app.py

# -*- coding: utf-8 -*-
"""Untitled6.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1F6f_vJbssO7C2FM6FILWljFYacDmbVBY
"""

from IPython.display import display, HTML

# Inject CSS to enable wrapping
display(HTML('''
    <style>
        .output_area pre {
            white-space: pre-wrap;
        }
    </style>
'''))

!pip install transformers gradio

import os
from google.colab import userdata

# Get the Hugging Face API key from Colab Secrets
api_key = userdata.get('HF_TOKEN')

# Ensure the API key is set before using it
if api_key is None:
    raise ValueError("Hugging Face API key not found. Please ensure it is set in Colab Secrets.")

# Set the Hugging Face token as an environment variable
os.environ["HF_TOKEN"] = api_key

# Hugging Face libraries will automatically use this token for authentication
print("Hugging Face API key successfully loaded! You're good to go!")

# Now you can continue with your Hugging Face-related code

# Import necessary libraries
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model and tokenizer
model_name = "distilgpt2"  # A lightweight, CPU-friendly model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Define the function to generate a response
def generate_response(prompt):
    # Tokenize the input prompt
    inputs = tokenizer(prompt, return_tensors="pt")
    # Generate a response
    outputs = model.generate(
    inputs.input_ids,
    max_length=50,
    do_sample=True,  # Enable sampling
    temperature=0.7,  # Controls randomness
    top_p=0.9,        # Nucleus sampling
    pad_token_id=tokenizer.eos_token_id
)

    # Decode the output and set clean_up_tokenization_spaces to True to avoid warnings
    response = tokenizer.decode(outputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=True)
    return response

# Example usage
prompt = "I went to Safeway and I bought a"
response = generate_response(prompt)
print(response)

def persona_response(prompt, persona="I am a helpful assistant"):
    full_prompt = f"{persona}. {prompt}"
    return generate_response(full_prompt)

# Import Gradio
import gradio as gr

# Define Gradio interface function
def chat_interface(user_input, persona="I am a helpful assistant"):
    return persona_response(user_input, persona)

# Set up Gradio interface
interface = gr.Interface(
    fn=chat_interface,
    inputs=["text", "text"],  # Allows input for both prompt and persona
    outputs="text",
    title="Simple Chatbot",
    description="Type something to chat with the bot! Add a persona to change its style, like 'I am a shopping assistant.'"
)

# Launch the Gradio interface in Colab
interface.launch(share=True)  # share=True creates a public link

"""### Uploading to Hugging Face Spaces

Now that we have our chatbot working, here’s how to upload it to Hugging Face Spaces:

1. Go to [Hugging Face Spaces](https://huggingface.co/spaces).
2. Create a new Space, choose "Gradio" as the app type, and name it (e.g., "My Simple Chatbot").
3. Upload this notebook, along with any necessary files or model assets.
4. Set up your Space and click "Deploy." Your Gradio chatbot will now be live!

**Tips for Deployment:**
- Ensure you include any persona settings or customizations.
- Test the app after deployment to confirm it works as expected.

Once deployed, students can share the link with family and friends!

"""