Create app.py

app.py

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+from transformers import pipeline
+import pandas as pd
+import gradio as gr
+
+# Initialize the sentiment analysis pipeline
+sentiment_pipeline = pipeline("sentiment-analysis")
+
+def analyze_csv(file_path):
+    # Read the CSV file
+    df = pd.read_csv(file_path)
+    
+    # Ensure the CSV has a 'text' column
+    if 'text' not in df.columns:
+        return "Error: CSV must contain a 'text' column."
+    
+    # Apply sentiment analysis on each text entry
+    results = df['text'].apply(lambda x: sentiment_pipeline(x)[0])
+    df['sentiment'] = results.apply(lambda r: r['label'])
+    df['score'] = results.apply(lambda r: r['score'])
+    
+    # Return the DataFrame as a CSV string
+    return df.to_csv(index=False)
+
+def gradio_analyze(file_obj):
+    # Get the path of the uploaded file and analyze it
+    file_path = file_obj.name
+    return analyze_csv(file_path)
+
+# Define the Gradio interface
+iface = gr.Interface(
+    fn=gradio_analyze,
+    inputs=gr.File(label="Upload CSV File", file_count="single", type="file"),
+    outputs=gr.Textbox(label="CSV with Sentiment Analysis"),
+    title="CSV Sentiment Analysis App",
+    description="Upload a CSV file with a 'text' column. The app will run sentiment analysis on each row and return the CSV with sentiment labels and scores."
+)
+
+if __name__ == "__main__":
+    iface.launch()