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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() |