Create app.py

app.py

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+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.inputs.File(file_count=1, label="Upload Excel File"), gr.inputs.Text(label="Column Name")],
+                     outputs=gr.outputs.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()