import random
from typing import Optional
from fastapi import FastAPI
from pydantic import BaseModel

from peft import PeftModel
from transformers import LLaMATokenizer, LLaMAForCausalLM, GenerationConfig

app = FastAPI()

tokenizer = LLaMATokenizer.from_pretrained("decapoda-research/llama-7b-hf")
model = LLaMAForCausalLM.from_pretrained(
    "decapoda-research/llama-7b-hf",
    load_in_8bit=True,
    device_map="auto",
)
model = PeftModel.from_pretrained(model, "tloen/alpaca-lora-7b")

class InputPrompt(BaseModel):
    instruction: str
    input: Optional[str] = None

class OutputResponse(BaseModel):
    response: str

@app.post("/evaluate")
def evaluate(input_prompt: InputPrompt):
    temperature = 0.9
    generation_config = GenerationConfig(
        temperature=temperature,
        top_p=0.75,
        num_beams=1, do_sample=True
    )
    prompt = generate_prompt(input_prompt.instruction, input_prompt.input)
    inputs = tokenizer(prompt, return_tensors="pt")
    input_ids = inputs["input_ids"].cuda()
    generation_output = model.generate(
        input_ids=input_ids,
        generation_config=generation_config,
        return_dict_in_generate=True,
        output_scores=True,
        max_new_tokens=256
    )
    for s in generation_output.sequences:
        output = tokenizer.decode(s)
        return OutputResponse(response=output.split("### Response:")[1].strip())

def generate_prompt(instruction, input=None):
    if input:
        return f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{instruction}

### Input:
{input}

### Response:"""
    else:
        return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
{instruction}

### Response:"""


if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=7860)