from fastapi import FastAPI, Request
from fastapi.responses import HTMLResponse, PlainTextResponse
from fastapi.templating import Jinja2Templates
import uvicorn
import subprocess
PACKAGES = ["transformers", "accelerate", "torch"]
for package in PACKAGES:
    subprocess.run(["pip3", "install", package], check=True)
from transformers import pipeline

app = FastAPI()
templates = Jinja2Templates(directory="")

@app.get("/", response_class=HTMLResponse)
async def read_item(request: Request):
    return templates.TemplateResponse("index.html", context={'request': request})

@app.get("/{content}", response_class=PlainTextResponse)
async def read_item(request: Request, content: str):
    return analyze_output(content)

@app.post("/{content}", response_class=PlainTextResponse)
async def read_item(request: Request, content: str):
    return analyze_output(content)

def analyze_output(input: str, pipe = pipeline("text-classification", model="Titeiiko/OTIS-Official-Spam-Model")):
    x = pipe(input)[0]
    if x["label"] == "LABEL_0":
        return str({"type":"Not Spam", "probability":x["score"]})
    else:
        return str({"type":"Spam", "probability":x["score"]})
    
if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=7860)