Spaces:
Running
on
Zero
Running
on
Zero
add auth
Browse files
app.py
CHANGED
@@ -1,20 +1,29 @@
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import gradio as gr
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from transformers import pipeline
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from datasets import load_dataset
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import pandas as pd
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# Load the dataset
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ds = load_dataset('ZennyKenny/demo_customer_nps')
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df = pd.DataFrame(ds['train'])
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# Initialize the model pipeline
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pipe = pipeline("text-generation", model="mistralai/Mistral-Small-24B-Base-2501")
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# Function to classify customer comments
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def classify_comments():
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results = []
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for comment in df['customer_comment']:
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prompt = f"Classify this customer feedback: '{comment}' into one of
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category = pipe(prompt, max_length=30)[0]['generated_text']
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results.append(category)
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df['comment_category'] = results
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import gradio as gr
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from transformers import pipeline
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import pandas as pd
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# Load the dataset
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DATASET_URL = 'https://huggingface.co/datasets/ZennyKenny/demo_customer_nps/resolve/main/customer_feedback_dataset.csv'
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from datasets import load_dataset
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ds = load_dataset('ZennyKenny/demo_customer_nps')
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df = pd.DataFrame(ds['train'])
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# Initialize the model pipeline
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from huggingface_hub import login
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import os
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# Login using the API key stored as an environment variable
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hf_api_key = os.getenv("API_KEY")
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login(token=hf_api_key)
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pipe = pipeline("text-generation", model="mistralai/Mistral-Small-24B-Base-2501")
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# Function to classify customer comments
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@spaces.GPU
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def classify_comments():
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results = []
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for comment in df['customer_comment']:
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prompt = f"Classify this customer feedback: '{comment}' into one of the following categories: Price of Service, Quality of Customer Support, Product Experience. Please only respond with the category name and nothing else."
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category = pipe(prompt, max_length=30)[0]['generated_text']
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results.append(category)
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df['comment_category'] = results
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