Update model.py

model.py

@@ -1,54 +1,31 @@
-# model.py
+import random
+from datetime import datetime
 
 def score_opportunity(data):
-    # Stage weights for scoring
-    stage_weight = {
-        "Prospecting": 10,
-        "Qualified": 20,
-        "Proposal": 30,
-        "Proposal/Price Quote": 35,
-        "Negotiation": 40,
-        "Closed Won": 50,
-        "Closed Lost": 0
-    }
-
-    lead_score = data.get("lead_score", 0)
-    email_score = min(10, data.get("emails_last_7_days", 0)) * 2    # up to 20
-    meeting_score = min(5, data.get("meetings_last_30_days", 0)) * 5  # up to 25
-    amount_score = min(data.get("amount", 0) / 1000, 25)              # up to 25
-    stage_score = stage_weight.get(data.get("stage"), 0)
+    # Simple scoring logic (replace with ML model in real app)
+    score = (
+        data["lead_score"] * 0.5 +
+        data["emails_last_7_days"] * 3 +
+        data["meetings_last_30_days"] * 5
+    )
 
-    # Total Score Calculation
-    total_score = lead_score * 0.25 + email_score + meeting_score + amount_score + stage_score
-    total_score = round(min(total_score, 100))
+    # Reduce score for long closing gap
+    today = datetime.today()
+    try:
+        close_date = datetime.strptime(data["close_date"], "%Y-%m-%d")
+        days_to_close = (close_date - today).days
+    except:
+        days_to_close = 30
 
-    # Confidence (0.0 to 1.0)
-    confidence = round(
-        min(1.0, (
-            (lead_score / 100) * 0.5 +
-            min(1, data.get("emails_last_7_days", 0) / 10) * 0.25 +
-            min(1, data.get("meetings_last_30_days", 0) / 5) * 0.25
-        )),
-        2
-    )
+    if days_to_close > 30:
+        score -= 10
+    elif days_to_close < 5:
+        score += 5
 
-    # Risk level and AI recommendation
-    if total_score >= 80:
-        risk = "Low"
-        recommendation = "🔥 Strong lead. Schedule final meeting or send proposal."
-    elif total_score >= 60:
-        risk = "Medium"
-        recommendation = "🗓️ Schedule another meeting before sending proposal."
-    elif total_score >= 40:
-        risk = "High"
-        recommendation = "📞 Reconnect with lead. Increase engagement."
-    else:
-        risk = "Very High"
-        recommendation = "⚠️ Low potential. Reassess or de-prioritize."
+    score = max(0, min(100, round(score)))
+    confidence = round(random.uniform(0.7, 0.95) if score >= 60 else random.uniform(0.4, 0.7), 2)
 
     return {
-        "score": total_score,
-        "confidence": confidence,
-        "risk": risk,
-        "recommendation": recommendation
+        "score": score,
+        "confidence": confidence
     }