import ollama
import json
import time
from parser import parse_resume

# --- Step 1: Parse Resume ---
resume_file = r"C:\Users\prana\Downloads\ABDM\Documents\PranavKerkar_resume.pdf"
parsed_data = parse_resume(resume_file)

# --- Step 2: Context for AI ---
context = f"""
You are an AI Interviewer.
Candidate's resume:
{json.dumps(parsed_data, indent=2)}

Rules:
- Start with: "Am I audible?"
- If 'no', retry politely.
- Ask questions based on resume (skills, education, projects).
- Wait 6–7 seconds before repeating if no response.
- If unanswered after 2 tries, mark 'Unanswered' and move on.
- End with: "Thank you for your time, results will be shared via email. Do you have any questions for me?"
"""

# --- Step 3: Helper Function for Rule-based Logic ---
def rule_based_response(user_input, parsed_data):
    """Simple rule-based interview flow"""

    # Greeting / Audible check
    if user_input in ["no", "not really", "can't hear"]:
        return "AI Interviewer: Let me try again, can you hear me now?"
    if user_input in ["yes", "yeah", "yep"]:
        return f"AI Interviewer: Great, how are you {parsed_data.get('name','there')}?"

    # Resume skills
    skills = parsed_data.get("skills", [])
    for skill in skills:
        if skill.lower() in user_input:
            return f"AI Interviewer: Since you mentioned {skill}, can you rate yourself 1–10 and explain why?"

    # Education based
    if "b.tech" in user_input or "bachelor" in user_input:
        return "AI Interviewer: Can you share a project you did during your Bachelor's?"

    if "m.tech" in user_input or "master" in user_input:
        return "AI Interviewer: What was your Master's thesis about?"

    # Projects fallback
    if "project" in user_input:
        return "AI Interviewer: Can you walk me through your most challenging project?"

    return None  # no rule → fallback to AI

# --- Step 4: Interview Loop ---
print("AI Interviewer: Am I audible?")

unanswered_count = 0
last_question = None

while True:
    candidate_input = input("Candidate: ").strip().lower()

    if candidate_input in ["exit", "quit", "bye"]:
        print("AI Interviewer: Thank you, goodbye.")
        break

    if candidate_input == "":
        if last_question and unanswered_count < 2:
            unanswered_count += 1
            print("AI Interviewer: (waiting 6 seconds...)")
            time.sleep(6)
            print("AI Interviewer: Let me repeat —", last_question)
            continue
        elif last_question and unanswered_count >= 2:
            print("AI Interviewer: Marking this question as 'Unanswered'. Moving on.")
            unanswered_count = 0
            last_question = None
            continue

    # 1. Rule-based first
    reply = rule_based_response(candidate_input, parsed_data)
    if reply:
        print(reply)
        last_question = reply.replace("AI Interviewer: ", "")
        unanswered_count = 0
        continue

    # 2. Fallback to Ollama AI
    response = ollama.chat(
        model="llama2",
        messages=[
            {"role": "system", "content": context},
            {"role": "user", "content": candidate_input}
        ]
    )

    interviewer_reply = response["message"]["content"]
    print("AI Interviewer:", interviewer_reply)
    last_question = interviewer_reply
    unanswered_count = 0