Create app.py

app.py

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+import os
+import json
+import numpy as np
+from fastapi import FastAPI
+from pydantic import BaseModel
+from sentence_transformers import SentenceTransformer
+from huggingface_hub import hf_hub_download
+
+# ============================================================
+# CONFIG
+# ============================================================
+
+REPO_USER = "ClergeF"
+
+MODEL_REPOS = {
+    "value_impact": "value-impact-model",
+    "impact": "impact-model",
+    "family": "family-model",
+    "community": "community-model",
+    "education": "education-model",
+    "health": "health-model",
+    "environment": "environment-model",
+    "business": "business-model",
+    "finance": "finance-model",
+    "history": "history-model",
+    "spirituality": "spirituality-model",
+    "innovation": "innovation-model",
+}
+
+# Embedder location in your HF repo:
+EMBEDDER_REPO = "MVT-models"
+EMBEDDER_SUBFOLDER = "universal_embedder"
+
+HF_TOKEN = os.environ.get("HF_TOKEN", None)
+
+# ============================================================
+# LOAD UNIVERSAL EMBEDDER
+# ============================================================
+
+print("Loading universal embedder from HuggingFace Hub...")
+
+embedder = SentenceTransformer(
+    f"{REPO_USER}/{EMBEDDER_REPO}",
+    subfolder=EMBEDDER_SUBFOLDER,
+    use_auth_token=HF_TOKEN
+)
+
+# ============================================================
+# MODEL LOADING HELPER
+# ============================================================
+
+def load_model(repo_name, filename):
+    """Download & load .json linear regression model from HF Hub."""
+    model_path = hf_hub_download(
+        repo_id=f"{REPO_USER}/{repo_name}",
+        filename=filename,
+        token=HF_TOKEN,
+    )
+    with open(model_path, "r") as f:
+        return json.load(f)
+
+print("Loading all 12 models from Hugging Face Hub...")
+
+models = {
+    "value_impact": load_model(MODEL_REPOS["value_impact"], "value_impact.json"),
+    "impact": load_model(MODEL_REPOS["impact"], "impact.json"),
+    "family": load_model(MODEL_REPOS["family"], "family_level.json"),
+    "community": load_model(MODEL_REPOS["community"], "community_level.json"),
+    "education": load_model(MODEL_REPOS["education"], "education_level.json"),
+    "health": load_model(MODEL_REPOS["health"], "health_level.json"),
+    "environment": load_model(MODEL_REPOS["environment"], "environment_level.json"),
+    "business": load_model(MODEL_REPOS["business"], "business_level.json"),
+    "finance": load_model(MODEL_REPOS["finance"], "finance_level.json"),
+    "history": load_model(MODEL_REPOS["history"], "history_level.json"),
+    "spirituality": load_model(MODEL_REPOS["spirituality"], "spirituality_level.json"),
+    "innovation": load_model(MODEL_REPOS["innovation"], "innovation_level.json"),
+}
+
+# ============================================================
+# FASTAPI
+# ============================================================
+
+app = FastAPI(title="MVT Community Value Model API")
+
+class InputText(BaseModel):
+    text: str
+
+# ============================================================
+# PREDICTION HELPERS
+# ============================================================
+
+def embed(text: str):
+    return embedder.encode([text])[0]
+
+def linear_predict(model_json, vec):
+    coef = np.array(model_json["coef"])
+    intercept = np.array(model_json["intercept"])
+
+    # Multi-output (value + impact)
+    if coef.ndim == 2:
+        return coef @ vec + intercept
+
+    # Single scalar output
+    return float(np.dot(coef, vec) + intercept)
+
+# ============================================================
+# API ROUTE
+# ============================================================
+
+@app.post("/predict")
+def predict(payload: InputText):
+    text = payload.text
+    vec = embed(text)
+
+    result = {}
+
+    # Two-output regression model
+    value_pred, impact_pred = linear_predict(models["value_impact"], vec)
+    result["estimated_value"] = float(value_pred)
+    result["impact_level"] = float(impact_pred)
+
+    # Individual category models
+    for key in [
+        "impact", "family", "community", "education", "health",
+        "environment", "business", "finance", "history",
+        "spirituality", "innovation"
+    ]:
+        result[key] = float(linear_predict(models[key], vec))
+
+    return {
+        "input": text,
+        "predictions": result
+    }