mock_model.py

import random

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
    }