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 product is good", "This product was quite expensive"]))
+
+
+def sentiment_analyzer(review):
+    sentiment = analyzer(review)
+    return sentiment[0]['label']
+
+def generate_sentiment_bar_chart(df):
+    # Validate DataFrame
+    if not {'Review', 'Sentiment'}.issubset(df.columns):
+        raise ValueError("DataFrame must contain 'Review' and 'Sentiment' columns.")
+    
+    # Count occurrences of each sentiment
+    sentiment_counts = df['Sentiment'].value_counts()
+    
+    # Create bar chart
+    fig, ax = plt.subplots(figsize=(8, 5))
+    sentiment_counts.plot(kind='bar', color=['green', 'red'], edgecolor='black', ax=ax)
+    
+    # Customize plot
+    ax.set_title("Sentiment Distribution", fontsize=14)
+    ax.set_xlabel("Sentiment", fontsize=12)
+    ax.set_ylabel("Count", fontsize=12)
+    ax.grid(axis='y', linestyle='--', alpha=0.7)
+    plt.xticks(rotation=45)
+    
+    # Adjust layout
+    plt.tight_layout()
+    
+    return fig
+
+
+def read_review_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' colum.")
+    df['Sentiment'] = df['Review'].apply(sentiment_analyzer)
+    chat_object = generate_sentiment_bar_chart(df)
+    return df, chat_object
+# file = '../files/product_review.xlsx'
+# result = read_review_and_analyze_sentiment(file)
+# print(result)
+
+gr.close_all()
+
+# demo = gr.Interface(fn=summary, inputs="text", outputs="text")
+demo = gr.Interface(fn=read_review_and_analyze_sentiment, 
+                    inputs=[gr.File(file_types=[".xlsx"],label="Input your review comment")],
+                    outputs=[gr.Dataframe(label="Sentiment"), gr.Plot(label="Sentiment Analysis")],
+                    title="GenAI Project 3: Sentiment Analyzer",
+                    description="This application is use to analyze the sentiment based on the File uploaded.")
+demo.launch()