Create app.py

app.py

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+import gradio as gr
+import torch
+import pandas as pd
+
+# Use a pipeline as a high-level helper
+from transformers import pipeline
+analyzer = pipeline("text-classification", model="distilbert/distilbert-base-uncased-finetuned-sst-2-english")
+
+# Load model directly
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+def sentiment_analyzer(reviews):
+    sentiment = analyzer(reviews)
+    return sentiment
+
+# Read Reviews & Analyzer Sentiments
+def read_reviews_and_analyze_sentiment(file_object):
+    """
+    Reads reviews from an Excel file, analyzes their sentiment, and returns a DataFrame
+    with the reviews, sentiment, and sentiment score.
+
+    Parameters:
+        input_excel (str): Path to the input Excel file.
+
+    Returns:
+        pd.DataFrame: DataFrame containing reviews, sentiment, and sentiment score.
+    """
+    # Read the Excel file
+    df = pd.read_xlsx(file_object)
+    
+
+    # Check if 'reviews' column exists
+    if 'review' not in df.columns:
+        raise ValueError("The input Excel file must contain a column named 'reviews'.")
+
+    # Analyze sentiment for each review
+    sentiments = []
+    scores = []
+    for review in df['review']:
+        obj = sentiment_analyzer(review[0])
+        sentiment = obj[0]['label']
+        score = obj[0]['score']
+        sentiments.append(sentiment)
+        scores.append(score)
+
+    # Add sentiment and score to the DataFrame
+    df['sentiment'] = sentiments
+    df['sentiment_score'] = scores
+
+    return df
+
+# file_path = '/content/sample_data/amazon customer reviews.csv'
+# result = read_reviews_and_analyze_sentiment(file_path)
+# print(result)
+
+
+gr.close_all()
+
+demo = gr.Interface(fn=read_reviews_and_analyze_sentiment,
+                    inputs=[gr.File(file_types=['xlsx'], label="Upload review file")],
+                    outputs=[gr.Dataframe(label="Analyze the sentiment of the reviews")],
+                    title = "Gen AI Sentiment Analyzer",
+                    description= "Review is positive negative."
+                    )
+demo.launch(share=True)