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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() |