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from pydantic import BaseModel |
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from fastapi.middleware.cors import CORSMiddleware |
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from langchain.chains import LLMChain |
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from langchain.prompts import PromptTemplate |
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from langchain_google_genai import ( |
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ChatGoogleGenerativeAI, |
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HarmBlockThreshold, |
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HarmCategory, |
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) |
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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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prompt = PromptTemplate(template=prompt, input_variables=['Prompt']) |
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llm = ChatGoogleGenerativeAI( |
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model="gemini-pro", |
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safety_settings={ |
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HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE, |
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}, |
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) |
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llmchain = LLMChain( |
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prompt=prompt, |
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llm=llm |
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) |
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llm_response = llmchain.run({"Prompt": prompt}) |
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return Generate(text=llm_response) |
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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", summary="Generate text from prompt", tags=["Generate"], response_model=Generate) |
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def inference(input_prompt: str): |
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return generate_text(prompt=input_prompt) |