app.py

import json
import numpy as np
from fastapi import FastAPI
from pydantic import BaseModel
from huggingface_hub import hf_hub_download
from sentence_transformers import SentenceTransformer

HF_USER = "ClergeF"
IMPACT_REPO = "impact-model"
IMPACT_FILE = "impact.json"

print("Loading embedder: all-MiniLM-L6-v2 …")
embedder = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")

def embed(text: str):
    return embedder.encode([text])[0]

def load_model():
    print(f"Loading {IMPACT_REPO}/{IMPACT_FILE}")
    path = hf_hub_download(
        repo_id=f"{HF_USER}/{IMPACT_REPO}",
        filename=IMPACT_FILE
    )
    with open(path, "r") as f:
        data = json.load(f)

        # 🔥 REMOVE unwanted fields BEFORE storing the model
        data.pop("matched_keyword", None)

        return data

def linear_predict(model_json, vec):
    coef = np.array(model_json["coef"])
    intercept = np.array(model_json["intercept"])
    return float(np.dot(coef, vec) + intercept)

print("Loading impact model...")
impact_model = load_model()
print("✔ Impact model loaded!")

app = FastAPI(title="Impact Rating API")

class InputText(BaseModel):
    text: str

@app.get("/")
def home():
    return {"status": "ok", "message": "Impact API running"}

@app.post("/rate")
def rate(payload: InputText):
    text = payload.text
    vec = embed(text)

    score = linear_predict(impact_model, vec)

    return {
        "input": text,
        "result": {
            "impact_score": score  # 🔥 cleaned output (NO matched_keyword)
        }
    }