update

main.py

@@ -30,7 +30,7 @@ class RerunRequest(BaseModel):
     intent: Literal["light", "medium", "deep"]
 
 INTENT_LIMITS = {
-    "light":  {"per_query": 20,  "total": 20},
+    "light":  {"per_query": 10,  "total": 25},
     "medium": {"per_query": 50,  "total": 300},
     "deep":   {"per_query": 100, "total": 800},
 }

processor.py

@@ -163,32 +163,30 @@ def text_matches_any(text, patterns):
 def determine_nature(text, sentiment_label):
     t = (text or "").lower()
     # 1. High-priority flags (dangerous or specific categories)
-    if text_matches_any(t, SEPARATIST_RE): return "Separatist"
-    if text_matches_any(t, CALL_TO_ACTION_RE): return "Call-to-Action"
-    if text_matches_any(t, COMMUNAL_RE): return "Communal"
-    if text_matches_any(t, CONSPIRACY_RE): return "Conspiratorial"
+    if text_matches_any(t, SEPARATIST_RE): return "separatist"
+    if text_matches_any(t, CALL_TO_ACTION_RE): return "call-to-action"
+    if text_matches_any(t, COMMUNAL_RE): return "communal"
+    if text_matches_any(t, CONSPIRACY_RE): return "conspiratorial"
 
     # 2. Trust the advanced model's label if available
     s = str(sentiment_label)
-    # The sentiment labels are Title-Cased (Pro-India, Anti-India, etc.)
-    # We return them as-is or ensure they match the nature output convention.
-    if s == "Pro-India": return "Pro-India"
-    if s == "Anti-India": return "Anti-India"
-    if s == "Pro-Government": return "Pro-Government"
-    if s == "Anti-Government": return "Anti-Government"
+    if s == "Pro-India": return "pro-india"
+    if s == "Anti-India": return "anti-india"
+    if s == "Pro-Government": return "pro-government"
+    if s == "Anti-Government": return "anti-government"
 
     # 3. Fallback to Regex for other cases or if model was Neutral
-    if text_matches_any(t, ANTI_INDIA_RE): return "Anti-India"
-    if text_matches_any(t, PRO_INDIA_RE): return "Pro-India"
-    if text_matches_any(t, CRITICAL_GOVT_RE): return "Critical-of-Government"
-    if text_matches_any(t, SUPPORT_OPPOSITION_RE): return "Supportive-of-Opposition"
+    if text_matches_any(t, ANTI_INDIA_RE): return "anti-india"
+    if text_matches_any(t, PRO_INDIA_RE): return "pro-india"
+    if text_matches_any(t, CRITICAL_GOVT_RE): return "critical-of-government"
+    if text_matches_any(t, SUPPORT_OPPOSITION_RE): return "supportive-of-opposition"
 
     # 4. Fallback to generic POS/NEG (legacy)
     s_upper = s.upper()
-    if "POS" in s_upper: return "Supportive"
-    if "NEG" in s_upper: return "Critical"
+    if "POS" in s_upper: return "supportive"
+    if "NEG" in s_upper: return "critical"
     
-    return "Neutral"
+    return "neutral"
 
 # ---------------- DANGEROUS FLAG ----------------
 danger_keywords = ["kill","attack","bomb","violence","terror","terrorist","militant",
@@ -197,8 +195,8 @@ pattern = re.compile(r'\b(?:' + '|'.join(map(re.escape, danger_keywords)) + r')\
                       flags=re.IGNORECASE)
 
 def is_dangerous(text, sentiment):
-    if pattern.search(text or ""): return True
-    return (str(sentiment).upper() == "NEGATIVE" and text.strip() != "")
+    # if pattern.search(text or ""): return True
+    return (str(sentiment).upper() == "ANTI-INDIA" and text.strip() != "")
 
 def generate_reports_from_csv(input_csv:str, out_dir:str) -> dict:
     """
@@ -277,9 +275,9 @@ def generate_reports_from_csv(input_csv:str, out_dir:str) -> dict:
         
         # Handle error or valid result
         if "error" in out:
-            preds.append(("Neutral", 0.0))
+            preds.append(("NEUTRAL", 0.0))
         else:
-            label = out.get("label", "Neutral")
+            label = out.get("label", "NEUTRAL")
             score = float(out.get("confidence", 0.0))
             preds.append((label, score))