app.py

import gradio as gr
import pandas as pd
from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt
from nltk.corpus import opinion_lexicon
from nltk.tokenize import word_tokenize
import nltk
from PIL import Image
import numpy as np
import io

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

# Your existing logic for generate_word_cloud goes here

def generate_word_cloud(excel_file, column_name):
    # Adapt your existing word cloud generation code to work with the input Excel file and column name.
    # Instead of displaying the plot, save it to a buffer and return the image.

    # Placeholder for your existing logic

    # Save the plot to a buffer
    buf = io.BytesIO()
    plt.savefig(buf, format='png')
    buf.seek(0)
    image = Image.open(buf)
    # Convert to numpy array for Gradio output
    image_array = np.array(image)
    return image_array

# Define Gradio interface
def process_excel(file_obj, column_name):
    # Save the uploaded file to a temporary location
    with open("temp_excel_file.xlsx", "wb") as f:
        f.write(file_obj.read())

    # Generate the word cloud
    image = generate_word_cloud("temp_excel_file.xlsx", column_name)

    # Return the image
    return image

iface = gr.Interface(fn=process_excel,
                     inputs=[gr.File(file_count=1, label="Upload Excel File"), gr.Textbox(label="Column Name")],
                     outputs=gr.Image(type="numpy", label="Word Cloud"),
                     title="Word Cloud Generator",
                     description="Upload an Excel file and enter the column name to generate a word cloud of positive and negative words.")

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