Create app.py

app.py

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+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=model_path)
+
+
+analyzer = pipeline("text-classification",
+                model="distilbert/distilbert-base-uncased-finetuned-sst-2-english")
+
+# print(analyzer(["This production is good", "This product was quite expensive"]))
+
+
+def sentiment_analyzer(review):
+    sentiment = analyzer(review)
+    return sentiment[0]['label']
+
+def sentiment_pie_chart(df):
+    sentiment_counts = df['Sentiment'].value_counts()
+
+    # Create a pie chart
+    fig, ax = plt.subplots()
+    pie = ax.pie(sentiment_counts, autopct='%1.1f%%', colors=['green', 'red'], labels=sentiment_counts.index)
+    
+    # Add count values to each slice
+    for i, p in enumerate(pie[0]):
+        count = f'({sentiment_counts[i]})'
+        ax.annotate(count, xy=p.get_xy(), xytext=(0, 0.5), textcoords='offset points', ha='center', fontsize=10)
+
+    ax.set_title('Review Sentiment Counts')
+    ax.set_xlabel('Sentiment')
+    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_pie_chart(df)
+    return df, chart_object
+
+# result = read_reviews_and_analyze_sentiment("../Files/Prod-review.xlsx")
+# print(result)
+
+
+demo = gr.Interface(fn=read_reviews_and_analyze_sentiment,
+                    inputs=[gr.File(file_types=["xlsx"], label="Upload your reviews file")],
+                    outputs=[gr.Dataframe(label="Sentiments"), gr.Plot(label="Sentiment Analysis")],
+                    title="Sentiment Analyzer",
+                    description="Analyze the sentiment based on file uploaded (Excel file .xlsx must contain a 'Reviews' column)")
+demo.launch()