EnzyMax-AI_API/main.py at main · IAyaanHere/EnzyMax-AI_API · GitHub

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import os
import uvicorn
from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel

from biomass_yield_predictor import predict_yield, load_model

app = FastAPI(title="EnzyMax AI API (Production)")

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Global model variables
g_bundle = None
x_bundle = None

@app.on_event("startup")
def load_models():
    global g_bundle, x_bundle
    print("Loading ML models for Production...")
    try:
        g_bundle = load_model(os.path.join('outputs', 'glucose_model.pkl'))
        print("✅ Glucose model loaded.")
    except Exception as e:
        print(f"⚠️ Glucose model error: {e}")

    try:
        x_bundle = load_model(os.path.join('outputs', 'xylose_model.pkl'))
        print("✅ Xylose model loaded.")
    except Exception as e:
        print(f"⚠️ Xylose model error: {e}")

class BiomassInput(BaseModel):
    Glucan: float
    Hemi: float
    Lignin: float
    Moisture: float
    PreTemp: float
    PreTime: float
    HydTemp: float
    HydTime: float
    pH: float
    EnzFPU: float
    EnzMg: float
    SevFactor: float
    Pre_treatment: str
    Feed: str

@app.get("/")
def home():
    return {"message": "EnzyMax API is Live on Hugging Face Spaces! 🚀"}

@app.post("/predict")
def predict(data: BiomassInput):
    try:
        input_dict = {
            'Glucan Content (% dry basis)': data.Glucan,
            'Hemicellulose Content (% dry basis)': data.Hemi,
            'Lignin Content (% dry basis)': data.Lignin,
            'Moisture Content (kg water/kg dry biomass)': data.Moisture,
            'Pretreatment Temperature (°C)': data.PreTemp,
            'Pretreatment Time (h)': data.PreTime,
            'Conversion Temperature (°C)': data.HydTemp,
            'Hydrolysis Time(h)': data.HydTime,
            'pH': data.pH,
            'Enzyme loading (FPU/g dry biomass)': data.EnzFPU,
            'Enzyme loading (mg / g glucan)': data.EnzMg,
            'Severity Factor': data.SevFactor,
            'Pre-treatment': data.Pre_treatment,
            'Feed': data.Feed
        }

        # Prediction function
        result = predict_yield(input_dict, _g_bundle=g_bundle, _x_bundle=x_bundle)

        return {
            "success": True,
            "glucose_yield_pct": result["glucose_yield_pct"],
            "xylose_yield_pct": result["xylose_yield_pct"],
            "note": result["note"]
        }

    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))


if __name__ == "__main__":
    # Hugging Face Spaces defaults to port 7860
    port = int(os.environ.get("PORT", 7860))
    uvicorn.run("main:app", host="0.0.0.0", port=port)