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from pydantic import BaseModel
from .ConfigEnv import config
from fastapi.middleware.cors import CORSMiddleware
from langchain.llms import Clarifai
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from TextGen import app
class Generate(BaseModel):
text:str
def generate_text(prompt: str):
if prompt == "":
return {"detail": "Please provide a prompt."}
else:
prompt = PromptTemplate(template=prompt, input_variables=['Prompt'])
llm = Clarifai(
pat = config.CLARIFAI_PAT,
user_id = config.USER_ID,
app_id = config.APP_ID,
model_id = config.MODEL_ID,
model_version_id=config.MODEL_VERSION_ID,
)
llmchain = LLMChain(
prompt=prompt,
llm=llm
)
llm_response = llmchain.run({"Prompt": prompt})
return Generate(text=llm_response)
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
@app.get("/", tags=["Home"])
def api_home():
return {'detail': 'Welcome to FastAPI TextGen Tutorial!'}
API_URL = "https://api-inference.huggingface.co/models/google/gemma-1.1-7b-it"
headers = {"Authorization": f"Bearer {config.HF_TOKEN}"}
def query(payload):
response = requests.post(API_URL, headers=headers, json=payload)
return response.json()
@app.get("/query", tags=["Query"])
def api_query(input: str):
return {'input': input}
return query({"inputs": input,"parameters":{"return_full_text":False,"max_length":1024}})
@app.post("/api/generate", summary="Generate text from prompt", tags=["Generate"], response_model=Generate)
def inference(input_prompt: str):
return generate_text(prompt=input_prompt) |