app.py

from fastapi import FastAPI
import torch 
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline 

app = FastAPI()

@app.get("/")
def greet_json():
    return {"Hello": "World!"}

@app.get("/hello/{msg}")
def say_hello(msg: str):
    print("model")
    torch.random.manual_seed(0) 
    model = AutoModelForCausalLM.from_pretrained( 
        "microsoft/Phi-3-mini-4k-instruct",  
        device_map="auto",  
        torch_dtype="auto",  
        trust_remote_code=True,  
    ) 
    print("token & msg")
    tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-128k-instruct") 

    messages = [ 
        {"role": "system", "content": "You are a helpful AI assistant."}, 
        {"role": "user", "content": "Can you provide ways to eat combinations of bananas and dragonfruits?"}, 
        {"role": "assistant", "content": "Sure! Here are some ways to eat bananas and dragonfruits together: 1. Banana and dragonfruit smoothie: Blend bananas and dragonfruits together with some milk and honey. 2. Banana and dragonfruit salad: Mix sliced bananas and dragonfruits together with some lemon juice and honey."}, 
        {"role": "user", "content": msg}, 
    ] 
    print("pipe")
    pipe = pipeline( 
        "text-generation", 
        model=model, 
        tokenizer=tokenizer, 
    ) 
    print("output")
 #   generation_args = { 
 #       "max_new_tokens": 500, 
 #       "return_full_text": False, 
 #       "temperature": 0.0, 
 #       "do_sample": False, 
#    } 

    output = pipe(messages) #, **generation_args) 
    print("complete")
    return {"message": output[0]['generated_text']}