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from fastapi import FastAPI, Request |
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from fastapi.responses import HTMLResponse, PlainTextResponse |
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from fastapi.staticfiles import StaticFiles |
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from fastapi.templating import Jinja2Templates |
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import uvicorn |
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from transformers import pipeline |
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app = FastAPI() |
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app.mount("/static", StaticFiles(directory="static"), name="static") |
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templates = Jinja2Templates(directory="") |
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@app.get("/", response_class=HTMLResponse) |
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async def read_item(request: Request): |
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return templates.TemplateResponse("index.html", context={'request': request}) |
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@app.get("/{content}", response_class=PlainTextResponse) |
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async def read_item(request: Request, content: str): |
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return analyze_output(content) |
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@app.post("/{content}", response_class=PlainTextResponse) |
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async def read_item(request: Request, content: str): |
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return analyze_output(content) |
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def analyze_output(input: str, pipe = pipeline("text-classification", model="Titeiiko/OTIS-Official-Spam-Model")): |
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x = pipe(input)[0] |
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if x["label"] == "LABEL_0": |
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return str({"type":"Not Spam", "probability":x["score"]}) |
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else: |
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return str({"type":"Spam", "probability":x["score"]}) |
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if __name__ == "__main__": |
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uvicorn.run(app, host="0.0.0.0", port=8000) |
