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from fastapi import FastAPI
from pydantic import BaseModel
from huggingface_hub import InferenceClient
import uvicorn
app = FastAPI()
client = InferenceClient("FacebookAI/roberta-large-mnli")
class Item(BaseModel):
prompt: str
#history: list
#system_prompt: str
#temperature: float = 0.0
#max_new_tokens: int = 1048
#top_p: float = 0.15
#repetition_penalty: float = 1.0
#trust_remote_code = True
#def format_prompt(message, history):
# prompt = "<s>"
# for user_prompt, bot_response in history:
# prompt += f"[INST] {user_prompt} [/INST]"
# prompt += f" {bot_response}</s> "
# prompt += f"[INST] {message} [/INST]"
# return prompt
def generate(item: Item):
#temperature = float(item.temperature)
#if temperature < 1e-2:
# temperature = 1e-2
#top_p = float(item.top_p)
#generate_kwargs = dict(
# temperature=temperature,
# max_new_tokens=item.max_new_tokens,
# top_p=top_p,
# repetition_penalty=item.repetition_penalty,
# do_sample=True,
# seed=42,
# )
#formatted_prompt = format_prompt(f"{item.system_prompt}, {item.prompt}", item.history)
#text = format_prompt(f"{item.system_prompt}, {item.prompt}", item.history)
text = item.prompt
print(text)
labels = ["Requirement", "Information"]
print(labels)
stream = client.zero_shot_classification(text, labels)
print("Stream: " + stream)
#stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
output = ""
for response in stream:
output += response.token.text
return output
@app.post("/generate/")
async def generate_text(item: Item):
return {"response": generate(item)}
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