Upload app.py

app.py

@@ -11,7 +11,7 @@ from huggingface_hub import InferenceClient
 # CONFIG
 
 # You can hard-code the model here, or set MODEL_ID as a Space secret.
-MODEL_ID = os.environ.get("MODEL_ID")
+MODEL_ID = os.environ.get("MODEL_ID", "meta-llama/Meta-Llama-3.1-8B-Instruct")
 HF_TOKEN = os.environ.get("HF_TOKEN")  # optional for this public model
 
 REPO_PATH = "/data/questions.json"  # where we store generated questions
@@ -397,32 +397,33 @@ def normalize_output(
 
 def ai_generate(lang: str, category_key: str, variant: str) -> Dict[str, Any]:
     prompt = build_prompt(lang, category_key, variant)
-    raw_text = None
     try:
         raw_text = model_call(prompt)
-    except Exception:
-        raw_text = None
-
-    parsed = try_parse_json(raw_text) if raw_text else None
-
-    if parsed:
-        normalized = normalize_output(parsed, lang, category_key, variant)
-        return normalized
+        parsed = try_parse_json(raw_text) if raw_text else None
+
+        if parsed:
+            return normalize_output(parsed, lang, category_key, variant)
+
+        # If model replied but JSON is bad, show that in safety_notes
+        return {
+            "category": category_key,
+            "language": lang,
+            "questions": FEWSHOTS[lang][category_key]["questions"][:4],
+            "micro_actions": FEWSHOTS[lang][category_key]["micro_actions"][:2],
+            "tone": "fallback",
+            "safety_notes": f"Model replied but JSON parsing failed. raw_text starts with: {repr(raw_text[:120])}",
+        }
+    except Exception as e:
+        # Surface the error
+        return {
+            "category": category_key,
+            "language": lang,
+            "questions": FEWSHOTS[lang][category_key]["questions"][:4],
+            "micro_actions": FEWSHOTS[lang][category_key]["micro_actions"][:2],
+            "tone": "error",
+            "safety_notes": f"Model call error: {type(e).__name__}: {e}",
+        }
 
-    # fallback: shuffle fewshots
-    few = FEWSHOTS[lang][category_key]
-    q_pool = few["questions"][:]
-    m_pool = few["micro_actions"][:]
-    random.shuffle(q_pool)
-    random.shuffle(m_pool)
-    return {
-        "category": category_key,
-        "language": lang,
-        "questions": (q_pool + [""] * 4)[:4],
-        "micro_actions": (m_pool + [""] * 2)[:2],
-        "tone": "fallback",
-        "safety_notes": "",
-    }
 
 
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