initial commit

app.py

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+import gradio as gr
+from transformers import pipeline
+import pandas as pd
+
+# Load the dataset
+DATASET_URL = 'https://huggingface.co/datasets/ZennyKenny/demo_customer_nps/resolve/main/customer_feedback_dataset.csv'
+df = pd.read_csv(DATASET_URL)
+
+# Initialize the model pipeline
+pipe = pipeline("text-generation", model="mistralai/Mistral-Small-24B-Base-2501")
+
+# Function to classify customer comments
+def classify_comments():
+    results = []
+    for comment in df['customer_comment']:
+        prompt = f"Classify this customer feedback: '{comment}' into one of five categories."
+        category = pipe(prompt, max_length=30)[0]['generated_text']
+        results.append(category)
+    df['comment_category'] = results
+    return df[['customer_comment', 'comment_category']].to_html(index=False)
+
+# Gradio Interface
+with gr.Blocks() as nps:
+    gr.Markdown("# NPS Comment Categorization")
+    classify_btn = gr.Button("Classify Comments")
+    output = gr.HTML()
+
+    classify_btn.click(fn=classify_comments, outputs=output)
+
+nps.launch()