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from fastapi import FastAPI, Request, Form |
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from fastapi.responses import HTMLResponse |
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from fastapi.templating import Jinja2Templates |
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from fastapi.staticfiles import StaticFiles |
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from pipeline.inference_pipeline import predict_batch, load_model |
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import numpy as np |
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import ast |
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app = FastAPI() |
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templates = Jinja2Templates(directory="app/templates") |
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app.mount("/static", StaticFiles(directory="app/static"), name="static") |
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model = load_model() |
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@app.get("/", response_class=HTMLResponse) |
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async def form_get(request: Request): |
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return templates.TemplateResponse("index.html", {"request": request}) |
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@app.post("/", response_class=HTMLResponse) |
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async def form_post(request: Request, series: str = Form(...)): |
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try: |
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parsed_input = ast.literal_eval(series) |
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input_data = np.array(parsed_input, dtype=np.float32) |
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if len(input_data.shape) == 2: |
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input_data = np.expand_dims(input_data, axis=0) |
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preds = predict_batch(input_data, model) |
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return templates.TemplateResponse("index.html", {"request": request, "result": preds}) |
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except Exception as e: |
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return templates.TemplateResponse("index.html", {"request": request, "result": f"❌ Error: {e}"}) |
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