Added app.py file

app.py

@@ -0,0 +1,59 @@
+import torch
+import gradio as gr
+import pandas as pd
+import matplotlib.pyplot as plt
+from transformers import pipeline
+
+analyzer = pipeline("text-classification",model="distilbert/distilbert-base-uncased-finetuned-sst-2-english")
+
+# model_path = ("../.venv/Models/models--distilbert--distilbert-base-uncased-finetuned-sst-2-english/snapshots/714eb0fa89d2f80546fda750413ed43d93601a13")
+
+# analyzer = pipeline("text-classification",model=model_path)
+
+# print(analyzer(["This production is good", "This product was quite expensive"]))
+
+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%%')
+    ax.set_title('Review Sentiment Counts')
+    ax.set_xlabel('Sentiment')
+    ax.set_ylabel('Count')
+    # ax.set_xticklabels(['Positive', 'Negative'], rotation=0)
+    # Return the figure object
+    return fig
+
+def read_reviews_and_analyze_sentiment(file_object):
+    df = pd.read_excel(file_object)
+
+    if 'Review' not in df.columns:
+        raise ValueError("Excel file must contain a 'Review' column.")
+
+    df['Sentiment'] = df['Review'].apply(sentiment_analyzer)
+    chart_object = sentiment_bar_chart(df)
+    return df, chart_object
+
+# result = read_reviews_and_analyze_sentiment("../Files/Sentiment_Analysis_Reviews.xlsx")
+# print(result)
+# df = read_reviews_and_analyze_sentiment('path_to_your_excel_file.xlsx')
+# print(df)
+
+demo = gr.Interface(fn=read_reviews_and_analyze_sentiment,
+                    # inputs=[gr.Textbox(label="Input you text/review comment for analysis", lines =4)],
+                    # outputs=[gr.Textbox(label="Sentiment", lines=1)],
+                    inputs=[gr.File(file_types=[".xlsx"], label="Upload your review comment file")],
+                    outputs=[gr.Dataframe(label="Sentiments"), gr.Plot(label="Sentiment Analysis")],
+                    title="Sentiment Analyzer",
+                    description="THIS APPLICATION CAN BE USED TO ANALYZE THE SENTIMENT BASED ON FILE UPLAODED.")
+demo.launch()
+
+
+# Assuming you have a dataframe `df` with appropriate data
+# fig = sentiment_bar_chart(df)
+# fig.show()  # This line is just to visualize the plot in a local environment