Create app.py

app.py

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+from transformers import pipeline
+import torch
+import gradio as gr
+
+
+def sentiment_analysis(text):
+  analyzer = pipeline("text-classification", model="distilbert/distilbert-base-uncased-finetuned-sst-2-english")
+  sentiment = analyzer(text)
+  return [sentiment[0]['label'], sentiment[0]['score']]
+
+# Test 1:
+# print(analyzer(["This is awesome. Reliable product.", "Very expensive product. Company should use better pricing."]))
+# print(sentiment_analysis(["This is awesome. Reliable product.", "Very expensive product. Company should use better pricing."]))
+# [{'label': 'POSITIVE', 'score': 0.9998791217803955}, {'label': 'NEGATIVE', 'score': 0.9994811415672302}]
+
+# Test with Gradio:
+
+# gr.close_all()
+
+# version 0.1
+# demo = gr.Interface(
+#     fn=sentiment_analysis, 
+#     inputs=[gr.Textbox(label="Text Input for Sentiment Analysis", lines=4)], 
+#     outputs=[gr.Textbox(label="Analyzed Sentiment", lines=4), gr.Textbox(label="Sentiment Strength", lines=1)], 
+#     title="GenAI Sentiment Analyzer", 
+#     description="This App does seniment analysis of text input")
+# demo.launch()
+
+# Uploading an excel file and getting output as required: 
+import pandas as pd
+import matplotlib.pyplot as plt
+
+def create_charts(df):
+    # Validate DataFrame
+    if not all(col in df.columns for col in ['Review', 'Sentiment', 'Sentiment Score']):
+        raise ValueError("The DataFrame must contain 'Review', 'Sentiment', and 'Sentiment Score' columns.")
+    # Create Pie Chart for Sentiment Distribution
+    sentiment_counts = df['Sentiment'].value_counts()
+    fig1, ax1 = plt.subplots(figsize=(8, 6))
+    ax1.pie(sentiment_counts, labels=sentiment_counts.index, autopct='%1.1f%%', colors=['skyblue', 'lightcoral'])
+    ax1.set_title('Distribution of Positive and Negative Reviews')
+
+    # Create Scatter Plot for Sentiment Scores
+    fig2, ax2 = plt.subplots(figsize=(10, 6))
+    for sentiment, color in zip(['positive', 'negative'], ['green', 'red']):
+        subset = df[df['Sentiment'].str.lower() == sentiment]
+        ax2.scatter(subset.index, subset['Sentiment Score'], label=sentiment.capitalize(), color=color, alpha=0.6)
+
+    ax2.axhline(0, color='gray', linewidth=0.5)
+    ax2.set_xlabel('Review Index')
+    ax2.set_ylabel('Sentiment Score')
+    ax2.set_title('Scatter Plot of Reviews by Sentiment Score')
+    ax2.legend()
+
+    return fig1, fig2
+
+
+
+def analyze_reviews(file_path):
+    # Read the Excel file
+    df = pd.read_excel(file_path)
+    
+   # Attempt to identify the review column if it is not labeled correctly
+    if 'Review' not in df.columns:
+        for col in df.columns:
+            if df[col].dtype == 'object':  # Assuming reviews are text
+                df.rename(columns={col: 'Review'}, inplace=True)
+                break
+    
+    # Ensure the dataframe now has a 'Review' column
+    if 'Review' not in df.columns:
+        raise ValueError("The input file must contain a column with review text.")
+    
+    # Remove any column that contains serial numbers
+    df = df[[col for col in df.columns if not pd.api.types.is_numeric_dtype(df[col]) or col == 'Review']]
+    
+
+    # Apply the get_sentiment function to each review
+    results = df['Review'].apply(sentiment_analysis)
+
+    # Split the results into separate columns for sentiment and sentiment score
+    [df['Sentiment'], df['Sentiment Score']] = zip(*results)
+
+    # Adjust the sentiment score to be negative if the sentiment is negative
+    df.loc[df['Sentiment'] == 'NEGATIVE', 'Sentiment Score'] *= -1
+
+    pie_chart, scatter_plot = create_charts(df)
+
+    return [df, pie_chart, scatter_plot]
+
+
+# Example usage
+# file_path = '/teamspace/studios/this_studio/sentiment-analyzer/Sample_Sentiments (1).xlsx'
+# result_df = analyze_reviews(file_path)
+# print(result_df)
+
+
+
+
+gr.close_all()
+
+# version 0.2
+demo = gr.Interface(
+    fn=analyze_reviews, 
+    inputs=[gr.File(label="Upload your excel file containing user reviews")], 
+    outputs=[
+        gr.DataFrame(label="Analysis of the uploaded excel file"), 
+        gr.Plot(label="Sentiment Analysis - Positive & Negative"),
+        gr.Plot(label="Sentiment Analysis - Sentiment Score Distribution")
+        ], 
+    title="GenAI Sentiment Analyzer", 
+    description="This App does sentiment analysis of User Reviews")
+demo.launch()
+
+