app.py

import gradio as gr
import os
from loguru import logger
from langchain_community.llms import LlamaCpp
from langchain_core.callbacks import CallbackManager, StreamingStdOutCallbackHandler
from langchain_core.prompts import PromptTemplate
import spaces
import json
# Create a directory for logs if it doesn't exist
if not os.path.exists('logs'):
    os.makedirs('logs')

# Define the log file path
log_file = 'logs/file_{time}.log'

# Configure the logger to write to the log file
logger.add(log_file, rotation="500 MB")

template = """Question: {question}

Answer: Let's work this out in a step by step way to be sure we have the right answer."""

prompt = PromptTemplate.from_template(template)
# Callbacks support token-wise streaming
callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])

# n_gpu_layers = -1  # The number of layers to put on the GPU. The rest will be on the CPU. If you don't know how many layers there are, you can use -1 to move all to GPU.
# n_batch = 512  # Should be between 1 and n_ctx, consider the amount of VRAM in your GPU.

# Make sure the model path is correct for your system!
llm = LlamaCpp(
    model_path="/home/user/app/models/Phi-3-mini-4k-instruct-q4.gguf",
    callback_manager=callback_manager,
    verbose=True,  # Verbose is required to pass to the callback manager
)
llm_chain = prompt | llm

@spaces.GPU(duration=120)
def greet(name):
    logger.info("This is an info message")
    question = name
    # print(llm_chain.invoke({"question": question}))
    return llm_chain.invoke({"question": question})

demo = gr.Interface(fn=greet, inputs="text", outputs="text")
demo.launch()