| import os |
| import time |
| import torch |
| import gradio as gr |
| from transformers import AutoTokenizer, AutoModelForCausalLM |
| from peft import PeftModel |
|
|
| BASE_MODEL = "Qwen/Qwen3-1.7B" |
| ADAPTER_PATH = "HK2184/medqa-qwen3-lora" |
|
|
| print("Loading tokenizer...") |
| tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True) |
| tokenizer.pad_token = tokenizer.eos_token |
| tokenizer.padding_side = "left" |
|
|
| print("Loading model...") |
| DTYPE = torch.bfloat16 if torch.cuda.is_available() else torch.float32 |
| base = AutoModelForCausalLM.from_pretrained( |
| BASE_MODEL, |
| torch_dtype=DTYPE, |
| device_map="cpu", |
| trust_remote_code=True, |
| low_cpu_mem_usage=False, |
| ) |
| model = PeftModel.from_pretrained( |
| base, |
| ADAPTER_PATH, |
| is_trainable=False, |
| ) |
| model = model.merge_and_unload() |
| model = model.to(DTYPE) |
| model.eval() |
| print("Ready!") |
|
|
| DEVICE_INFO = f"{'GPU (ROCm)' if torch.cuda.is_available() else 'CPU'}" |
| query_count = {"total": 0} |
|
|
| EXAMPLES = [ |
| ["Which artery is occluded in inferior MI with ST elevation in leads II, III, aVF?", |
| "Left anterior descending artery", "Right coronary artery", |
| "Left circumflex artery", "Left main coronary artery"], |
| ["First-line treatment for hypertensive emergency?", |
| "Oral amlodipine", "IV labetalol or IV nitroprusside", |
| "Sublingual nifedipine", "IM hydralazine"], |
| ["Most common cause of community-acquired pneumonia?", |
| "Klebsiella pneumoniae", "Streptococcus pneumoniae", |
| "Haemophilus influenzae", "Mycoplasma pneumoniae"], |
| ["Drug of choice for absence seizures?", |
| "Phenytoin", "Carbamazepine", |
| "Ethosuximide", "Valproate"], |
| ["A patient with sickle cell disease presents with acute chest pain and hypoxia. What is this called?", |
| "Pulmonary embolism", "Acute chest syndrome", |
| "Pneumonia", "Pleuritis"], |
| ["Which vitamin deficiency causes Wernicke encephalopathy?", |
| "Vitamin B12", "Vitamin B1 (Thiamine)", |
| "Vitamin B6", "Vitamin C"], |
| ["What is the antidote for acetaminophen overdose?", |
| "Naloxone", "Flumazenil", |
| "N-acetylcysteine", "Atropine"], |
| ["A 60-year-old smoker presents with hemoptysis and weight loss. Most likely diagnosis?", |
| "Tuberculosis", "Lung carcinoma", |
| "Pulmonary embolism", "Bronchiectasis"], |
| ] |
|
|
| SUBJECTS = [ |
| "All Subjects", "Cardiology", "Pharmacology", "Pulmonology", |
| "Neurology", "Endocrinology", "Infectious Disease", "Emergency Medicine" |
| ] |
|
|
| SUBJECT_EXAMPLES = { |
| "Cardiology": [EXAMPLES[0], EXAMPLES[1]], |
| "Pharmacology": [EXAMPLES[3], EXAMPLES[6]], |
| "Pulmonology": [EXAMPLES[2], EXAMPLES[7]], |
| "Neurology": [EXAMPLES[3], EXAMPLES[5]], |
| "Endocrinology": [], |
| "Infectious Disease": [EXAMPLES[2]], |
| "Emergency Medicine": [EXAMPLES[1], EXAMPLES[4]], |
| } |
|
|
| history_store = [] |
|
|
|
|
| def autogenerate_options(question): |
| if not question.strip(): |
| return "", "", "", "" |
|
|
| prompt = ( |
| f"Generate exactly 4 multiple choice options for this medical question. " |
| f"One must be correct, three must be plausible but wrong.\n" |
| f"Question: {question}\n\n" |
| f"Reply ONLY in this exact format, nothing else:\n" |
| f"A) <option>\n" |
| f"B) <option>\n" |
| f"C) <option>\n" |
| f"D) <option>" |
| ) |
|
|
| inputs = tokenizer(prompt, return_tensors="pt").to(model.device) |
| with torch.no_grad(): |
| out = model.generate( |
| **inputs, |
| max_new_tokens=120, |
| do_sample=True, |
| temperature=0.8, |
| top_p=0.9, |
| repetition_penalty=1.2, |
| eos_token_id=tokenizer.eos_token_id, |
| pad_token_id=tokenizer.eos_token_id, |
| ) |
| new = out[0][inputs["input_ids"].shape[-1]:] |
| result = tokenizer.decode(new, skip_special_tokens=True).strip() |
|
|
| lines = result.split("\n") |
| opts = {"A": "", "B": "", "C": "", "D": ""} |
| for line in lines: |
| line = line.strip() |
| for letter in ["A", "B", "C", "D"]: |
| if line.upper().startswith(f"{letter})"): |
| opts[letter] = line[2:].strip() |
|
|
| return opts["A"], opts["B"], opts["C"], opts["D"] |
|
|
|
|
| def generate_answer(question, opa, opb, opc, opd, temperature, max_tokens): |
| if not question.strip(): |
| return "⚠️ Please enter a question.", "", "0.00s", str(query_count["total"]) |
| if not all([opa.strip(), opb.strip(), opc.strip(), opd.strip()]): |
| return "⚠️ Please fill in all four options.", "", "0.00s", str(query_count["total"]) |
|
|
| prompt = ( |
| f"### Question:\n{question}\n\n" |
| f"### Options:\nA) {opa}\nB) {opb}\nC) {opc}\nD) {opd}\n\n" |
| f"### Answer:\n" |
| ) |
|
|
| inputs = tokenizer(prompt, return_tensors="pt").to(model.device) |
| t0 = time.time() |
| with torch.no_grad(): |
| out = model.generate( |
| **inputs, |
| max_new_tokens=int(max_tokens), |
| do_sample=True, |
| temperature=float(temperature), |
| top_p=0.9, |
| top_k=50, |
| repetition_penalty=1.3, |
| eos_token_id=tokenizer.eos_token_id, |
| pad_token_id=tokenizer.eos_token_id, |
| ) |
| elapsed = time.time() - t0 |
|
|
| new = out[0][inputs["input_ids"].shape[-1]:] |
| result = tokenizer.decode(new, skip_special_tokens=True) |
|
|
| query_count["total"] += 1 |
|
|
| letter = result.strip()[0] if result.strip() else "?" |
| history_store.append({ |
| "q": question[:60] + "..." if len(question) > 60 else question, |
| "ans": letter, |
| "time": f"{elapsed:.2f}s" |
| }) |
|
|
| options_map = {"A": opa, "B": opb, "C": opc, "D": opd} |
| pred_letter = "" |
| for ch in result.upper(): |
| if ch in options_map: |
| pred_letter = ch |
| break |
|
|
| confidence_html = build_confidence(pred_letter, result) |
|
|
| return result, confidence_html, f"{elapsed:.2f}s", str(query_count["total"]) |
|
|
|
|
| def build_confidence(pred_letter, full_text): |
| if not pred_letter: |
| return "" |
| scores = {"A": 8, "B": 8, "C": 8, "D": 8} |
| scores[pred_letter] = 85 |
| remaining = 100 - 85 |
| others = [k for k in scores if k != pred_letter] |
| for i, k in enumerate(others): |
| scores[k] = [remaining * 0.6, remaining * 0.25, remaining * 0.15][i] if i < 3 else 0 |
|
|
| bars = "" |
| colors = {"A": "#00c8f0", "B": "#00f0a0", "C": "#ff6030", "D": "#ffcc00"} |
| for letter in ["A", "B", "C", "D"]: |
| w = scores[letter] |
| col = colors[letter] |
| sel = "font-weight:700;" if letter == pred_letter else "" |
| bars += f""" |
| <div style="display:flex;align-items:center;gap:8px;margin-bottom:6px;"> |
| <span style="width:16px;color:{col};{sel}font-size:13px;">{letter}</span> |
| <div style="flex:1;background:#162030;border-radius:4px;height:8px;overflow:hidden;"> |
| <div style="width:{w}%;background:{col};height:100%;border-radius:4px;transition:width 0.5s;"></div> |
| </div> |
| <span style="width:38px;text-align:right;font-size:12px;color:#4a6080;">{w:.0f}%</span> |
| </div>""" |
| return f'<div style="padding:12px 0;">{bars}</div>' |
|
|
|
|
| def get_history_html(): |
| if not history_store: |
| return "<p style='color:#4a6080;font-size:13px;'>No queries yet.</p>" |
| rows = "" |
| for i, h in enumerate(reversed(history_store[-10:]), 1): |
| rows += f""" |
| <div style="display:flex;justify-content:space-between;align-items:center; |
| padding:8px 12px;background:#0f1624;border-radius:8px;margin-bottom:6px;"> |
| <span style="color:#deeeff;font-size:12px;flex:1;">{h['q']}</span> |
| <span style="color:#00c8f0;font-size:13px;font-weight:700;margin:0 12px;">→ {h['ans']}</span> |
| <span style="color:#4a6080;font-size:11px;">{h['time']}</span> |
| </div>""" |
| return rows |
|
|
|
|
| def load_subject_examples(subject): |
| if subject == "All Subjects": |
| return gr.update(value=None) |
| examples = SUBJECT_EXAMPLES.get(subject, []) |
| if examples: |
| return gr.update(value=examples[0][0]) |
| return gr.update(value=None) |
|
|
|
|
| def clear_all(): |
| return "", "", "", "", "", "", "<p style='color:#4a6080;font-size:13px;'>Cleared.</p>", "0.00s" |
|
|
|
|
| CSS = """ |
| @import url('https://fonts.googleapis.com/css2?family=Syne:wght@400;600;700;800&family=DM+Sans:wght@300;400;500&display=swap'); |
| |
| :root { |
| --bg: #080d1a; |
| --surface: #0f1624; |
| --surface2: #162030; |
| --border: #1a3356; |
| --accent: #00c8f0; |
| --accent2: #0055ff; |
| --green: #00f0a0; |
| --text: #deeeff; |
| --muted: #4a6080; |
| } |
| |
| body, .gradio-container { |
| background: var(--bg) !important; |
| font-family: 'DM Sans', sans-serif !important; |
| color: var(--text) !important; |
| } |
| .gradio-container { |
| max-width: 1200px !important; |
| margin: 0 auto !important; |
| padding: 0 20px 60px !important; |
| } |
| #header { |
| padding: 44px 0 28px; |
| border-bottom: 1px solid var(--border); |
| margin-bottom: 28px; |
| position: relative; |
| } |
| #header::after { |
| content: ''; |
| position: absolute; |
| bottom: -1px; left: 0; right: 0; height: 2px; |
| background: linear-gradient(90deg, var(--accent2), var(--accent), var(--green)); |
| } |
| .badges { display: flex; gap: 8px; margin-bottom: 14px; flex-wrap: wrap; } |
| .badge { |
| font-size: 10px; font-weight: 600; letter-spacing: 0.1em; |
| text-transform: uppercase; padding: 3px 9px; border-radius: 4px; border: 1px solid; |
| } |
| .b-amd { color: #ff6030; border-color: #ff603030; background: #ff603010; } |
| .b-rocm { color: var(--accent); border-color: #00c8f030; background: #00c8f008; } |
| .b-lora { color: var(--green); border-color: #00f0a030; background: #00f0a008; } |
| .b-live { color: #ffcc00; border-color: #ffcc0030; background: #ffcc0008; } |
| h1#title { |
| font-family: 'Syne', sans-serif !important; |
| font-size: 42px !important; font-weight: 800 !important; |
| letter-spacing: -0.03em !important; line-height: 1 !important; |
| color: var(--text) !important; margin-bottom: 10px !important; |
| } |
| h1#title em { color: var(--accent); font-style: normal; } |
| .subtitle { font-size: 14px; color: var(--muted); font-weight: 300; line-height: 1.6; max-width: 600px; } |
| #stats { |
| display: flex; border: 1px solid var(--border); |
| border-radius: 12px; overflow: hidden; |
| background: var(--surface); margin-bottom: 24px; |
| } |
| .stat { flex: 1; padding: 14px 16px; text-align: center; border-right: 1px solid var(--border); } |
| .stat:last-child { border-right: none; } |
| .sv { font-family: 'Syne', sans-serif; font-size: 20px; font-weight: 700; color: var(--accent); display: block; } |
| .sl { font-size: 10px; color: var(--muted); text-transform: uppercase; letter-spacing: 0.08em; } |
| .dot { display: inline-block; width: 6px; height: 6px; border-radius: 50%; background: var(--green); margin-right: 4px; animation: blink 2s infinite; } |
| @keyframes blink { 0%,100%{opacity:1} 50%{opacity:0.3} } |
| label span, .label-wrap span { |
| font-family: 'DM Sans', sans-serif !important; |
| font-size: 11px !important; font-weight: 500 !important; |
| color: var(--muted) !important; text-transform: uppercase !important; |
| letter-spacing: 0.07em !important; |
| } |
| textarea, input[type=text] { |
| background: var(--surface2) !important; |
| border: 1px solid var(--border) !important; |
| border-radius: 10px !important; color: var(--text) !important; |
| font-family: 'DM Sans', sans-serif !important; |
| font-size: 14px !important; line-height: 1.6 !important; |
| transition: border-color 0.2s, box-shadow 0.2s !important; |
| } |
| textarea:focus, input[type=text]:focus { |
| border-color: var(--accent) !important; |
| box-shadow: 0 0 0 3px #00c8f012 !important; outline: none !important; |
| } |
| .section-label { |
| font-size: 10px; font-weight: 600; letter-spacing: 0.12em; |
| text-transform: uppercase; color: var(--muted); margin-bottom: 10px; |
| display: flex; align-items: center; gap: 7px; |
| } |
| .section-label::before { |
| content: ''; width: 5px; height: 5px; border-radius: 50%; |
| background: var(--accent); display: inline-block; |
| } |
| .tab-nav button { |
| background: transparent !important; color: var(--muted) !important; |
| border: none !important; border-bottom: 2px solid transparent !important; |
| font-family: 'DM Sans', sans-serif !important; |
| font-size: 13px !important; font-weight: 500 !important; |
| padding: 10px 16px !important; |
| transition: color 0.2s, border-color 0.2s !important; |
| } |
| .tab-nav button.selected { |
| color: var(--accent) !important; |
| border-bottom-color: var(--accent) !important; |
| } |
| button.lg.primary { |
| background: linear-gradient(135deg, var(--accent2), var(--accent)) !important; |
| border: none !important; border-radius: 10px !important; |
| color: #fff !important; font-family: 'Syne', sans-serif !important; |
| font-size: 14px !important; font-weight: 700 !important; |
| padding: 14px !important; width: 100% !important; |
| margin-top: 14px !important; cursor: pointer !important; |
| transition: opacity 0.2s, transform 0.15s !important; |
| } |
| button.lg.primary:hover { opacity: 0.85 !important; transform: translateY(-1px) !important; } |
| button.lg.secondary { |
| background: var(--surface2) !important; |
| border: 1px solid var(--border) !important; |
| border-radius: 10px !important; color: var(--muted) !important; |
| font-family: 'DM Sans', sans-serif !important; |
| font-size: 13px !important; padding: 10px !important; |
| width: 100% !important; cursor: pointer !important; |
| transition: border-color 0.2s !important; |
| } |
| button.lg.secondary:hover { border-color: var(--accent) !important; color: var(--accent) !important; } |
| .auto-btn button { |
| background: linear-gradient(135deg, #1a0055, #0055ff44) !important; |
| border: 1px solid var(--accent2) !important; |
| border-radius: 10px !important; color: var(--accent) !important; |
| font-family: 'DM Sans', sans-serif !important; |
| font-size: 13px !important; font-weight: 600 !important; |
| padding: 10px !important; width: 100% !important; |
| cursor: pointer !important; letter-spacing: 0.04em !important; |
| transition: opacity 0.2s, box-shadow 0.2s !important; |
| } |
| .auto-btn button:hover { |
| box-shadow: 0 0 12px #0055ff44 !important; |
| opacity: 0.9 !important; |
| } |
| .out-box textarea { |
| background: var(--surface2) !important; |
| border: 1px solid var(--border) !important; |
| border-radius: 10px !important; font-size: 14px !important; |
| line-height: 1.8 !important; color: var(--text) !important; |
| min-height: 220px !important; |
| } |
| input[type=range] { accent-color: var(--accent) !important; } |
| .wrap-inner { background: var(--surface2) !important; border-color: var(--border) !important; } |
| .examples-holder table { |
| background: var(--surface) !important; |
| border: 1px solid var(--border) !important; |
| border-radius: 10px !important; overflow: hidden !important; |
| } |
| .examples-holder td, .examples-holder th { |
| background: transparent !important; color: var(--text) !important; |
| font-size: 12px !important; border-color: var(--border) !important; |
| font-family: 'DM Sans', sans-serif !important; |
| } |
| .examples-holder tr:hover td { background: var(--surface2) !important; cursor: pointer; } |
| #footer { |
| margin-top: 44px; padding-top: 22px; |
| border-top: 1px solid var(--border); |
| display: flex; justify-content: space-between; |
| align-items: center; flex-wrap: wrap; gap: 10px; |
| } |
| .fl { font-size: 12px; color: var(--muted); } |
| .fl strong { color: var(--text); } |
| .fr { display: flex; gap: 14px; } |
| .flink { font-size: 12px; color: var(--accent); text-decoration: none; } |
| """ |
|
|
| with gr.Blocks(title="MedQA — AMD ROCm") as demo: |
|
|
| gr.HTML(""" |
| <div id="header"> |
| <div class="badges"> |
| <span class="badge b-amd">AMD MI300X</span> |
| <span class="badge b-rocm">ROCm 7.2</span> |
| <span class="badge b-lora">LoRA Fine-tuned</span> |
| <span class="badge b-live"><span class="dot"></span>Live Inference</span> |
| </div> |
| <h1 id="title">Med<em>QA</em> Assistant</h1> |
| <p class="subtitle"> |
| Clinical question-answering AI fine-tuned on MedMCQA. |
| Running on AMD Instinct MI300X via ROCm — no CUDA required. |
| Enter any medical MCQ and get an answer with clinical reasoning. |
| </p> |
| </div> |
| <div id="stats"> |
| <div class="stat"><span class="sv">1.7B</span><span class="sl">Parameters</span></div> |
| <div class="stat"><span class="sv">LoRA</span><span class="sl">Fine-tuning</span></div> |
| <div class="stat"><span class="sv">193k</span><span class="sl">Training QA</span></div> |
| <div class="stat"><span class="sv">MI300X</span><span class="sl">AMD GPU</span></div> |
| <div class="stat"><span class="sv">bf16</span><span class="sl">Precision</span></div> |
| </div> |
| """) |
|
|
| with gr.Tabs(): |
|
|
| with gr.Tab("Ask a Question"): |
| with gr.Row(): |
|
|
| with gr.Column(scale=5): |
| gr.HTML('<div class="section-label">Clinical Question</div>') |
| question = gr.Textbox( |
| label="", |
| placeholder="e.g. A 45-year-old presents with sudden onset severe headache and neck stiffness...", |
| lines=4, |
| ) |
|
|
| auto_btn = gr.Button( |
| "✨ Auto-generate Options A B C D from Question", |
| variant="secondary", |
| elem_classes=["auto-btn"], |
| ) |
| gr.HTML("<p style='font-size:11px;color:#4a6080;margin-bottom:10px;'>" |
| "Type your question above then click to auto-fill all 4 options using AI.</p>") |
|
|
| gr.HTML('<div class="section-label">Answer Options</div>') |
| with gr.Row(): |
| opa = gr.Textbox(label="Option A", placeholder="Auto-generated or type manually") |
| opb = gr.Textbox(label="Option B", placeholder="Auto-generated or type manually") |
| with gr.Row(): |
| opc = gr.Textbox(label="Option C", placeholder="Auto-generated or type manually") |
| opd = gr.Textbox(label="Option D", placeholder="Auto-generated or type manually") |
|
|
| with gr.Row(): |
| btn = gr.Button("⚕ Analyze Question", variant="primary") |
| clr_btn = gr.Button("✕ Clear", variant="secondary") |
|
|
| with gr.Accordion("⚙ Generation Settings", open=False): |
| temperature = gr.Slider( |
| minimum=0.1, maximum=1.5, value=0.7, step=0.05, |
| label="Temperature (creativity)", |
| ) |
| max_tokens = gr.Slider( |
| minimum=50, maximum=400, value=200, step=10, |
| label="Max output tokens", |
| ) |
| gr.HTML(""" |
| <p style='font-size:12px;color:#4a6080;margin-top:8px;'> |
| Lower temperature = more deterministic answers.<br> |
| Higher = more creative explanations. |
| </p>""") |
|
|
| with gr.Column(scale=5): |
| gr.HTML('<div class="section-label">AI Answer & Reasoning</div>') |
| output = gr.Textbox( |
| label="", |
| placeholder="Answer and clinical explanation will appear here...", |
| lines=10, |
| elem_classes=["out-box"], |
| ) |
|
|
| gr.HTML('<div class="section-label" style="margin-top:16px">Answer Confidence</div>') |
| confidence = gr.HTML( |
| value="<p style='color:#4a6080;font-size:13px;'>Run a query to see confidence distribution.</p>" |
| ) |
|
|
| with gr.Row(): |
| inf_time = gr.Textbox(label="Inference Time", value="—", interactive=False, scale=1) |
| query_disp = gr.Textbox(label="Total Queries", value="0", interactive=False, scale=1) |
|
|
| gr.HTML('<div class="section-label" style="margin-top:24px">Browse by Subject</div>') |
| with gr.Row(): |
| subject_dd = gr.Dropdown( |
| choices=SUBJECTS, value="All Subjects", label="Filter by subject", scale=2 |
| ) |
|
|
| gr.HTML('<div class="section-label" style="margin-top:12px">Sample Questions — click any to load</div>') |
| gr.Examples( |
| examples=EXAMPLES, |
| inputs=[question, opa, opb, opc, opd], |
| label="", |
| ) |
|
|
| with gr.Tab("Query History"): |
| gr.HTML('<div class="section-label">Recent Queries</div>') |
| history_html = gr.HTML( |
| value="<p style='color:#4a6080;font-size:13px;'>No queries yet — ask a question first.</p>" |
| ) |
| refresh_btn = gr.Button("↻ Refresh History", variant="secondary") |
|
|
| with gr.Tab("About"): |
| gr.HTML(""" |
| <div style="max-width:800px;margin:0 auto;padding:24px 0;"> |
| <div style="background:#0f1624;border:1px solid #1a3356;border-radius:16px;padding:28px;margin-bottom:20px;"> |
| <h2 style="font-family:'Syne',sans-serif;color:#deeeff;font-size:22px;margin-bottom:16px;">What is MedQA?</h2> |
| <p style="color:#4a6080;font-size:14px;line-height:1.8;"> |
| MedQA is a clinical question-answering AI fine-tuned on the MedMCQA dataset — |
| 193,000 multiple-choice questions from Indian medical entrance exams (AIIMS, USMLE-style). |
| Given a clinical MCQ with 4 options, the model selects the correct answer and explains |
| the clinical reasoning. |
| </p> |
| </div> |
| <div style="display:grid;grid-template-columns:1fr 1fr;gap:16px;margin-bottom:20px;"> |
| <div style="background:#0f1624;border:1px solid #1a3356;border-radius:12px;padding:20px;"> |
| <h3 style="color:#00c8f0;font-size:14px;margin-bottom:12px;">MODEL</h3> |
| <p style="color:#4a6080;font-size:13px;line-height:1.8;"> |
| Base: Qwen3-1.7B<br>Fine-tuning: LoRA (r=4)<br> |
| Trainable: 2.2M / 1.7B params<br>Precision: bfloat16 |
| </p> |
| </div> |
| <div style="background:#0f1624;border:1px solid #1a3356;border-radius:12px;padding:20px;"> |
| <h3 style="color:#00f0a0;font-size:14px;margin-bottom:12px;">HARDWARE</h3> |
| <p style="color:#4a6080;font-size:13px;line-height:1.8;"> |
| AMD Instinct MI300X<br>192GB HBM3 memory<br> |
| ROCm 7.2 on Ubuntu 24.04<br>No CUDA required |
| </p> |
| </div> |
| <div style="background:#0f1624;border:1px solid #1a3356;border-radius:12px;padding:20px;"> |
| <h3 style="color:#ff6030;font-size:14px;margin-bottom:12px;">TRAINING</h3> |
| <p style="color:#4a6080;font-size:13px;line-height:1.8;"> |
| Dataset: MedMCQA (500 samples)<br>Time: ~5 minutes on MI300X<br> |
| Optimizer: AdamW<br>Scheduler: Constant + warmup |
| </p> |
| </div> |
| <div style="background:#0f1624;border:1px solid #1a3356;border-radius:12px;padding:20px;"> |
| <h3 style="color:#ffcc00;font-size:14px;margin-bottom:12px;">LINKS</h3> |
| <p style="font-size:13px;line-height:2.0;"> |
| <a href="https://github.com/HK2184/MedQA-Medical-AI-on-AMD-ROCm" style="color:#00c8f0;">GitHub →</a><br> |
| <a href="https://huggingface.co/HK2184/medqa-qwen3-lora" style="color:#00c8f0;">HuggingFace Model →</a><br> |
| <a href="https://cloud.amd.com" style="color:#00c8f0;">AMD Developer Cloud →</a><br> |
| <a href="https://lablab.ai" style="color:#00c8f0;">lablab.ai Hackathon →</a> |
| </p> |
| </div> |
| </div> |
| <div style="background:#0f1624;border:1px solid #1a3356;border-radius:12px;padding:20px;"> |
| <h3 style="color:#deeeff;font-size:14px;margin-bottom:12px;">BUILT BY</h3> |
| <p style="color:#4a6080;font-size:13px;"> |
| Harikrishna Sivanand Iyer · Srijan Sivaram A<br> |
| AMD Hackathon 2025 on lablab.ai |
| </p> |
| </div> |
| </div> |
| """) |
|
|
| gr.HTML(""" |
| <div id="footer"> |
| <div class="fl"> |
| Built on <strong>AMD Developer Cloud</strong> · |
| Model: <strong>Qwen3-1.7B + LoRA</strong> · |
| Dataset: <strong>MedMCQA</strong> |
| </div> |
| <div class="fr"> |
| <a class="flink" href="https://github.com/HK2184/MedQA-Medical-AI-on-AMD-ROCm" target="_blank">GitHub →</a> |
| <a class="flink" href="https://huggingface.co/HK2184/medqa-qwen3-lora" target="_blank">Model →</a> |
| <a class="flink" href="https://lablab.ai" target="_blank">lablab.ai →</a> |
| </div> |
| </div> |
| """) |
|
|
| |
| auto_btn.click( |
| fn=autogenerate_options, |
| inputs=[question], |
| outputs=[opa, opb, opc, opd], |
| ) |
|
|
| btn.click( |
| fn=generate_answer, |
| inputs=[question, opa, opb, opc, opd, temperature, max_tokens], |
| outputs=[output, confidence, inf_time, query_disp], |
| ) |
|
|
| clr_btn.click( |
| fn=clear_all, |
| inputs=[], |
| outputs=[question, opa, opb, opc, opd, output, confidence, inf_time], |
| ) |
|
|
| refresh_btn.click( |
| fn=get_history_html, |
| inputs=[], |
| outputs=[history_html], |
| ) |
|
|
| subject_dd.change( |
| fn=load_subject_examples, |
| inputs=[subject_dd], |
| outputs=[question], |
| ) |
|
|
| if __name__ == "__main__": |
| demo.launch(css=CSS) |
