Main Code Backup.md

Main Code Backup:

import gradio as gr

import requests

import json

import datetime

import os

OLLAMA_URL = "http://localhost:11434/api/generate"

HISTORY_FILE = "interview_history.json"

# ── Persistent History Helpers ─────────────────────────────────────────────────

def load_history():

"""Load history from JSON file on disk"""

if os.path.exists(HISTORY_FILE):

with open(HISTORY_FILE, "r") as f:

return json.load(f)

return []

def save_history(history):

"""Save history to JSON file on disk"""

with open(HISTORY_FILE, "w") as f:

json.dump(history, f, indent=2)

# ── Ollama helper ──────────────────────────────────────────────────────────────

def ask_ollama(prompt, model="mistral:7b", temperature=0.7):

try:

payload = {

"model": model,

"prompt": prompt,

"stream": False,

"temperature": temperature

}

r = requests.post(OLLAMA_URL, json=payload, timeout=300)

return r.json().get("response", "No response")

except Exception as e:

return f"❌ Error: {str(e)}"

# ── Question generation ────────────────────────────────────────────────────────

QUESTION_PROMPTS = [

"Generate ONE interview question about the candidate's most recent project experience. Question:",

"Generate ONE follow-up interview question about the specific technologies or tools used. Question:",

"Generate ONE interview question about a challenge they faced and how they overcame it. Question:",

]

def clean_question(raw):

if "Question:" in raw:

raw = raw.split("Question:")[-1].strip()

if "?" in raw:

raw = raw.split("?")[0] + "?"

return raw.strip()

def generate_all_questions(job_desc, history_state):

if not job_desc or len(job_desc.strip()) < 20:

return "Please paste a job description (20+ characters).", "", "Ready", history_state, gr.update()

job_desc = job_desc[:500]

questions = []

for p in QUESTION_PROMPTS:

full_prompt = f"Based on this job description:\n{job_desc}\n\n{p}"

q = clean_question(ask_ollama(full_prompt, temperature=0.8))

questions.append(q)

session = {

"timestamp": datetime.datetime.now().strftime("%Y-%m-%d %H:%M"),

"job_snippet": job_desc[:60] + "...",

"questions": questions,

"answers": ["", "", ""],

"scores": ["", "", ""],

"numeric_scores": [],

}

history_state = history_state or []

history_state.append(session)

save_history(history_state) # 💾 Save to disk

tips_md = build_tips(job_desc)

return questions[0], "0", "Question 1 / 3", history_state, gr.update(value=tips_md)

# ── Answer scoring ─────────────────────────────────────────────────────────────

def score_answer(answer, q_index_str, history_state):

if not answer or len(answer.strip()) < 15:

return "Please write a longer answer (15+ characters).", history_state

idx = int(q_index_str) if q_index_str else 0

answer = answer[:500]

prompt = f"""You are a strict interview coach evaluating a candidate's spoken interview answer.

Candidate's answer: {answer}

STEP 1 — Relevance check:

Is this a genuine attempt at answering an interview question?

It is NOT relevant if it is: random text, mix of random text, code, gibberish, a single word, copy-pasted content, or completely off-topic, only partially meaningful or relevant.

STEP 2 -

If NOT relevant, respond with ONLY these two lines and nothing else:

Relevant: NO

Score: NIL/10

Warning: ⚠️ Irrelevant response detected. Please answer the interview question properly using the STAR format.

If it IS a genuine interview answer, respond with ALL of the following lines and NOTHING else:

Relevant: YES

Score: X/10

Strength: (one sentence about what was done well)

Weakness: (one sentence about the biggest gap)

Fix: (one specific, actionable improvement the candidate can make to their spoken answer — no code)"""

feedback = ask_ollama(prompt, temperature=0.5)

# Save to history

if history_state:

last = history_state[-1]

if idx < len(last["answers"]):

last["answers"][idx] = answer[:80] + "..."

for line in feedback.splitlines():

if line.startswith("Score:"):

score_str = line.replace("Score:", "").strip()

last["scores"][idx] = score_str

# Extract numeric score

try:

numeric = float(score_str.split("/")[0].strip())

last["numeric_scores"].append(numeric)

except:

pass

break

history_state[-1] = last

save_history(history_state) # 💾 Save to disk

return feedback, history_state

# ── Navigation ─────────────────────────────────────────────────────────────────

def next_question(q_index_str, answer, history_state):

"""Move to next question, returning previous Q+A for the review panel."""

idx = int(q_index_str) if q_index_str else 0

if not history_state:

return "Start an interview first.", "", str(idx), "No session", history_state, "", "", ""

session = history_state[-1]

questions = session["questions"]

next_idx = idx + 1

# Capture what the user just answered (for the prev panel)

prev_q = questions[idx]

prev_a = answer or "(no answer given)"

prev_score = session["scores"][idx] if session["scores"][idx] else "(no feedback yet)"

if next_idx >= len(questions):

session_log = render_session_log(session, up_to=idx)

return (

"✅ All 3 questions complete! Check your history.",

"", # clear answer box

str(idx),

"Interview Complete 🎉",

history_state,

prev_q,

prev_a,

session_log,

)

session_log = render_session_log(session, up_to=idx)

return (

questions[next_idx],

"", # clear answer box

str(next_idx),

f"Question {next_idx + 1} / 3",

history_state,

prev_q,

prev_a,

session_log,

)

def render_session_log(session, up_to):

"""Render completed Q+A+Score pairs for the current session (up to index up\_to)."""

if up_to < 0:

return "No completed questions yet."

lines = []

for i in range(up_to + 1):

q = session["questions"][i]

a = session["answers"][i] or "(no answer saved)"

sc = session["scores"][i] or "(no feedback yet)"

lines.append(f"**Q{i+1}:** {q}")

lines.append(f"*Your answer:* {a}")

lines.append(f"*Score:* {sc}")

lines.append("")

return "\n".join(lines)

# ── History rendering ──────────────────────────────────────────────────────────

def compute_stats(history):

"""Compute overall stats across all sessions"""

all_scores = []

for s in history:

for score in s.get("numeric_scores", []):

all_scores.append(score)

if not all_scores:

return None

avg = sum(all_scores) / len(all_scores)

best = max(all_scores)

# Trend: compare first half vs second half

trend = ""

if len(all_scores) >= 4:

mid = len(all_scores) // 2

first_avg = sum(all_scores[:mid]) / mid

second_avg = sum(all_scores[mid:]) / (len(all_scores) - mid)

diff = second_avg - first_avg

if diff > 0.5:

trend = "📈 Improving!"

elif diff < -0.5:

trend = "📉 Declining — practice more"

else:

trend = "➡️ Consistent"

return {

"total_sessions": len(history),

"total_answers": len(all_scores),

"avg_score": round(avg, 1),

"best_score": round(best, 1),

"trend": trend

}

def render_history(history_state):

"""Render full history with stats"""

history = history_state or []

# Load from disk too (in case state and disk diverge)

disk_history = load_history()

if len(disk_history) > len(history):

history = disk_history

if not history:

return "No sessions yet. Start your first interview above!"

lines = []

# Stats block

stats = compute_stats(history)

if stats:

lines.append("## 📊 Your Progress")

lines.append(f"| Metric | Value |")

lines.append(f"|--------|-------|")

lines.append(f"| Total Sessions | {stats['total_sessions']} |")

lines.append(f"| Total Answers | {stats['total_answers']} |")

lines.append(f"| Average Score | {stats['avg_score']}/10 |")

lines.append(f"| Best Score | {stats['best_score']}/10 |")

if stats['trend']:

lines.append(f"| Trend | {stats['trend']} |")

lines.append("")

# Sessions

lines.append("## 📝 Session History")

for i, s in enumerate(reversed(history), 1):

scores_display = " | ".join(s["scores"]) if any(s["scores"]) else "No feedback yet"

avg = ""

if s.get("numeric_scores"):

avg = f" · Avg: {round(sum(s['numeric_scores'])/len(s['numeric_scores']), 1)}/10"

lines.append(f"### Session {len(history) - i + 1} — {s['timestamp']}{avg}")

lines.append(f"**Role:** {s['job_snippet']}")

lines.append(f"**Scores:** {scores_display}")

for j, (q, a, sc) in enumerate(zip(s["questions"], s["answers"], s["scores"]), 1):

lines.append(f"\n### **Q{j}:** {q}\n")

if a:

lines.append(f"**Answer:** {a}\n")

if sc:

lines.append(f"**Score:** {sc}\n")

lines.append("\n---")

return "\n".join(lines)

# ── Tips & Resources ───────────────────────────────────────────────────────────

TIPS_DB = {

"python": {

"label": "Python / Backend",

"leetcode": [

("Two Sum", "https://leetcode.com/problems/two-sum/", "Easy"),

("LRU Cache", "https://leetcode.com/problems/lru-cache/", "Medium"),

("Word Search II", "https://leetcode.com/problems/word-search-ii/", "Hard"),

],

"concepts": ["OOP principles", "Decorators & generators", "Async / await", "REST API design"],

},

"react": {

"label": "React / Frontend",

"leetcode": [

("Valid Parentheses", "https://leetcode.com/problems/valid-parentheses/", "Easy"),

("Flatten Nested List", "https://leetcode.com/problems/flatten-nested-list-iterator/", "Medium"),

],

"concepts": ["Virtual DOM", "Hooks & state management", "Component lifecycle", "Web performance"],

},

"machine learning": {

"label": "Machine Learning",

"leetcode": [

("Find Peak Element", "https://leetcode.com/problems/find-peak-element/", "Medium"),

("Kth Largest Element", "https://leetcode.com/problems/kth-largest-element-in-an-array/", "Medium"),

],

"concepts": ["Bias-variance tradeoff", "Overfitting & regularisation", "Gradient descent", "Model evaluation metrics"],

},

"sql": {

"label": "SQL / Databases",

"leetcode": [

("Employees Earning More Than Manager", "https://leetcode.com/problems/employees-earning-more-than-their-managers/", "Easy"),

("Department Top 3 Salaries", "https://leetcode.com/problems/department-top-three-salaries/", "Hard"),

],

"concepts": ["JOINs & subqueries", "Indexing strategies", "Transactions & ACID", "Query optimisation"],

},

}

DEFAULT_TIPS = {

"label": "General Software Engineering",

"leetcode": [

("Two Sum", "https://leetcode.com/problems/two-sum/", "Easy"),

("Merge Intervals", "https://leetcode.com/problems/merge-intervals/", "Medium"),

("Trapping Rain Water", "https://leetcode.com/problems/trapping-rain-water/", "Hard"),

],

"concepts": ["STAR answer format", "System design basics", "Time & space complexity", "Behavioural questions"],

}

def build_tips(job_desc=""):

jd_lower = job_desc.lower()

matched = DEFAULT_TIPS

for key, data in TIPS_DB.items():

if key in jd_lower:

matched = data

break

lc_rows = "\n".join(

f"| [{p}]({url}) | {diff} |"

for p, url, diff in matched["leetcode"]

)

concept_rows = "\n".join(f"- ✅ {c}" for c in matched["concepts"])

return f"""## 🎯 Tips for: {matched['label']}

### 📚 Key Concepts to Revise

{concept_rows}

### 💻 Recommended LeetCode Problems

| Problem | Difficulty |

|---------|-----------|

{lc_rows}

### 🧠 General Interview Advice

- Use the **STAR format** (Situation → Task → Action → Result) for behavioural questions

- Always quantify impact: *"reduced load time by 40%"* beats *"made it faster"*

- It's okay to take 30 seconds to think before answering

- Ask clarifying questions — interviewers reward curiosity

### 🔗 Useful Resources

- [Grokking the System Design Interview](https://www.educative.io/courses/grokking-the-system-design-interview)

- [NeetCode Roadmap](https://neetcode.io/roadmap)

- [Tech Interview Handbook](https://www.techinterviewhandbook.org/)

- [Pramp — Free Mock Interviews](https://www.pramp.com/)

"""

# ── Custom CSS ─────────────────────────────────────────────────────────────────

CUSTOM_CSS = """

@import url('https://fonts.googleapis.com/css2?family=Google+Sans:wght@400;500;700\&display=swap');

* { font-family: 'Google Sans', 'Product Sans', sans-serif !important; }

.gr-button-primary {

background: linear-gradient(135deg, #6366f1, #8b5cf6) !important;

border: none !important;

color: white !important;

font-weight: 600 !important;

transition: transform 0.15s, box-shadow 0.15s !important;

}

.gr-button-primary:hover {

transform: translateY(-2px) !important;

box-shadow: 0 6px 20px rgba(99,102,241,0.4) !important;

}

.gr-button-secondary {

border: 2px solid #6366f1 !important;

color: #6366f1 !important;

font-weight: 600 !important;

transition: all 0.15s !important;

}

.gr-button-secondary:hover {

background: #6366f1 !important;

color: white !important;

}

#progress_box textarea {

font-weight: 700 !important;

font-size: 1rem !important;

color: #6366f1 !important;

text-align: center !important;

}

#feedback_box textarea {

font-size: 0.95rem !important;

line-height: 1.6 !important;

}

.tab-nav button { font-weight: 600 !important; }

/* ── Animated blob background ── */

body {

background: #0d0d1f !important;

}

gradio-app {

background: transparent !important;

}

.gradio-container {

background: transparent !important;

}

"""

# ── Build UI ───────────────────────────────────────────────────────────────────

with gr.Blocks(

title="AI Interview Coach",

theme=gr.themes.Soft(

primary_hue=gr.themes.colors.violet,

secondary_hue=gr.themes.colors.purple,

neutral_hue=gr.themes.colors.slate,

font=[gr.themes.GoogleFont("Google Sans"), "ui-sans-serif", "sans-serif"],

),

css=CUSTOM_CSS

) as demo:

# Load history from disk on startup

history_state = gr.State(load_history())

q_index = gr.State("0")

gr.HTML("""

<div style="text-align:center; padding: 1.8rem 0 0.8rem;">

<h1 style="font-size:2.2rem; font-weight:800; margin:0; font-size: 2.2rem;

background: linear-gradient(135deg,#6366f1,#a78bfa);

-webkit-background-clip:text; -webkit-text-fill-color:transparent;">

AI Interview Coach

</h1>

<p style="color:#64748b; margin-top:0.4rem; font-size:1rem;">

<b>Practice · Get Feedback · Improve </b><br>

<span style="font-size:0.8rem; color:#808080;">Powered by <span style="color:#f97316; font-weight:600;">Mistral 7B

</p>

</div>

""")

gr.HTML("""

<style>

@keyframes floatBlob1 {

0% { transform: translate(0px, 0px); }

33% { transform: translate(-30px, -40px); }

66% { transform: translate(40px, -20px); }

100% { transform: translate(0px, 0px); }

}

@keyframes floatBlob2 {

0% { transform: translate(0px, 0px); }

33% { transform: translate(50px, 30px); }

66% { transform: translate(-30px, 50px); }

100% { transform: translate(0px, 0px); }

}

@keyframes floatBlob3 {

0% { transform: translate(0px, 0px); }

33% { transform: translate(-40px, 50px); }

66% { transform: translate(30px, -40px); }

100% { transform: translate(0px, 0px); }

}

</style>

<div style="

position: fixed; width: 400px; height: 300px;

border-radius: 50%; background: #6366f1;

filter: blur(80px); opacity: 0.35;

top: -100px; left: -100px;

z-index: 0; pointer-events: none;

animation: floatBlob1 20s ease-in-out infinite;

"></div>

<div style="

position: fixed; width: 500px; height: 400px;

border-radius: 50%; background: #8b5cf6;

filter: blur(100px); opacity: 0.25;

top: 50%; right: -200px;

z-index: 0; pointer-events: none;

animation: floatBlob2 25s ease-in-out infinite;

"></div>

<div style="

position: fixed; width: 600px; height: 350px;

border-radius: 50%; background: #f97316;

filter: blur(90px); opacity: 0.2;

bottom: -100px; left: 100px;

z-index: 0; pointer-events: none;

animation: floatBlob3 15s ease-in-out infinite;

"></div>

""")

with gr.Tabs():

# ── Tab 1: Practice ───────────────────────────────────────────────────

with gr.Tab("🎯 Practice"):

with gr.Row():

with gr.Column(scale=1):

gr.Markdown("### 📋 Step 1 — Paste the Job Description")

job_desc_box = gr.Textbox(

label="Job Description",

lines=6,

placeholder="Paste from LinkedIn, company careers page, etc.",

)

start_btn = gr.Button("🚀 Start Interview", variant="primary", size="lg")

with gr.Column(scale=1):

gr.Markdown("### 💬 Step 2 — Answer Questions")

progress_box = gr.Textbox(

label="Progress",

value="Ready to start",

interactive=False,

elem_id="progress_box",

)

question_box = gr.Textbox(

label="Interview Question",

lines=3,

interactive=False,

)

answer_box = gr.Textbox(

label="Your Answer",

lines=5,

placeholder="Be specific — use the STAR format (Situation, Task, Action, Result).",

)

with gr.Row():

feedback_btn = gr.Button("📊 Get Feedback", variant="primary")

next_btn = gr.Button("➡️ Next Question", variant="secondary")

# ── Previous answer review panel ──────────────────────────────────

with gr.Accordion("🔁 Previous Question Review", open=False) as prev_accordion:

with gr.Row():

prev_question_box = gr.Textbox(

label="Previous Question",

interactive=False,

lines=2,

placeholder="Will show the last question after you click Next Question...",

)

prev_answer_box = gr.Textbox(

label="Your Previous Answer",

interactive=False,

lines=3,

placeholder="Will show your last answer here...",

)

# ── Full session log accordion ─────────────────────────────────────

with gr.Accordion("📋 This Session's Log", open=False):

session_log_display = gr.Markdown("Complete questions to see your session log here.")

gr.Markdown("### 🏆 Step 3 — Coach Feedback")

feedback_box = gr.Textbox(

label="AI Coach Feedback",

interactive=False,

lines=7,

elem_id="feedback_box",

)

# ── Tab 2: History ────────────────────────────────────────────────────

with gr.Tab("📈 History & Progress"):

gr.Markdown("""

### Your Interview Sessions

History is **saved to disk** — persists across restarts! 💾

""")

with gr.Row():

refresh_btn = gr.Button("🔄 Refresh History", variant="secondary")

clear_btn = gr.Button("🗑️ Clear History", variant="secondary")

history_display = gr.Markdown(

render_history(load_history()) # Load from disk on startup

)

# ── Tab 3: Tips ───────────────────────────────────────────────────────

with gr.Tab("💡 Tips & Resources"):

tips_display = gr.Markdown(build_tips())

# ── Wire up events ─────────────────────────────────────────────────────────

start_btn.click(

fn=generate_all_questions,

inputs=[job_desc_box, history_state],

outputs=[question_box, q_index, progress_box, history_state, tips_display],

).then(

fn=render_history,

inputs=[history_state],

outputs=[history_display],

)

feedback_btn.click(

fn=score_answer,

inputs=[answer_box, q_index, history_state],

outputs=[feedback_box, history_state],

).then(

fn=render_history,

inputs=[history_state],

outputs=[history_display],

)

next_btn.click(

fn=next_question,

inputs=[q_index, answer_box, history_state],

outputs=[question_box, answer_box, q_index, progress_box, history_state, prev_question_box, prev_answer_box, session_log_display],

)

refresh_btn.click(

fn=render_history,

inputs=[history_state],

outputs=[history_display],

)

def clear_history(history_state):

"""Clear all history"""

if os.path.exists(HISTORY_FILE):

os.remove(HISTORY_FILE)

return [], "History cleared! Start a new interview above."

clear_btn.click(

fn=clear_history,

inputs=[history_state],

outputs=[history_state, history_display],

)

demo.launch(share = True)