Create app.py

app.py

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+import torch
+import gradio as gr
+from transformers import pipeline
+import pandas as pd
+from textblob import TextBlob
+import matplotlib.pyplot as plt
+
+# model_path = "C:\\Users\\abdul\\Documents\\genaiproj\\genai\\Models\\models--distilbert--distilbert-base-uncased-finetuned-sst-2-english"
+analyzer = pipeline("text-classification", model="distilbert/distilbert-base-uncased-finetuned-sst-2-english")     
+# analyzer = pipeline("text-classification", model=model_path)
+
+# print(analyzer(["Nice to meet you!", "very expensive"]))
+
+def sentiment_analysis(review):
+    sentiment = analyzer(review)
+    return sentiment[0]['label']
+
+def plot_sentiment_distribution(df):
+    # Check if required columns are present
+    if 'Review' not in df.columns or 'Sentiment' not in df.columns:
+        raise ValueError("DataFrame must contain 'Review' and 'Sentiment' columns.")
+    
+    # Count positive and negative sentiments
+    sentiment_counts = df['Sentiment'].value_counts()
+
+    # Create a bar chart
+    fig, ax = plt.subplots()
+    sentiment_counts.plot(kind='bar', ax=ax, color=['skyblue', 'salmon'])
+    
+    # Set chart labels and title
+    ax.set_xlabel('Sentiment')
+    ax.set_ylabel('Count')
+    ax.set_title('Sentiment Distribution')
+    
+    # Return the figure object
+    return fig
+
+def analyze_reviews(file):
+    if not file.name.endswith('.xlsx'):
+        return "Invalid file type. Please upload an Excel file."
+    
+    # Read the Excel file
+    df = pd.read_excel(file)
+    
+    if 'Review' not in df.columns:
+        return "The Excel file must contain a column named 'Review'."
+    
+    # Apply get_sentiment function to each review and create new column
+    df['Sentiment'] = df['Review'].apply(sentiment_analysis)
+    chart_object = plot_sentiment_distribution(df)
+    return df, chart_object
+
+# Result = analyze_reviews("C:\\Users\\abdul\\Documents\\genaiproj\\genai\\Files\\app_reviews.xlsx")
+# print(Result)
+
+# Example usage
+# file_path = 'path_to_your_excel_file.xlsx'  # Update with your actual file path
+# result_df = analyze_reviews(file_path)
+# print(result_df)
+
+
+gr.close_all()
+
+# demo = gr.Interface(fn=summary, inputs="text", outputs="text")
+
+demo = gr.Interface(
+    fn=analyze_reviews, 
+    inputs=[gr.File(label="Input file to analyze")], 
+    outputs=[gr.Dataframe(label="Sentiments"), gr.Plot(label="Sentiment Distribution")], 
+    title="Sentiment Analyzer", 
+    theme="soft",
+    description="Analyze the sentiment of any review in seconds!")
+    
+demo.launch(share=True)
+
+
+
+
+# Example usage
+# data = {'Review': ['Great product!', 'Not good', 'Excellent service', 'Bad experience'], 
+#         'Sentiment': ['Positive', 'Negative', 'Positive', 'Negative']}
+# df = pd.DataFrame(data)
+# fig = plot_sentiment_distribution(df)
+# fig.show()