| # **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) | |