self.color_to_words = color_to_words
        self.default_color = default_color
        self.words_to_color = {word: color for (color, words) in color_to_words.items() for word in words}
    
    def __call__(self, word, **kwargs):
        return self.words_to_color.get(word, self.default_color)

def generate_wordcloud(excel_file, column_name):
    # Read the Excel file from the uploaded file
    df = pd.read_excel(excel_file)
    
    # Use the user-provided column name
    if column_name not in df.columns:
        return "The specified column name does not exist in the Excel file."

    text_data = ' '.join(df[column_name].dropna().astype(str))

    # Tokenize the text
    tokens = word_tokenize(text_data.lower())

    # Combine positive and negative tokens
    combined_tokens = [word for word in tokens if word in positive_words or word in negative_words]

    # Generate word cloud text
    combined_text = ' '.join(combined_tokens)

    # Generate a word cloud
    wordcloud = WordCloud(stopwords=STOPWORDS, background_color='white', width=800, height=400).generate(combined_text)

    # Map of words to colors
    color_to_words = {
        'green': positive_words,
        'red': negative_words
    }

    # Default color for words not in any predefined list
    default_color = 'gray'

    # Create the custom color function
    grouped_color_func = SimpleGroupedColorFunc(color_to_words, default_color)

    # Apply our color function
    wordcloud.recolor(color_func=grouped_color_func)

    # Save the word cloud to a temporary file
    with tempfile.NamedTemporaryFile(delete=False, suffix='.png') as tmp:
        wordcloud.to_image().save(tmp.name)
        # Return the path to the temporary file
        return tmp.name

# Define Gradio interface with an additional input for the column name
iface = gr.Interface(fn=generate_wordcloud, 
                     inputs=[gr.File(label="Upload Excel File"), gr.Text(label="Column Name")], 
                     outputs=gr.Image(label="Word Cloud"),
                     title="Sentiment Analysis Word Cloud Generator",
                     description="Upload an Excel file and specify a column name to generate a sentiment-based word cloud.")

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