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import gradio as gr
from huggingface_hub import InferenceClient
from transformers import pipeline
# Hugging Face Inference API Client
client = InferenceClient("bigscience/bloom")
# Hugging Face Transformers Pipeline for Question Answering
qa_pipeline = pipeline("question-answering", model="distilbert-base-cased-distilled-squad")
# Code Snippets
code_snippets = {
"fibonacci": {
"python": """
def fib(n):
if n <= 0:
return 0
elif n == 1:
return 1
else:
return fib(n-1) + fib(n-2)
""",
"javascript": """
function fib(n) {
if (n <= 0) return 0;
if (n === 1) return 1;
return fib(n - 1) + fib(n - 2);
}
"""
}
}
# Chatbot Function
def chatbot(message, history):
if "python" in message.lower() and "fibonacci" in message.lower():
return "Here is the Fibonacci code in Python:", gr.Code(language="python", value=code_snippets["fibonacci"]["python"])
elif "javascript" in message.lower() and "fibonacci" in message.lower():
return "Here is the Fibonacci code in JavaScript:", gr.Code(language="javascript", value=code_snippets["fibonacci"]["javascript"])
elif "huggingface" in message.lower():
# Generate text using the Hugging Face Inference API
prompt = "Write a short poem about cybersecurity."
response = client.text_generation(prompt, max_length=50)
return f"Hugging Face Generated Text: {response['generated_text']}", None
elif "question" in message.lower():
# Use the QA pipeline to answer a question
question = "What is the purpose of cybersecurity?"
context = "Cybersecurity involves protecting systems, networks, and programs from digital attacks."
result = qa_pipeline(question=question, context=context)
return f"Hugging Face QA Answer: {result['answer']}", None
else:
return "Please ask about Python/JavaScript code or Hugging Face functionalities.", None
# Gradio Interface
with gr.Blocks() as demo:
code_output = gr.Code(render=False)
with gr.Row():
with gr.Column():
gr.Markdown("<center><h1>Chat About Code or Hugging Face</h1></center>")
gr.ChatInterface(
chatbot,
examples=[
"Python Fibonacci",
"JavaScript Fibonacci",
"HuggingFace: Generate text",
"Ask a question about cybersecurity",
],
additional_outputs=[code_output],
type="messages"
)
with gr.Column():
gr.Markdown("<center><h1>Code or Model Output</h1></center>")
code_output.render()
# Launch the Gradio App
demo.launch() |