Rename mock_model.py to scorer.py

mock_model.py

@@ -1,48 +0,0 @@
-import random
-
-# New function: stage-based lead score generator
-def get_lead_score_for_stage(stage):
-    stage_scores = {
-        "Prospecting": (20, 40),
-        "Proposal/Price Quote": (50, 70),
-        "Negotiation": (70, 90),
-        "Closed Won": (90, 100),
-        "Closed Lost": (10, 30)
-    }
-    return random.randint(*stage_scores.get(stage, (30, 70)))
-
-def generate_recommendation(stage, emails, meetings):
-    if stage == "Negotiation":
-        return "Follow up with final pricing discussion."
-    elif stage == "Proposal/Price Quote":
-        return "Schedule one more meeting to present value."
-    elif meetings == 0:
-        return "Schedule a meeting to initiate engagement."
-    elif emails < 2:
-        return "Send follow-up email to re-engage."
-    else:
-        return "Continue monitoring engagement signals."
-
-def predict(data, model, tokenizer, summarizer):
-    score = random.randint(50, 95)
-    confidence = round(random.uniform(0.65, 0.95), 2)
-
-    if score >= 75:
-        risk = "Low"
-    elif score >= 55:
-        risk = "Medium"
-    else:
-        risk = "High"
-
-    recommendation = generate_recommendation(
-        data["stage"],
-        data["emails_last_7_days"],
-        data["meetings_last_30_days"]
-    )
-
-    return {
-        "score": score,
-        "confidence": confidence,
-        "risk": risk,
-        "recommendation": recommendation
-    }

scorer.py

@@ -0,0 +1,35 @@
+import random
+
+def get_lead_score(stage, emails, meetings, close_gap):
+    base = {
+        "Prospecting": (20, 40),
+        "Proposal/Price Quote": (50, 70),
+        "Negotiation": (70, 90),
+        "Closed Won": (90, 100),
+        "Closed Lost": (10, 30)
+    }
+    low, high = base.get(stage, (30, 70))
+    engagement_bonus = min(emails + meetings, 5)
+    urgency_bonus = 5 if close_gap < 14 else 0
+    return min(100, random.randint(low, high) + engagement_bonus + urgency_bonus)
+
+def calculate_score(lead_score, emails, meetings, close_gap):
+    activity_weight = min((emails * 2 + meetings * 3), 25)
+    deadline_weight = max(0, 10 - int(close_gap / 5))
+    return min(100, lead_score + activity_weight + deadline_weight - random.randint(5, 15))
+
+def calculate_confidence(score):
+    if score > 85:
+        return round(random.uniform(0.90, 0.97), 2)
+    elif score > 65:
+        return round(random.uniform(0.80, 0.89), 2)
+    else:
+        return round(random.uniform(0.65, 0.79), 2)
+
+def calculate_risk(score, confidence, emails, meetings):
+    if score > 80 and confidence > 0.9:
+        return "Low"
+    elif score > 60 or (emails + meetings) >= 4:
+        return "Medium"
+    else:
+        return "High"