import gradio as gr import os, requests, io, json import numpy as np import pandas as pd import matplotlib matplotlib.use("Agg") import matplotlib.pyplot as plt from groq import Groq from PIL import Image from datetime import datetime from huggingface_hub import HfApi, hf_hub_download GROQ_KEY = os.environ.get("GROQ_API_KEY", "") HF_TOKEN = os.environ.get("HF_TOKEN", "") HISTORY_REPO = "Saicharan21/cardiolab-chat-history" PAPERS_DB_REPO = "Saicharan21/cardiolab-papers-db" CARDIOLAB_MODEL_REPO = "Saicharan21/CardioLab-AI-Model" CHAT_MODELS = { "Llama 3.3 70B (Best)": "llama-3.3-70b-versatile", "Llama 3.1 8B (Fast)": "llama-3.1-8b-instant", "Llama 4 Scout": "meta-llama/llama-4-scout-17b-16e-instruct", "Llama 4 Maverick": "meta-llama/llama-4-maverick-17b-128e-instruct", } KNOWHOW = ("MCL: Sylgard 184 PDMS 10:1 ratio 48hr cure green laser PIV 70bpm 5L/min. " "TGT: Arduino Uno Stepper Motor 150mL blood 0 20 40 60min TAT PF1.2 hemolysis platelets. " "NORMAL: TAT below 8. PF1.2 below 2.0. Hemo below 20. Plt above 150. " "uPAD: Jaffe reaction creatinine picric acid orange-red. Normal 0.6-1.2 mg/dL. CKD above 1.5. " "MHV: 27mm SJM Regent bileaflet trileaflet monoleaflet pediatric. " "PIV: green laser 532nm. Normal velocity 0.5-2.0 m/s. Shear below 5 Pa. Risk above 10 Pa. " "Equipment: Heska HT5 analyzer PIV Tygon tubing Arduino Uno.") CSS = """ @import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&display=swap'); body, .gradio-container { background: #f8fafc !important; font-family: Inter, sans-serif !important; } .tab-nav { background: #fff !important; border-bottom: 1px solid #e2e8f0 !important; padding: 0 8px !important; display: flex !important; flex-wrap: wrap !important; } .tab-nav button { background: transparent !important; color: #64748b !important; border: none !important; border-bottom: 2px solid transparent !important; border-radius: 0 !important; padding: 10px 12px !important; font-weight: 500 !important; font-size: 0.8em !important; white-space: nowrap !important; margin-bottom: -1px !important; } .tab-nav button:hover { color: #c1121f !important; background: #fff5f5 !important; } .tab-nav button.selected { color: #c1121f !important; border-bottom: 2px solid #c1121f !important; font-weight: 700 !important; background: transparent !important; } .message.user { background: linear-gradient(135deg, #c1121f, #e63946) !important; color: white !important; border-radius: 14px 14px 4px 14px !important; padding: 12px 16px !important; } .message.bot { background: #ffffff !important; color: #1a202c !important; border: 1px solid #e2e8f0 !important; border-left: 3px solid #c1121f !important; border-radius: 4px 14px 14px 14px !important; padding: 12px 16px !important; } textarea { background: #fff !important; color: #1a202c !important; border: 1px solid #e2e8f0 !important; border-radius: 10px !important; } textarea:focus { border-color: #c1121f !important; outline: none !important; box-shadow: 0 0 0 2px rgba(193,18,31,0.1) !important; } button.primary { background: #c1121f !important; color: white !important; border: none !important; border-radius: 8px !important; font-weight: 600 !important; } button.primary:hover { background: #a00e18 !important; transform: translateY(-1px) !important; } button.secondary { background: #f1f5f9 !important; color: #475569 !important; border: 1px solid #e2e8f0 !important; border-radius: 8px !important; } input[type=number] { background: #fff !important; color: #1a202c !important; border: 1px solid #e2e8f0 !important; border-radius: 8px !important; } label span { color: #475569 !important; font-weight: 500 !important; font-size: 0.82em !important; } ::-webkit-scrollbar { width: 5px; } ::-webkit-scrollbar-thumb { background: #c1121f; border-radius: 4px; } """ HEADER = """

SJSU

Biomedical Eng.

CardioLab AI

SJSU Biomedical Engineering

RAG Active 4 Models 16 Papers

""" # ── PAPER DATABASE ───────────────────────────────────────── CHUNKS = [] METADATA = [] EMBEDDINGS = None PAPERS_LOADED = False EMBEDDER = None def load_papers(): global CHUNKS, METADATA, EMBEDDINGS, PAPERS_LOADED, EMBEDDER try: from sentence_transformers import SentenceTransformer chunks_path = hf_hub_download(repo_id=PAPERS_DB_REPO, filename="chunks.json", repo_type="dataset", token=HF_TOKEN) meta_path = hf_hub_download(repo_id=PAPERS_DB_REPO, filename="metadata.json", repo_type="dataset", token=HF_TOKEN) emb_path = hf_hub_download(repo_id=PAPERS_DB_REPO, filename="embeddings.npy", repo_type="dataset", token=HF_TOKEN) with open(chunks_path) as f: CHUNKS = json.load(f) with open(meta_path) as f: METADATA = json.load(f) EMBEDDINGS = np.load(emb_path) EMBEDDER = SentenceTransformer("all-MiniLM-L6-v2") PAPERS_LOADED = True print("Papers loaded: " + str(len(CHUNKS)) + " chunks") except Exception as e: print("Paper load error: " + str(e)) load_papers() def search_papers(query, n=4): if not PAPERS_LOADED or EMBEDDINGS is None or EMBEDDER is None: return "", [] try: q_emb = EMBEDDER.encode([query]) norms = np.linalg.norm(EMBEDDINGS, axis=1, keepdims=True) emb_norm = EMBEDDINGS / (norms + 1e-10) q_norm = q_emb / (np.linalg.norm(q_emb) + 1e-10) scores = (emb_norm @ q_norm.T).flatten() top_idx = np.argsort(scores)[::-1][:n] context = ""; results = []; seen = set() for idx in top_idx: chunk = CHUNKS[idx]; meta = METADATA[idx]; score = float(scores[idx]) if score > 0.25: results.append({"chunk": chunk, "paper": meta["paper"], "score": score}) if meta["paper"] not in seen: context += chr(10) + "=== FROM: " + meta["paper"] + " ===" + chr(10) seen.add(meta["paper"]) context += chunk[:500] + chr(10) return context, results except: return "", [] # ── SESSION MANAGEMENT ───────────────────────────────────── def load_all_sessions(): if not HF_TOKEN: return {} try: path = hf_hub_download(repo_id=HISTORY_REPO, filename="chat_history.json", repo_type="dataset", token=HF_TOKEN) with open(path) as f: return json.load(f) except: return {} def save_all_sessions(sessions): if not HF_TOKEN: return False try: api2 = HfApi(token=HF_TOKEN) api2.upload_file(path_or_fileobj=json.dumps(sessions, indent=2).encode(), path_in_repo="chat_history.json", repo_id=HISTORY_REPO, repo_type="dataset", token=HF_TOKEN, commit_message="Update") return True except: return False def get_session_list(): s = load_all_sessions() return list(reversed(list(s.keys()))) if s else ["No saved sessions"] def save_session(history, name): if not history: return "Nothing to save", gr.update() if not name or not name.strip(): name = "Chat " + datetime.now().strftime("%b %d %H:%M") sessions = load_all_sessions() sessions[name] = {"messages": history, "saved_at": datetime.now().isoformat()} ok = save_all_sessions(sessions) choices = get_session_list() return ("Saved: " + name if ok else "Save failed"), gr.update(choices=choices, value=name) def load_session(name): if not name or "No saved" in name: return [], "Select a session" sessions = load_all_sessions() return (sessions[name]["messages"], "Loaded: " + name) if name in sessions else ([], "Not found") def delete_session(name): if not name or "No saved" in name: return "Select a session", gr.update() sessions = load_all_sessions() if name in sessions: del sessions[name]; save_all_sessions(sessions) choices = get_session_list() return "Deleted: " + name, gr.update(choices=choices, value=choices[0] if choices else None) return "Not found", gr.update() def new_chat(): return [], "", "New chat" # ── SEARCH ───────────────────────────────────────────────── def get_pubmed(query, n=3): try: r = requests.get("https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi", params={"db":"pubmed","term":query+" AND (heart valve OR hemodynamics OR microfluidic OR thrombogen OR creatinine OR CKD)","retmax":n,"retmode":"json","sort":"date","field":"tiab"},timeout=10) ids = r.json()["esearchresult"]["idlist"] return chr(10).join(["https://pubmed.ncbi.nlm.nih.gov/"+i for i in ids]) if ids else "" except: return "" def quick_search(query): if not query.strip(): return "Please enter a topic." try: expanded = query if GROQ_KEY: try: client = Groq(api_key=GROQ_KEY) resp = client.chat.completions.create(model="llama-3.1-8b-instant", messages=[{"role":"system","content":"Biomedical PubMed expert. Convert to MeSH terms. Return ONLY terms."}, {"role":"user","content":"Optimize: " + query}], max_tokens=60) expanded = resp.choices[0].message.content.strip() or query except: pass forced = expanded + " AND (heart valve OR hemodynamics OR microfluidic OR thrombogen OR creatinine OR PIV OR CKD)" r = requests.get("https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi", params={"db":"pubmed","term":forced,"retmax":8,"retmode":"json","sort":"date","field":"tiab"},timeout=12) ids = r.json()["esearchresult"]["idlist"] out = "QUERY: " + query + chr(10) + "="*40 + chr(10) + chr(10) if ids: r2 = requests.get("https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi", params={"db":"pubmed","id":",".join(ids),"retmode":"xml","rettype":"abstract"},timeout=12) import xml.etree.ElementTree as ET root = ET.fromstring(r2.content) out += "PUBMED:" + chr(10) for article in root.findall(".//PubmedArticle"): try: title = article.find(".//ArticleTitle").text or "No title" pmid = article.find(".//PMID").text or "" year_el = article.find(".//PubDate/Year") year = year_el.text if year_el is not None else "" out += str(title)[:85] + " (" + year + ")" + chr(10) out += " https://pubmed.ncbi.nlm.nih.gov/" + pmid + chr(10) + chr(10) except: continue try: r3 = requests.get("https://api.semanticscholar.org/graph/v1/paper/search", params={"query":expanded,"limit":5,"fields":"title,year,url,citationCount"},timeout=12) papers = r3.json().get("data",[]) out += "SEMANTIC SCHOLAR:" + chr(10) for p in papers: year = p.get("year",0) or 0 if int(year) >= 2015: out += p.get("title","")[:85] + " (" + str(year) + ")" cites = p.get("citationCount",0) if cites: out += " | " + str(cites) + " citations" out += chr(10) + " " + p.get("url","") + chr(10) + chr(10) except: pass out += "SJSU SCHOLARWORKS:" + chr(10) out += " https://scholarworks.sjsu.edu/do/search/?q=" + requests.utils.quote(query) + "&context=6781027" return out except Exception as e: return "Search error: " + str(e) # ── CHAT ─────────────────────────────────────────────────── def research_chat(message, history, chat_model): if not message.strip(): return "", history if not GROQ_KEY: history.append({"role":"user","content":message}) history.append({"role":"assistant","content":"Error: Add GROQ_API_KEY to Space Settings."}) return "", history try: model_id = CHAT_MODELS.get(chat_model, "llama-3.3-70b-versatile") client = Groq(api_key=GROQ_KEY) paper_context, paper_results = search_papers(message, n=4) if paper_context: system_prompt = ("You are CardioLab AI for SJSU Biomedical Engineering. " "Answer using SJSU CardioLab research papers below. Cite paper names." + chr(10) + "SJSU PAPERS:" + chr(10) + paper_context + chr(10) + "KNOWLEDGE: " + KNOWHOW) else: system_prompt = "You are CardioLab AI for SJSU Biomedical Engineering. " + KNOWHOW msgs = [{"role":"system","content":system_prompt}] for item in history: if isinstance(item, dict): msgs.append({"role":item["role"],"content":item["content"]}) msgs.append({"role":"user","content":message}) resp = client.chat.completions.create(model=model_id, messages=msgs, max_tokens=800) answer = resp.choices[0].message.content if paper_results: unique_papers = list(dict.fromkeys([r["paper"] for r in paper_results])) answer += chr(10) + chr(10) + "Sources:" for p in unique_papers[:3]: answer += chr(10) + " - " + p.replace(".pdf","").replace("_"," ") pubmed = get_pubmed(message, n=2) if pubmed: answer += chr(10) + "PubMed: " + pubmed history.append({"role":"user","content":message}) history.append({"role":"assistant","content":answer}) return "", history except Exception as e: history.append({"role":"user","content":message}) history.append({"role":"assistant","content":"Error: " + str(e)}) return "", history def voice_chat(audio, history): if audio is None: history.append({"role":"assistant","content":"Please record first."}) return history try: client = Groq(api_key=GROQ_KEY) with open(audio, "rb") as f: tx = client.audio.transcriptions.create(file=("audio.wav", f, "audio/wav"), model="whisper-large-v3") paper_context, _ = search_papers(tx.text, n=3) system = "You are CardioLab AI. " + KNOWHOW if paper_context: system = "You are CardioLab AI. Use these SJSU papers:" + chr(10) + paper_context + chr(10) + KNOWHOW msgs = [{"role":"system","content":system}] for item in history: if isinstance(item, dict): msgs.append({"role":item["role"],"content":item["content"]}) msgs.append({"role":"user","content":tx.text}) resp = client.chat.completions.create(model="llama-3.3-70b-versatile", messages=msgs, max_tokens=500) history.append({"role":"user","content":"Voice: " + tx.text}) history.append({"role":"assistant","content":resp.choices[0].message.content}) return history except Exception as e: history.append({"role":"assistant","content":"Voice error: " + str(e)}) return history # ── PHASE D ──────────────────────────────────────────────── def generate_protocol(experiment_type, specific_params): if not GROQ_KEY: return "Error: Add GROQ_API_KEY" if not experiment_type: return "Select experiment type" try: client = Groq(api_key=GROQ_KEY) paper_context, _ = search_papers(experiment_type, n=4) lab_ctx = { "MCL": "Sylgard 184 PDMS 10:1 ratio 48hr cure. Tygon tubing. 70bpm 5L/min.", "PIV": "Green laser 532nm. Normal velocity 0.5-2.0 m/s. Shear below 5 Pa.", "Thrombogenicity": "Arduino Uno stepper motor 48V. 150mL fresh blood. Sample 0 20 40 60 min. Heska HT5. TAT below 8 ng/mL. PF1.2 below 2.0 nmol/L.", "uPAD": "Whatman filter paper. Wax printer 120C. Jaffe reaction picric acid.", "FSI": "COMSOL ALE mesh. Blood 1060 kg/m3 0.0035 Pa.s.", } extra = next((v for k, v in lab_ctx.items() if k.lower() in experiment_type.lower()), "") system_msg = ("You are CardioLab AI protocol generator for SJSU. Generate COMPLETE protocol with: " "1.OBJECTIVE 2.MATERIALS AND EQUIPMENT 3.SAFETY 4.PROCEDURE 5.DATA COLLECTION " "6.ANALYSIS 7.EXPECTED RESULTS with normal ranges 8.TROUBLESHOOTING. " "Use exact SJSU CardioLab values.") user_msg = "Generate protocol for: " + experiment_type if specific_params and specific_params.strip(): user_msg += chr(10) + "Parameters: " + specific_params if extra: user_msg += chr(10) + "Context: " + extra if paper_context: user_msg += chr(10) + "SJSU papers: " + paper_context[:600] resp = client.chat.completions.create(model="llama-3.3-70b-versatile", messages=[{"role":"system","content":system_msg},{"role":"user","content":user_msg}], max_tokens=1200) return resp.choices[0].message.content except Exception as e: return "Error: " + str(e) def generate_report(data_description, experiment_type, results): if not GROQ_KEY: return "Error: Add GROQ_API_KEY" try: client = Groq(api_key=GROQ_KEY) paper_context, _ = search_papers(experiment_type, n=3) system_msg = ("You are CardioLab AI report writer for SJSU. Generate professional research report with: " "1.ABSTRACT 2.INTRODUCTION 3.MATERIALS AND METHODS 4.RESULTS AND DISCUSSION " "5.CONCLUSION 6.RECOMMENDATIONS 7.REFERENCES. Academic style.") user_msg = "Write report for: " + experiment_type if data_description and data_description.strip(): user_msg += chr(10) + "Description: " + data_description if results and results.strip(): user_msg += chr(10) + "Results: " + results if paper_context: user_msg += chr(10) + "SJSU papers: " + paper_context[:600] resp = client.chat.completions.create(model="llama-3.3-70b-versatile", messages=[{"role":"system","content":system_msg},{"role":"user","content":user_msg}], max_tokens=1500) return resp.choices[0].message.content except Exception as e: return "Error: " + str(e) def generate_hypothesis(research_area, current_findings): if not GROQ_KEY: return "Error: Add GROQ_API_KEY" try: client = Groq(api_key=GROQ_KEY) paper_context, _ = search_papers(research_area, n=3) system_msg = ("You are CardioLab AI research assistant for SJSU. Generate 3 testable hypotheses. " "For each: H0 null, H1 alternative, rationale, suggested experiment, expected outcome.") user_msg = "Hypotheses for: " + research_area if current_findings and current_findings.strip(): user_msg += chr(10) + "Findings: " + current_findings if paper_context: user_msg += chr(10) + "SJSU papers: " + paper_context[:500] resp = client.chat.completions.create(model="llama-3.3-70b-versatile", messages=[{"role":"system","content":system_msg},{"role":"user","content":user_msg}], max_tokens=1000) return resp.choices[0].message.content except Exception as e: return "Error: " + str(e) # ── ANALYSIS TOOLS ───────────────────────────────────────── def analyze_upad_photo(image): if image is None: return None, "Upload a uPAD photo first." try: img = Image.fromarray(image) if not isinstance(image, Image.Image) else image arr = np.array(img); h, w = arr.shape[:2] y1, y2, x1, x2 = int(h*0.35), int(h*0.65), int(w*0.35), int(w*0.65) zone = arr[y1:y2, x1:x2] R = float(np.mean(zone[:,:,0])); G = float(np.mean(zone[:,:,1])); B = float(np.mean(zone[:,:,2])) c = max(0, round(0.018*(R-B)-0.3, 2)) if c < 1.2: s, a = "Normal", "Monitor annually." elif c < 1.5: s, a = "Borderline", "Repeat in 3 months." elif c < 3.0: s, a = "Stage 2 CKD", "Consult nephrologist." elif c < 6.0: s, a = "Stage 3-4 CKD", "Immediate consultation." else: s, a = "Stage 5 CKD", "Emergency care." ri = img.copy() import PIL.ImageDraw as D; D.Draw(ri).rectangle([x1, y1, x2, y2], outline=(0,255,0), width=3) return ri, ("R:" + str(round(R,1)) + " G:" + str(round(G,1)) + " B:" + str(round(B,1)) + chr(10) + "Creatinine: " + str(c) + " mg/dL" + chr(10) + "Stage: " + s + chr(10) + "Action: " + a) except Exception as e: return None, "Error: " + str(e) def mk_chart(fn, title, bg, fg, gc, ac, pb): fig2, ax = plt.subplots(figsize=(8,5)); fig2.patch.set_facecolor(bg); ax.set_facecolor(pb) fn(ax); ax.set_title(title, color=fg, fontweight="bold", fontsize=13, pad=8) ax.tick_params(colors=ac, labelsize=10); ax.grid(True, alpha=0.3, color=gc, linestyle="--") for sp in ["top","right"]: ax.spines[sp].set_visible(False) for sp in ["bottom","left"]: ax.spines[sp].set_color(gc) plt.tight_layout(); buf = io.BytesIO() plt.savefig(buf, format="png", facecolor=bg, bbox_inches="tight", dpi=130); buf.seek(0) res = Image.open(buf).copy(); plt.close(); return res def analyze_piv_csv(file, theme="White"): if file is None: return None, None, None, None, "Upload PIV CSV first." try: df = pd.read_csv(file.name); cols = [c.lower().strip() for c in df.columns]; df.columns = cols num_cols = df.select_dtypes(include=[np.number]).columns.tolist() if not num_cols: return None, None, None, None, "No numeric columns." bg = "#fff" if theme=="White" else "#0a1628"; fg = "#1a202c" if theme=="White" else "white" gc = "#e2e8f0" if theme=="White" else "#2d4a8a"; ac = "#4a5568" if theme=="White" else "#a8b2d8" pb = "#f7fafc" if theme=="White" else "#132340" x = np.arange(len(df)) vc = next((c for c in cols if any(k in c for k in ["vel","speed","v_mag"])), num_cols[0] if num_cols else None) sc2 = next((c for c in cols if any(k in c for k in ["shear","stress","tau","wss"])), num_cols[1] if len(num_cols)>1 else None) tc = next((c for c in cols if "time" in c or "frame" in c), None); xv = df[tc] if tc else x def pv(ax): if vc: ax.plot(xv, df[vc], color="#c1121f", linewidth=2.5, marker="o", markersize=5) ax.fill_between(xv, df[vc], alpha=0.15, color="#c1121f") ax.axhline(y=2.0, color="#f59e0b", linestyle="--", linewidth=2, label="Risk 2.0 m/s") ax.set_ylabel("Velocity (m/s)", color=ac); ax.legend(fontsize=9, labelcolor=fg, facecolor=pb) def ps(ax): if sc2: xp = xv.values if tc else x ax.plot(xp, df[sc2], color="#0057a8", linewidth=2.5, marker="s", markersize=5) ax.fill_between(xp, df[sc2], alpha=0.15, color="#0057a8") ax.axhline(y=5, color="#f59e0b", linestyle="--", linewidth=2, label="Caution 5 Pa") ax.axhline(y=10, color="#c1121f", linestyle="--", linewidth=2, label="Risk 10 Pa") ax.set_ylabel("Shear (Pa)", color=ac); ax.legend(fontsize=9, labelcolor=fg, facecolor=pb) def psc(ax): if vc and sc2: s3 = ax.scatter(df[vc], df[sc2], c=x, cmap="RdYlGn_r", s=90, edgecolors=fg, linewidth=0.5, zorder=5) cb = plt.colorbar(s3, ax=ax, label="Time"); cb.ax.yaxis.label.set_color(fg); cb.ax.tick_params(colors=ac) ax.axvline(x=2.0, color="#f59e0b", linestyle="--", linewidth=2); ax.axhline(y=10, color="#c1121f", linestyle="--", linewidth=2) ax.set_xlabel("Velocity (m/s)", color=ac); ax.set_ylabel("Shear (Pa)", color=ac) def psum(ax): ax.axis("off"); risk = [] st = "CLINICAL SUMMARY" + chr(10) + "="*20 + chr(10) + chr(10) for col in num_cols[:3]: mn = round(df[col].mean(), 3); mx = round(df[col].max(), 3) st += col[:14] + ":" + chr(10) + " Mean: " + str(mn) + chr(10) + " Max: " + str(mx) + chr(10) + chr(10) if "vel" in col and mx > 2.0: risk.append("HIGH VELOCITY") if "shear" in col and mx > 10: risk.append("HIGH SHEAR") bc = "#c1121f" if risk else "#2ecc71" st += "="*20 + chr(10) + ("OVERALL: HIGH RISK" if risk else "OVERALL: LOW RISK") ax.text(0.05, 0.97, st, transform=ax.transAxes, color=fg, fontsize=10, va="top", fontfamily="monospace", bbox=dict(boxstyle="round,pad=0.8", facecolor=pb, edgecolor=bc, linewidth=2.5)) i1 = mk_chart(pv, "Velocity Profile", bg, fg, gc, ac, pb) i2 = mk_chart(ps, "Wall Shear Stress", bg, fg, gc, ac, pb) i3 = mk_chart(psc, "Velocity vs Shear", bg, fg, gc, ac, pb) i4 = mk_chart(psum, "Clinical Summary", bg, fg, gc, ac, pb) ai = "" if GROQ_KEY: try: client = Groq(api_key=GROQ_KEY) resp = client.chat.completions.create(model="llama-3.3-70b-versatile", messages=[{"role":"system","content":"PIV expert SJSU CardioLab."}, {"role":"user","content":"PIV from 27mm SJM Regent:" + chr(10) + df.describe().to_string()[:500]}], max_tokens=250) ai = chr(10) + "AI: " + resp.choices[0].message.content except: pass return i1, i2, i3, i4, "PIV: " + str(len(df)) + " rows" + ai except Exception as e: return None, None, None, None, "Error: " + str(e) def analyze_tgt_csv(file, theme="White"): if file is None: return None, None, None, None, "Upload TGT CSV first." try: df = pd.read_csv(file.name); cols = [c.lower().strip() for c in df.columns]; df.columns = cols num_cols = df.select_dtypes(include=[np.number]).columns.tolist() bg = "#fff" if theme=="White" else "#0a1628"; fg = "#1a202c" if theme=="White" else "white" gc = "#e2e8f0" if theme=="White" else "#2d4a8a"; ac = "#4a5568" if theme=="White" else "#a8b2d8" pb = "#f7fafc" if theme=="White" else "#132340" tc = next((c for c in cols if "time" in c or "min" in c), None) tatc = next((c for c in cols if "tat" in c), num_cols[0] if num_cols else None) pfc = next((c for c in cols if "pf" in c), num_cols[1] if len(num_cols)>1 else None) hc = next((c for c in cols if "hemo" in c), num_cols[2] if len(num_cols)>2 else None) plc = next((c for c in cols if "platelet" in c or "plt" in c), num_cols[3] if len(num_cols)>3 else None) def mk2(dc, color, yl, lim, ll, title, bar=False): def fn(ax): if dc and dc in df.columns: xp = df[tc].values if tc else range(len(df)); yp = df[dc].values if bar: bs = ax.bar(range(len(yp)), yp, color=color, alpha=0.85, edgecolor=bg, width=0.6) for b, v in zip(bs, yp): ax.text(b.get_x()+b.get_width()/2, b.get_height()+0.5, str(round(v,1)), ha="center", va="bottom", color=fg, fontsize=10, fontweight="bold") else: ax.plot(xp, yp, color=color, linewidth=3, marker="o", markersize=8) ax.fill_between(xp, yp, alpha=0.15, color=color) for xi, yi in zip(xp, yp): ax.annotate(str(round(yi,1)), (xi, yi), textcoords="offset points", xytext=(0,10), ha="center", color=fg, fontsize=10, fontweight="bold") ax.axhline(y=lim, color="#f59e0b", linestyle="--", linewidth=2.5, label=ll) ax.legend(fontsize=10, labelcolor=fg, facecolor=pb); ax.set_ylabel(yl, color=ac) mv = round(float(np.max(yp)), 2) ax.set_title(title + chr(10) + "Max: " + str(mv) + " - " + ("HIGH" if mv>lim else "NORMAL"), color=fg, fontweight="bold", fontsize=12) return mk_chart(fn, title, bg, fg, gc, ac, pb) i1 = mk2(tatc, "#c1121f", "TAT (ng/mL)", 8, "Normal: 8", "TAT") i2 = mk2(pfc, "#0057a8", "PF1.2", 2.0, "Normal: 2.0", "PF1.2") i3 = mk2(hc, "#2ecc71", "Free Hgb (mg/L)", 20, "Normal: 20", "Free Hemoglobin", bar=True) i4 = mk2(plc, "#e8a020", "Platelets", 150, "Normal>150", "Platelets") ai = "" if GROQ_KEY: try: client = Groq(api_key=GROQ_KEY) resp = client.chat.completions.create(model="llama-3.3-70b-versatile", messages=[{"role":"system","content":"Hematology expert. Thrombogenicity risk."}, {"role":"user","content":"TGT:" + chr(10) + df.describe().to_string()[:500]}], max_tokens=250) ai = chr(10) + "AI: " + resp.choices[0].message.content except: pass return i1, i2, i3, i4, "TGT: " + str(len(df)) + " rows" + ai except Exception as e: return None, None, None, None, "Error: " + str(e) def generate_image(prompt): if not prompt.strip(): return None, "Enter description.", "" if not HF_TOKEN: return None, "Add HF_TOKEN.", "" try: enhanced, desc = prompt, "" if GROQ_KEY: try: client = Groq(api_key=GROQ_KEY) resp = client.chat.completions.create(model="llama-3.3-70b-versatile", messages=[{"role":"system","content":"Format: DESCRIPTION: [2 sentences] PROMPT: [detailed image prompt]"}, {"role":"user","content":"Biomedical image: " + prompt}], max_tokens=200) full = resp.choices[0].message.content if "DESCRIPTION:" in full and "PROMPT:" in full: desc = full.split("DESCRIPTION:")[1].split("PROMPT:")[0].strip() enhanced = full.split("PROMPT:")[1].strip() except: pass headers = {"Authorization": "Bearer " + HF_TOKEN, "Content-Type": "application/json"} for url in ["https://router.huggingface.co/hf-inference/models/black-forest-labs/FLUX.1-schnell", "https://router.huggingface.co/hf-inference/models/stabilityai/stable-diffusion-xl-base-1.0"]: try: r = requests.post(url, headers=headers, json={"inputs":enhanced,"parameters":{"num_inference_steps":8}}, timeout=60) if r.status_code == 200: return Image.open(io.BytesIO(r.content)), "Generated!", desc except: continue return None, "Models busy.", desc except Exception as e: return None, "Error: " + str(e), "" def piv_manual(v, s, h): vr = "HIGH-stenosis" if float(v)>2.0 else "NORMAL" sr = "HIGH-thrombosis" if float(s)>10 else "ELEVATED" if float(s)>5 else "NORMAL" return "Velocity: " + str(v) + " m/s - " + vr + chr(10) + "Shear: " + str(s) + " Pa - " + sr + chr(10) + "HR: " + str(h) + " bpm" def tgt_manual(t, p, h, pl, tm): risk = sum([float(t)>15, float(p)>2.0, float(h)>50, float(pl)<150]) return "TAT:" + str(t) + " PF1.2:" + str(p) + chr(10) + "Hemo:" + str(h) + " Plt:" + str(pl) + chr(10) + ("HIGH RISK" if risk>=3 else "MODERATE" if risk>=2 else "LOW RISK") # ── UI ───────────────────────────────────────────────────── with gr.Blocks(title="CardioLab AI - SJSU", css=CSS) as demo: gr.HTML(HEADER) gr.HTML("""

RAG Active: 417 chunks from 16 SJSU papers · Fine-tuned Model · Select model using radio buttons in Chat tab

""") with gr.Tabs(): with gr.Tab("Chat"): with gr.Row(): with gr.Column(scale=1, min_width=200): gr.HTML("""

CardioLab

Conversations

""") new_chat_btn = gr.Button("+ New Chat", variant="secondary") session_dropdown = gr.Dropdown(choices=get_session_list(), label="Saved Sessions", interactive=True) load_btn = gr.Button("Load Session", variant="primary") session_name_box = gr.Textbox(placeholder="Name this session...", label="Session Name", lines=1) with gr.Row(): save_btn = gr.Button("Save", variant="primary", scale=2) delete_btn = gr.Button("Del", variant="secondary", scale=1) session_status = gr.Textbox(label="", lines=1, interactive=False, container=False) with gr.Column(scale=4): chat_model_radio = gr.Radio( choices=list(CHAT_MODELS.keys()), value="Llama 3.3 70B (Best)", label="Select AI Model", container=True ) chatbot = gr.Chatbot(label="", height=400, show_label=False, container=False) with gr.Row(): msg_box = gr.Textbox(placeholder="Ask anything — AI searches 16 SJSU papers + PubMed...", label="", lines=2, scale=5, container=False) with gr.Column(scale=1, min_width=80): send_btn = gr.Button("Send", variant="primary") clear_btn = gr.Button("Clear", variant="secondary") send_btn.click(research_chat, inputs=[msg_box, chatbot, chat_model_radio], outputs=[msg_box, chatbot]) msg_box.submit(research_chat, inputs=[msg_box, chatbot, chat_model_radio], outputs=[msg_box, chatbot]) clear_btn.click(lambda: ([], ""), outputs=[chatbot, msg_box]) new_chat_btn.click(new_chat, outputs=[chatbot, msg_box, session_status]) save_btn.click(save_session, inputs=[chatbot, session_name_box], outputs=[session_status, session_dropdown]) load_btn.click(load_session, inputs=session_dropdown, outputs=[chatbot, session_status]) delete_btn.click(delete_session, inputs=session_dropdown, outputs=[session_status, session_dropdown]) with gr.Tab("Voice"): voice_chatbot = gr.Chatbot(label="", height=360, show_label=False) audio_input = gr.Audio(sources=["microphone"], type="filepath", label="Record Question") with gr.Row(): voice_btn = gr.Button("Ask by Voice", variant="primary") voice_clear = gr.Button("Clear", variant="secondary") voice_btn.click(voice_chat, inputs=[audio_input, voice_chatbot], outputs=voice_chatbot) voice_clear.click(lambda: [], outputs=voice_chatbot) with gr.Tab("Papers"): gr.Markdown("### Search PubMed + Semantic Scholar + SJSU ScholarWorks") with gr.Row(): search_input = gr.Textbox(placeholder="e.g. bileaflet mechanical heart valve thrombogenicity hemodynamics", label="Research Topic", scale=4) search_btn = gr.Button("Search", variant="primary", scale=1) search_output = gr.Textbox(label="Results", lines=22) search_btn.click(quick_search, inputs=search_input, outputs=search_output) search_input.submit(quick_search, inputs=search_input, outputs=search_output) with gr.Tab("PIV CSV"): with gr.Row(): piv_file = gr.File(label="Upload PIV CSV", file_types=[".csv"], scale=3) piv_theme = gr.Radio(["White","Dark"], value="White", label="Theme", scale=1) piv_btn = gr.Button("Analyze PIV Data", variant="primary") piv_result = gr.Textbox(label="AI Analysis", lines=4) with gr.Row(): piv_c1 = gr.Image(label="Velocity Profile", type="pil") piv_c2 = gr.Image(label="Shear Stress", type="pil") with gr.Row(): piv_c3 = gr.Image(label="Velocity vs Shear", type="pil") piv_c4 = gr.Image(label="Clinical Summary", type="pil") piv_btn.click(analyze_piv_csv, inputs=[piv_file, piv_theme], outputs=[piv_c1, piv_c2, piv_c3, piv_c4, piv_result]) with gr.Tab("TGT CSV"): with gr.Row(): tgt_file = gr.File(label="Upload TGT CSV", file_types=[".csv"], scale=3) tgt_theme = gr.Radio(["White","Dark"], value="White", label="Theme", scale=1) tgt_btn = gr.Button("Analyze TGT Data", variant="primary") tgt_result = gr.Textbox(label="AI Assessment", lines=4) with gr.Row(): tgt_c1 = gr.Image(label="TAT", type="pil"); tgt_c2 = gr.Image(label="PF1.2", type="pil") with gr.Row(): tgt_c3 = gr.Image(label="Hemoglobin", type="pil"); tgt_c4 = gr.Image(label="Platelets", type="pil") tgt_btn.click(analyze_tgt_csv, inputs=[tgt_file, tgt_theme], outputs=[tgt_c1, tgt_c2, tgt_c3, tgt_c4, tgt_result]) with gr.Tab("uPAD"): with gr.Row(): with gr.Column(): photo_input = gr.Image(label="Upload uPAD Photo", type="numpy", height=260) analyze_btn = gr.Button("Analyze uPAD Photo", variant="primary") with gr.Column(): photo_img = gr.Image(label="Detection Zone", type="pil", height=260) photo_text = gr.Textbox(label="CKD Result", lines=8) analyze_btn.click(analyze_upad_photo, inputs=photo_input, outputs=[photo_img, photo_text]) gr.Markdown("**Manual RGB:**") with gr.Row(): r = gr.Number(label="R", value=210); g = gr.Number(label="G", value=140); b = gr.Number(label="B", value=80) out3 = gr.Textbox(label="Result", lines=3) gr.Button("Analyze RGB", variant="secondary").click( lambda r, g, b: "Creatinine: " + str(max(0,round(0.02*(r-b)-0.5,2))) + " mg/dL" + chr(10) + ("Normal" if max(0,round(0.02*(r-b)-0.5,2)) < 1.2 else "Borderline" if max(0,round(0.02*(r-b)-0.5,2)) < 1.5 else "CKD"), inputs=[r, g, b], outputs=out3) with gr.Tab("AI Image"): with gr.Row(): img_prompt = gr.Textbox(placeholder="e.g. 27mm bileaflet mechanical heart valve cross section", label="Describe image", lines=2, scale=4) with gr.Column(scale=1): img_btn = gr.Button("Generate", variant="primary") img_status = gr.Textbox(label="Status", lines=1) img_desc = gr.Textbox(label="AI Description", lines=2, interactive=False) img_output = gr.Image(label="Generated Image", type="pil", height=400) img_btn.click(generate_image, inputs=img_prompt, outputs=[img_output, img_status, img_desc]) with gr.Tab("PIV Manual"): with gr.Row(): with gr.Column(): v = gr.Number(label="Max Velocity m/s", value=1.8) s = gr.Number(label="Wall Shear Pa", value=6.5) h = gr.Number(label="Heart Rate bpm", value=72) piv_out = gr.Textbox(label="Result", lines=4) gr.Button("Analyze PIV", variant="primary").click(piv_manual, inputs=[v, s, h], outputs=piv_out) with gr.Tab("TGT Manual"): with gr.Row(): with gr.Column(): t1 = gr.Number(label="TAT ng/mL", value=18); t2 = gr.Number(label="PF1.2", value=2.5) t3 = gr.Number(label="Hemoglobin mg/L", value=60); t4 = gr.Number(label="Platelets", value=140) t5 = gr.Number(label="Time min", value=40); out2 = gr.Textbox(label="Result", lines=6) gr.Button("Analyze TGT", variant="primary").click(tgt_manual, inputs=[t1, t2, t3, t4, t5], outputs=out2) with gr.Tab("Protocol Generator"): gr.Markdown("### Generate complete lab protocols from SJSU CardioLab knowledge") with gr.Row(): with gr.Column(scale=1): proto_type = gr.Dropdown( choices=["MCL Setup", "PIV Experiment", "Thrombogenicity Tester Blood Clotting Test", "uPAD Fabrication", "uPAD Creatinine Test", "FSI COMSOL Simulation", "Valve Testing"], value="Thrombogenicity Tester Blood Clotting Test", label="Experiment Type") proto_params = gr.Textbox(placeholder="e.g. 27mm SJM valve 70bpm porcine blood", label="Specific Parameters", lines=2) proto_btn = gr.Button("Generate Protocol", variant="primary") with gr.Column(scale=2): proto_output = gr.Textbox(label="Generated Protocol", lines=28) proto_btn.click(generate_protocol, inputs=[proto_type, proto_params], outputs=proto_output) with gr.Tab("Report Writer"): gr.Markdown("### Generate professional research reports") with gr.Row(): with gr.Column(scale=1): report_exp = gr.Dropdown( choices=["MCL PIV Flow Analysis", "TGT Thrombogenicity Study", "uPAD CKD Detection", "FSI Simulation Study", "Heart Valve Comparison"], value="TGT Thrombogenicity Study", label="Study Type") report_desc = gr.Textbox(placeholder="e.g. TGT with 27mm SJM bileaflet at 70bpm 150mL porcine blood", label="Experiment Description", lines=3) report_results = gr.Textbox(placeholder="e.g. TAT=12.3 PF1.2=2.8 Hemo=45 Plt=142", label="Your Results", lines=2) report_btn = gr.Button("Generate Report", variant="primary") with gr.Column(scale=2): report_output = gr.Textbox(label="Generated Report", lines=28) report_btn.click(generate_report, inputs=[report_desc, report_exp, report_results], outputs=report_output) with gr.Tab("Hypothesis Generator"): gr.Markdown("### Generate testable research hypotheses") with gr.Row(): with gr.Column(scale=1): hyp_area = gr.Dropdown( choices=["Bileaflet MHV Thrombogenicity", "uPAD CKD Detection Accuracy", "PIV Flow Characterization", "FSI Simulation Validation", "Valve Design Comparison"], value="Bileaflet MHV Thrombogenicity", label="Research Area") hyp_findings = gr.Textbox(placeholder="Current observations from your experiments", label="Current Findings", lines=3) hyp_btn = gr.Button("Generate Hypotheses", variant="primary") with gr.Column(scale=2): hyp_output = gr.Textbox(label="Research Hypotheses", lines=25) hyp_btn.click(generate_hypothesis, inputs=[hyp_area, hyp_findings], outputs=hyp_output) gr.HTML("""

CardioLab AI v40 · SJSU Biomedical Engineering · Inspired by Biomni Stanford · Apache 2.0 · $0 Cost

""") demo.launch()