Update utilities.py

utilities.py

@@ -1,21 +1,19 @@
-def preprocess_features(data):
-    # Example of building a simple structured representation
-    text = (
-        f"Industry: {data.get('industry')}. "
-        f"Stage: {data.get('stage')}. "
-        f"Amount: {data.get('amount')} USD. "
-        f"Lead Score: {data.get('lead_score')}. "
-        f"{data.get('emails_last_7_days', 0)} emails in last 7 days. "
-        f"{data.get('meetings_last_30_days', 0)} meetings in last 30 days."
+def prepare_text_input(data):
+    return (
+        f"Stage: {data.get('stage')}, "
+        f"Industry: {data.get('industry')}, "
+        f"Lead Score: {data.get('lead_score')}, "
+        f"Emails: {data.get('emails_last_7_days')}, "
+        f"Meetings: {data.get('meetings_last_30_days')}, "
+        f"Amount: {data.get('amount')}, "
+        f"Close Gap: {data.get('close_date_gap')} days"
     )
-    return {"text": text}
 
-def generate_recommendation(summarizer, data):
-    prompt = (
-        f"Generate a next action for a deal at stage {data['stage']} with "
-        f"{data['emails_last_7_days']} emails and {data['meetings_last_30_days']} meetings."
-    )
-    input_ids = summarizer.tokenizer(prompt, return_tensors="pt").input_ids
-    output_ids = summarizer.generate(input_ids, max_new_tokens=30)
-    summary = summarizer.tokenizer.decode(output_ids[0], skip_special_tokens=True)
-    return summary
+def generate_recommendation(data):
+    stage = data.get("stage", "").lower()
+    if stage == "negotiation":
+        return "Follow up with final pricing discussion."
+    elif stage == "proposal/price quote":
+        return "Schedule one more meeting to present value."
+    else:
+        return "Send an email to re-engage the lead."