app.py

import os
import torch
import gradio as gr
import spaces
from huggingface_hub import InferenceClient
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import Chroma
from langchain.prompts import PromptTemplate

# Verify PyTorch version compatibility
TORCH_VERSION = torch.__version__
SUPPORTED_TORCH_VERSIONS = ['2.0.1', '2.1.2', '2.2.2', '2.4.0']
if TORCH_VERSION.rsplit('+')[0] not in SUPPORTED_TORCH_VERSIONS:
    print(f"Warning: Current PyTorch version {TORCH_VERSION} may not be compatible with ZeroGPU. "
          f"Supported versions are: {', '.join(SUPPORTED_TORCH_VERSIONS)}")

# Initialize components outside of GPU scope
client = InferenceClient("meta-llama/Llama-3.2-3B-Instruct")
embeddings = HuggingFaceEmbeddings(
    model_name="sentence-transformers/all-MiniLM-L6-v2",
    model_kwargs={"device": "cpu"}  # Keep embeddings on CPU
)

# Load database
db = Chroma(
    persist_directory="db",
    embedding_function=embeddings
)

# Prompt templates
DEFAULT_SYSTEM_PROMPT = """
Based on the information in this document provided in context, answer the question as accurately as possible in 1 or 2 lines. If the information is not in the context,
respond with "I don't know" or a similar acknowledgment that the answer is not available.
""".strip()

def generate_prompt(prompt: str, system_prompt: str = DEFAULT_SYSTEM_PROMPT) -> str:
    return f"""
[INST] <<SYS>>
{system_prompt}
<</SYS>>

{prompt} [/INST]
""".strip()

template = generate_prompt(
    """
{context}

Question: {question}
""",
    system_prompt="Use the following pieces of context to answer the question at the end. Do not provide commentary or elaboration more than 1 or 2 lines.?"
)

prompt_template = PromptTemplate(template=template, input_variables=["context", "question"])

@spaces.GPU(duration=30)  # Reduced duration for faster queue priority
def respond(
    message,
    history,
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    """GPU-accelerated response generation"""
    try:
        # Retrieve context (CPU operation)
        docs = db.similarity_search(message, k=2)
        context = "\n".join([doc.page_content for doc in docs])
        print(f"Retrieved context: {context[:200]}...")
        
        # Format prompt
        formatted_prompt = prompt_template.format(
            context=context,
            question=message
        )
        print(f"Full prompt: {formatted_prompt}")
        
        # Stream response (GPU operation)
        response = ""
        for message in client.text_generation(
            prompt=formatted_prompt,
            max_new_tokens=max_tokens,
            stream=True,
            temperature=temperature,
            top_p=top_p,
        ):
            response += message
            yield response
            
    except Exception as e:
        yield f"An error occurred: {str(e)}"

# Create Gradio interface
demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(
            value=DEFAULT_SYSTEM_PROMPT,
            label="System Message",
            lines=3,
            visible=False
        ),
        gr.Slider(
            minimum=1,
            maximum=2048,
            value=500,
            step=1,
            label="Max new tokens"
        ),
        gr.Slider(
            minimum=0.1,
            maximum=4.0,
            value=0.1,
            step=0.1,
            label="Temperature"
        ),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.95,
            step=0.05,
            label="Top-p (nucleus sampling)"
        ),
    ],
    title="ROS2 Expert Assistant",
    description="Ask questions about ROS2, navigation, and robotics. I'll provide concise answers based on the available documentation.",
)

if __name__ == "__main__":
    demo.launch()