Update app.py

app.py

@@ -5,55 +5,38 @@ from pydantic import BaseModel
 from huggingface_hub import hf_hub_download
 from sentence_transformers import SentenceTransformer
 
-# ============================================================
-# CONFIG
-# ============================================================
-
 HF_USER = "ClergeF"
 IMPACT_REPO = "impact-model"
 IMPACT_FILE = "impact.json"
 
-# ============================================================
-# EMBEDDER
-# ============================================================
-
 print("Loading embedder: all-MiniLM-L6-v2 …")
 embedder = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
 
 def embed(text: str):
     return embedder.encode([text])[0]
 
-# ============================================================
-# LOAD IMPACT MODEL
-# ============================================================
-
-def load_model(repo, filename):
-    print(f"Loading: {repo}/{filename}")
+def load_model():
+    print(f"Loading {IMPACT_REPO}/{IMPACT_FILE}")
     path = hf_hub_download(
-        repo_id=f"{HF_USER}/{repo}",
-        filename=filename,
-        revision="main",
-        force_download=True
+        repo_id=f"{HF_USER}/{IMPACT_REPO}",
+        filename=IMPACT_FILE
     )
     with open(path, "r") as f:
-        return json.load(f)
+        data = json.load(f)
 
-print("Loading impact model…")
-impact_model = load_model(IMPACT_REPO, IMPACT_FILE)
-print("✔ Impact model loaded!")
+        # 🔥 REMOVE unwanted fields BEFORE storing the model
+        data.pop("matched_keyword", None)
 
-# ============================================================
-# LINEAR PREDICT
-# ============================================================
+        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)
 
-# ============================================================
-# API
-# ============================================================
+print("Loading impact model...")
+impact_model = load_model()
+print("✔ Impact model loaded!")
 
 app = FastAPI(title="Impact Rating API")
 
@@ -69,19 +52,11 @@ def rate(payload: InputText):
     text = payload.text
     vec = embed(text)
 
-    try:
-        score = linear_predict(impact_model, vec)
-        result = {
-            "impact_score": score,
-            "matched_keyword": None
-        }
-    except Exception as e:
-        result = {
-            "impact_score": impact_model.get("default_score", 0),
-            "matched_keyword": None
-        }
+    score = linear_predict(impact_model, vec)
 
     return {
         "input": text,
-        "result": result
+        "result": {
+            "impact_score": score  # 🔥 cleaned output (NO matched_keyword)
+        }
     }