Create app.py

app.py

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+# This app analyzes product review sentiments using a DistilBERT model via Hugging Face + Gradio.
+# Input must be a .csv or .xlsx file with a 'Reviews' column; other columns are optional.
+# Only 'Reviews' are processed for classification as Positive or Negative.
+
+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
+# model_path= ("../Models/models--distilbert--distilbert-base-uncased-finetuned-sst-2-english"
+#              "/snapshots/714eb0fa89d2f80546fda750413ed43d93601a13")
+
+
+analyzer = pipeline("text-classification",
+                 model="distilbert/distilbert-base-uncased-finetuned-sst-2-english")
+
+# analyzer = pipeline("text-classification",
+#                  model=model_path)
+
+
+#print(analyzer(["Sahithi is good person  and will always trust all people"]))
+
+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('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):
+    # Load the Excel file into a DataFrame
+    df = pd.read_excel(file_object)
+
+    # Check if 'Review' column is in the DataFrame
+    if 'Reviews' not in df.columns:
+        raise ValueError("Excel file must contain a 'Review' column.")
+
+    # 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 df, chart_object
+
+# result = read_reviews_and_analyze_sentiment("../Files/Prod-review.xlsx")
+# print(result)
+# Example usage:
+# 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.File(label="Upload your review comment file")],
+                    outputs=[gr.Dataframe(label="Sentiments"), gr.Plot(label="Sentiment Analysis")],
+                    title="@Sahibhim GenAI Project 3: Sentiment Analyzer",
+                    description="THIS APPLICATION WILL BE USED TO ANALYZE THE SENTIMENT BASED ON FILE UPLAODED.")
+demo.launch()