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from pydantic import BaseModel |
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from .ConfigEnv import config |
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from fastapi.middleware.cors import CORSMiddleware |
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from happytransformer import HappyTextToText, TTSettings |
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from langchain.llms import Clarifai |
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from langchain.chains import LLMChain |
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from langchain.prompts import PromptTemplate |
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from TextGen import app |
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class Generate(BaseModel): |
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text:str |
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def generate_text(prompt: str): |
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if prompt == "": |
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return {"detail": "Please provide a prompt."} |
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else: |
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happy_tt = HappyTextToText("T5", "vennify/t5-base-grammar-correction") |
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args = TTSettings(num_beams=5, min_length=1, max_length=100000) |
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result = happy_tt.generate_text(f"grammar: {prompt}", args=args) |
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return result.text |
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app.add_middleware( |
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CORSMiddleware, |
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allow_origins=["*"], |
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allow_credentials=True, |
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allow_methods=["*"], |
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allow_headers=["*"], |
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) |
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@app.get("/", tags=["Home"]) |
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def api_home(): |
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return {'detail': 'Welcome to FastAPI TextGen Tutorial!'} |
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@app.post("/api/generate", tags=["Generate"], response_model=Generate) |
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def inference(input_prompt: str): |
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return generate_text(prompt=input_prompt) |
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