src/streamlit_app.py

import streamlit as st
from groq import Groq
import re
from fpdf import FPDF
import datetime
import os

# ── Page Config ───────────────────────────────────
st.set_page_config(
    page_title = "InterviewGen AI",
    page_icon  = "🎯",
    layout     = "wide"
)

# ── Custom CSS ────────────────────────────────────
st.markdown("""
<style>
    .main-header {
        font-size: 2.8rem;
        font-weight: 900;
        background: linear-gradient(90deg, #667eea, #764ba2);
        -webkit-background-clip: text;
        -webkit-text-fill-color: transparent;
        text-align: center;
        padding: 1rem 0;
    }
    .question-card {
        background: #f8f9fa;
        border-left: 5px solid #667eea;
        padding: 1.2rem;
        margin: 0.8rem 0;
        border-radius: 10px;
        box-shadow: 0 2px 4px rgba(0,0,0,0.1);
    }
    .answer-card {
        background: linear-gradient(135deg, #e8f4f8, #f0fff4);
        border-left: 5px solid #2ecc71;
        padding: 1.2rem;
        margin: 0.8rem 0;
        border-radius: 10px;
    }
    .score-card {
        background: linear-gradient(135deg, #fff3cd, #ffeaa7);
        border-left: 5px solid #f39c12;
        padding: 1.2rem;
        margin: 0.8rem 0;
        border-radius: 10px;
    }
    .metric-card {
        background: linear-gradient(135deg, #667eea, #764ba2);
        color: white;
        padding: 1rem;
        border-radius: 10px;
        text-align: center;
    }
</style>
""", unsafe_allow_html=True)

# ── Groq Client ───────────────────────────────────
GROQ_API_KEY = os.environ.get("GROQ_API_KEY", "")
client       = Groq(api_key=GROQ_API_KEY)

# ── Helper Functions ──────────────────────────────
def generate_questions(role, difficulty, q_type, num, job_desc=""):
    job_context = f"Job Description: {job_desc[:500]}" if job_desc else ""
    prompt = f"""You are a senior technical interviewer at a top tech company.
{job_context}
Generate exactly {num} {difficulty} level {q_type} interview questions for a {role}.
Format EXACTLY like this:
Q1: [question]
A1: [detailed answer]

Q2: [question]
A2: [detailed answer]

Only output questions and answers. Nothing else."""

    response = client.chat.completions.create(
        model       = "llama-3.3-70b-versatile",
        messages    = [{"role": "user", "content": prompt}],
        temperature = 0.7,
        max_tokens  = 2000
    )
    return response.choices[0].message.content

def evaluate_answer(question, user_answer, correct_answer):
    prompt = f"""You are a technical interviewer evaluating a candidate answer.
Question: {question}
Candidate Answer: {user_answer}
Expected Answer: {correct_answer}

Evaluate the candidate answer and provide:
1. Score: X/10
2. Strengths: what they got right
3. Improvements: what they missed
4. Verdict: Pass/Fail

Be concise and professional."""

    response = client.chat.completions.create(
        model       = "llama-3.3-70b-versatile",
        messages    = [{"role": "user", "content": prompt}],
        temperature = 0.3,
        max_tokens  = 500
    )
    return response.choices[0].message.content

def parse_questions(text):
    qa_pairs = []
    blocks   = re.split(r"Q\d+:", text)
    blocks   = [b.strip() for b in blocks if b.strip()]
    for block in blocks:
        if re.search(r"A\d+:", block):
            parts    = re.split(r"A\d+:", block, maxsplit=1)
            question = parts[0].strip()
            answer   = parts[1].strip() if len(parts) > 1 else "N/A"
        else:
            question = block.strip()
            answer   = "N/A"
        qa_pairs.append({"question": question, "answer": answer})
    return qa_pairs

# ── Session State Init ────────────────────────────
if "history"         not in st.session_state: st.session_state.history         = []
if "total_generated" not in st.session_state: st.session_state.total_generated = 0
if "parsed_qa"       not in st.session_state: st.session_state.parsed_qa       = []
if "mock_index"      not in st.session_state: st.session_state.mock_index      = 0
if "mock_scores"     not in st.session_state: st.session_state.mock_scores     = []
if "mock_active"     not in st.session_state: st.session_state.mock_active     = False

# ── Header ────────────────────────────────────────
st.markdown("<p class=\'main-header\'>🎯 InterviewGen AI</p>", unsafe_allow_html=True)
st.markdown("<p style=\'text-align:center;color:gray;font-size:1.1rem;\'>Professional Interview Preparation Powered by LLaMA-3.3 & Groq</p>", unsafe_allow_html=True)
st.divider()

# ── Top Metrics ───────────────────────────────────
col1, col2, col3, col4 = st.columns(4)
with col1: st.metric("Questions Generated", st.session_state.total_generated)
with col2: st.metric("Sessions",            len(st.session_state.history))
with col3: st.metric("Mock Interviews",     len(st.session_state.mock_scores))
with col4:
    avg = sum(st.session_state.mock_scores) / len(st.session_state.mock_scores) if st.session_state.mock_scores else 0
    st.metric("Avg Mock Score", f"{avg:.1f}/10")

st.divider()

# ── Sidebar ───────────────────────────────────────
with st.sidebar:
    st.markdown("## Settings")

    role = st.selectbox(
        "Select Role",
        ["Python Developer", "Data Scientist",
         "Software Engineer", "ML Engineer",
         "DevOps Engineer", "Full Stack Developer",
         "Data Analyst", "Backend Developer",
         "Frontend Developer", "AI Engineer"]
    )
    difficulty = st.select_slider(
        "Difficulty Level",
        options=["Junior", "Mid-Level", "Senior"]
    )
    num_questions = st.slider(
        "Number of Questions",
        min_value=1, max_value=10, value=5
    )
    show_answers = st.toggle("Show Answers", value=True)

    st.divider()
    st.markdown("### Paste Job Description (Optional)")
    job_desc = st.text_area(
        "Job Description",
        placeholder="Paste job description here for targeted questions...",
        height=150
    )

# ── Tabs ──────────────────────────────────────────
tab1, tab2, tab3 = st.tabs([
    "📋 Generate Questions",
    "🎯 Mock Interview Mode",
    "📚 History"
])

# ════════════════════════════════════════════════
# TAB 1 — Generate Questions
# ════════════════════════════════════════════════
with tab1:
    q_type = st.radio(
        "Question Type",
        ["Technical", "Behavioral", "Mixed"],
        horizontal=True
    )

    generate_btn = st.button(
        "🚀 Generate Interview Questions",
        use_container_width=True
    )

    if generate_btn:
        with st.spinner("LLaMA-3.3 is generating questions..."):
            raw    = generate_questions(role, difficulty, q_type, num_questions, job_desc)
            parsed = parse_questions(raw)
            st.session_state.parsed_qa = parsed

        st.markdown(f"### {role} | {difficulty} | {q_type}")
        st.divider()

        questions = []
        answers   = []

        for i, qa in enumerate(parsed):
            q = qa["question"]
            a = qa["answer"]
            questions.append(q)
            answers.append(a)

            st.info(f"**Q{i+1}.** {q}")
            if show_answers:
                st.success(f"**Answer:** {a}")
            st.write("")

        st.session_state.total_generated += len(parsed)
        st.session_state.history.append({
            "time"      : datetime.datetime.now().strftime("%H:%M:%S"),
            "role"      : role,
            "difficulty": difficulty,
            "type"      : q_type,
            "questions" : questions,
            "answers"   : answers
        })

        # ── PDF Export ────────────────────────────
        try:
            pdf = FPDF()
            pdf.add_page()
            pdf.set_font("Arial", "B", 14)
            pdf.cell(190, 10, f"Interview Questions - {role}", ln=True, align="C")
            pdf.set_font("Arial", "", 9)
            pdf.cell(190, 8, f"Type: {q_type} | Difficulty: {difficulty}", ln=True, align="C")
            pdf.ln(4)
            for i, (q, a) in enumerate(zip(questions, answers)):
                q_c = q.encode("latin-1", "replace").decode("latin-1")
                a_c = a.encode("latin-1", "replace").decode("latin-1")
                pdf.set_font("Arial", "B", 10)
                pdf.multi_cell(190, 7, f"Q{i+1}. {q_c}")
                if show_answers:
                    pdf.set_font("Arial", "", 9)
                    pdf.multi_cell(190, 6, f"Answer: {a_c}")
                pdf.ln(2)
            pdf_path = "/tmp/interview_questions.pdf"
            pdf.output(pdf_path)
            with open(pdf_path, "rb") as f:
                st.download_button(
                    label     = "📥 Download as PDF",
                    data      = f,
                    file_name = f"interview_{role.replace(' ','_')}.pdf",
                    mime      = "application/pdf"
                )
        except Exception as e:
            st.warning(f"PDF error: {e}")

# ════════════════════════════════════════════════
# TAB 2 — Mock Interview Mode
# ════════════════════════════════════════════════
with tab2:
    st.markdown("### 🎯 Mock Interview Mode")
    st.markdown("Answer questions one by one — AI will evaluate your answers!")
    st.divider()

    if not st.session_state.parsed_qa:
        st.info("First generate questions in Tab 1, then come back here!")
    else:
        total_q = len(st.session_state.parsed_qa)
        idx     = st.session_state.mock_index

        if idx < total_q:
            current_qa = st.session_state.parsed_qa[idx]

            st.markdown(f"**Question {idx+1} of {total_q}**")
            st.progress((idx) / total_q)

            st.markdown(f"""
            <div class="question-card">
                <strong>Q{idx+1}. {current_qa["question"]}</strong>
            </div>
            """, unsafe_allow_html=True)

            user_answer = st.text_area(
                "Your Answer",
                placeholder="Type your answer here...",
                height=150,
                key=f"answer_{idx}"
            )

            col1, col2 = st.columns(2)
            with col1:
                submit_btn = st.button("Submit Answer", use_container_width=True)
            with col2:
                skip_btn = st.button("Skip Question", use_container_width=True)

            if submit_btn and user_answer:
                with st.spinner("AI is evaluating your answer..."):
                    evaluation = evaluate_answer(
                        current_qa["question"],
                        user_answer,
                        current_qa["answer"]
                    )

                st.markdown(f"""
                <div class="score-card">
                    <strong>AI Evaluation:</strong><br>{evaluation}
                </div>
                """, unsafe_allow_html=True)

                # Extract score
                score_match = re.search(r"(\d+)/10", evaluation)
                if score_match:
                    score = int(score_match.group(1))
                    st.session_state.mock_scores.append(score)

                st.session_state.mock_index += 1
                st.rerun()

            if skip_btn:
                st.session_state.mock_index += 1
                st.rerun()

        else:
            st.success("Mock Interview Complete!")
            if st.session_state.mock_scores:
                avg = sum(st.session_state.mock_scores) / len(st.session_state.mock_scores)
                st.markdown(f"### Your Final Score: {avg:.1f}/10")
                if avg >= 8:
                    st.balloons()
                    st.success("Excellent! You are ready for the interview!")
                elif avg >= 6:
                    st.warning("Good performance! A little more practice needed.")
                else:
                    st.error("Keep practicing! Review the answers carefully.")

            if st.button("Restart Mock Interview"):
                st.session_state.mock_index  = 0
                st.session_state.mock_scores = []
                st.rerun()

# ════════════════════════════════════════════════
# TAB 3 — History
# ════════════════════════════════════════════════
with tab3:
    st.markdown("### Question History")
    if not st.session_state.history:
        st.info("No history yet! Generate some questions first.")
    else:
        for session in reversed(st.session_state.history):
            with st.expander(f"{session['time']} - {session['role']} | {session['difficulty']} | {session['type']}"):
                for i, (q, a) in enumerate(zip(session["questions"], session["answers"])):
                    st.markdown(f"**Q{i+1}.** {q}")
                    if show_answers:
                        st.markdown(f"*A: {a[:200]}...*")
                    st.write("")