Update model.py

model.py

@@ -1,33 +1,52 @@
 # model.py
 
 def score_opportunity(data):
-    # Weighted score calculation
-    raw_score = (
-        0.3 * (data['lead_score'] or 0) +
-        0.2 * (data['email_count'] or 0) +
-        0.2 * (data['meeting_count'] or 0) -
-        0.1 * (data['close_date_gap'] or 0)
+    # Stage weights
+    stage_weights = {
+        "Prospecting": 0.1,
+        "Qualified": 0.2,
+        "Proposal/Price Quote": 0.3,
+        "Proposal": 0.3,
+        "Negotiation": 0.4,
+        "Closed Won": 0.5,
+        "Closed Lost": 0.0
+    }
+
+    # Base scoring formula
+    score = (
+        0.25 * (data.get('lead_score') or 0) +
+        0.2 * (data.get('emails_last_7_days') or 0) * 5 +
+        0.2 * (data.get('meetings_last_30_days') or 0) * 10 +
+        0.15 * (data.get('amount') or 0) / 10000 +
+        0.2 * stage_weights.get(data.get('stage'), 0)
     )
-    # Extra points if at negotiation stage
-    if data['stage'] == "Negotiation":
-        raw_score += 10
 
-    # Clip score between 0–100
-    raw_score = max(0, min(100, round(raw_score)))
+    score = min(100, round(score))
+
+    # Confidence score between 0 and 1
+    confidence = round(
+        min(1.0, (
+            (data.get('lead_score') or 0) / 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
+    )
 
-    # Risk classification logic
-    if raw_score >= 75:
+    # Risk and recommendation
+    if score >= 80:
         risk = "Low"
-        recommendation = "✅ High chance of closing. Prioritize this deal."
-    elif raw_score >= 50:
+        recommendation = "🔥 Strong lead. Proceed with final proposal or close."
+    elif score >= 60:
         risk = "Medium"
-        recommendation = "🔁 Moderate potential. Consider a follow-up soon."
+        recommendation = "🗓️ Schedule another meeting before sending proposal."
     else:
         risk = "High"
         recommendation = "⚠️ Low potential. Reassess or de-prioritize."
 
     return {
-        "score": raw_score,
+        "score": score,
+        "confidence": confidence,
         "risk": risk,
         "recommendation": recommendation
     }