Upload SentimentAnalyzer.py

SentimentAnalyzer.py

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+import torch
+import re
+import gradio as gr
+import pandas as pd
+import matplotlib.pyplot as plt
+from pydantic import BaseModel, PydanticUserError, ConfigDict
+from pydantic import BaseModel, ConfigDict
+
+class MyModel(BaseModel):
+    request: 'starlette.requests.Request'
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+from pydantic_core import core_schema
+from starlette.requests import Request
+
+def get_pydantic_core_schema(request_type, handler):
+    return core_schema.any_schema()
+
+Request.__get_pydantic_core_schema__ = get_pydantic_core_schema
+
+
+from transformers import pipeline
+
+pipe = pipeline("text-classification", 
+                model="distilbert/distilbert-base-uncased-finetuned-sst-2-english")
+
+
+
+analyzer=pipeline(task="text-classification", model="distilbert/distilbert-base-uncased-finetuned-sst-2-english")
+
+
+def sentiment_analyzer(review):
+    sentiment = analyzer(review)
+    return sentiment[0]['label']
+
+def sentiment_bar_chart(df):
+    sentiment_counts=df['Sentiment'].value_counts()
+
+    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 fig
+    
+
+def get_sentiment(review):
+    from textblob import TextBlob
+    analysis=TextBlob(review)
+    return analysis.sentiment.polarity
+
+def read_reviews_and_analyze_sentiment(file_object):
+    print("file_obj received:", file_object)
+    
+    # Handle file path or file object
+    if hasattr(file_object, 'name'):
+        file_name = file_object.name
+    else:
+        file_name = file_object
+
+    # Read file based on extension
+    if str(file_name).lower().endswith('.csv'):
+        df = pd.read_csv(file_object)
+    elif str(file_name).lower().endswith(('.xlsx', '.xls', '.xlsm', '.xlsb', '.ods', '.odt')):
+        df = pd.read_excel(file_object)
+    else:
+        raise ValueError("Unsupported file type. Please upload a CSV or Excel file.")
+    
+    df['Sentiment']=df['Reviews'].apply(sentiment_analyzer)
+    chart_object=sentiment_bar_chart(df)
+
+    return df, chart_object
+
+
+gr.close_all()
+demo = gr.Interface(
+    fn=read_reviews_and_analyze_sentiment,
+    inputs=gr.File(file_types=['.xlsx','csv'], label='Upload your review comment file'),
+    outputs=[gr.Dataframe(label='Sentiments'), gr.Plot(label='Sentiment Analysis')],
+    title='KS Sentiment Analyzer',
+    description='This application will classify the sentiment of reviews based on an uploaded Excel file. The file must contain a column named "Reviews".'
+)
+
+demo.launch(share=True)
+