app.py

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()