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app.py

@@ -12,17 +12,15 @@ from PIL import Image
 GROQ_KEY = os.environ.get("GROQ_API_KEY", "")
 HF_TOKEN = os.environ.get("HF_TOKEN", "")
 
-KNOWHOW = ("SJSU CardioLab: "
-    "MCL: Sylgard 184 PDMS 10:1 ratio 48hr cure green laser PIV 70bpm 5L/min. "
+KNOWHOW = ("MCL: Sylgard 184 PDMS 10:1 ratio 48hr cure green laser PIV 70bpm 5L/min. "
     "TGT: Arduino Uno Stepper Motor 150mL blood sampled at 0 20 40 60min measures TAT PF1.2 hemolysis platelets. "
     "uPAD: Jaffe reaction creatinine plus picric acid gives orange-red color normal 0.6-1.2 mg/dL CKD above 1.5. "
-    "MHV: 27mm SJM Regent bileaflet also trileaflet monoleaflet pediatric. "
-    "Equipment: Heska HT5 hematology analyzer time-resolved PIV Tygon tubing Arduino Uno.")
+    "MHV: 27mm SJM Regent bileaflet also trileaflet monoleaflet pediatric.")
 
 CSS = """
 body, .gradio-container { background: #f0f4f8 !important; }
-.tab-nav { background: #ffffff !important; border-bottom: 2px solid #e2e8f0 !important; padding: 0 10px !important; }
-.tab-nav button { background: #f7fafc !important; color: #2d3748 !important; border: 1px solid #e2e8f0 !important; border-radius: 8px 8px 0 0 !important; padding: 12px 18px !important; font-weight: 600 !important; margin-top: 6px !important; }
+.tab-nav { background: #ffffff !important; border-bottom: 2px solid #e2e8f0 !important; padding: 4px 5px 0 5px !important; display: flex !important; flex-wrap: wrap !important; gap: 3px !important; overflow: visible !important; }
+.tab-nav button { background: #f7fafc !important; color: #2d3748 !important; border: 1px solid #e2e8f0 !important; border-radius: 6px 6px 0 0 !important; padding: 8px 10px !important; font-weight: 600 !important; font-size: 0.8em !important; margin: 0 !important; white-space: nowrap !important; min-width: 0 !important; }
 .tab-nav button:hover { background: #ebf4ff !important; color: #1a237e !important; }
 .tab-nav button.selected { background: linear-gradient(135deg, #e63946, #c1121f) !important; color: #ffffff !important; font-weight: 700 !important; }
 button.primary { background: linear-gradient(135deg, #e63946 0%, #c1121f 100%) !important; color: white !important; border: none !important; border-radius: 8px !important; font-weight: 700 !important; }
@@ -33,112 +31,141 @@ textarea, input[type=number] { background: #f7fafc !important; color: #1a202c !i
 label span { color: #2b6cb0 !important; font-weight: 600 !important; font-size: 0.85em !important; text-transform: uppercase !important; }
 """
 
-# ─── PIV CSV ANALYSIS ────────────────────────────────────────────
+def get_pubmed(query, n=5):
+    try:
+        r = requests.get("https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi",
+            params={"db":"pubmed","term":query+" AND (mechanical heart valve OR microfluidic OR CKD OR thrombogenicity)","retmax":n,"retmode":"json","sort":"date"},timeout=10)
+        ids = r.json()["esearchresult"]["idlist"]
+        if not ids: return ""
+        return chr(10).join(["https://pubmed.ncbi.nlm.nih.gov/"+i for i in ids])
+    except: return ""
+
+def quick_search(query):
+    if not query.strip(): return "Please enter a topic."
+    pubmed = get_pubmed(query, n=8)
+    try:
+        r = requests.get("https://api.semanticscholar.org/graph/v1/paper/search",
+            params={"query":query+" biomedical","limit":5,"fields":"title,year,url,citationCount"},timeout=10)
+        papers = r.json().get("data",[])
+        scholar = chr(10).join([p.get("title","")[:80]+" ("+str(p.get("year",""))+")"+chr(10)+"   "+p.get("url","") for p in papers if p.get("url","")])
+    except: scholar = ""
+    return "PUBMED:"+chr(10)+pubmed+chr(10)+chr(10)+"SCHOLAR:"+chr(10)+scholar
+
+def research_chat(message, 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:
+        client = Groq(api_key=GROQ_KEY)
+        msgs = [{"role":"system","content":"You are CardioLab AI. Expert in MHV MCL PIV TGT uPAD CKD FSI. Never invent URLs. "+KNOWHOW}]
+        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="llama-3.3-70b-versatile",messages=msgs,max_tokens=700)
+        answer = resp.choices[0].message.content
+        pubmed = get_pubmed(message, n=3)
+        if pubmed: answer += chr(10)+chr(10)+"PUBMED:"+chr(10)+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 your question 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")
+        msgs = [{"role":"system","content":"You are CardioLab AI. "+KNOWHOW}]
+        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
+
 def analyze_piv_csv(file):
     if file is None:
-        return None, "Please upload a PIV CSV file."
+        return None, "Please upload a PIV CSV file 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, "No numeric columns found. Check your CSV file."
 
         fig, axes = plt.subplots(2, 2, figsize=(14, 10))
         fig.patch.set_facecolor("#0d1b3e")
-        fig.suptitle("PIV Data Analysis — SJSU CardioLab MCL", color="white", fontsize=16, fontweight="bold", y=1.02)
-
-        colors = ["#e63946", "#4361ee", "#2ecc71", "#e67e22"]
-
-        # Plot 1 — Velocity over time or position
-        ax1 = axes[0, 0]
-        ax1.set_facecolor("#1a2744")
-        vel_col = next((c for c in cols if "vel" in c or "v_" in c or "u" == c or "speed" in c), cols[0] if len(cols)>0 else None)
-        x_col = next((c for c in cols if "time" in c or "x" in c or "pos" in c or "frame" in c), None)
-        if vel_col and x_col:
-            ax1.plot(df[x_col], df[vel_col], color="#e63946", linewidth=2, label=vel_col)
-            ax1.set_xlabel(x_col, color="#a8b2d8")
-            ax1.set_ylabel(vel_col, color="#a8b2d8")
-        elif vel_col:
-            ax1.plot(df[vel_col], color="#e63946", linewidth=2)
-            ax1.set_ylabel(vel_col, color="#a8b2d8")
-        else:
-            ax1.plot(df.iloc[:,0], color="#e63946", linewidth=2)
-        ax1.set_title("Velocity Profile", color="white", fontweight="bold")
-        ax1.tick_params(colors="#a8b2d8")
-        ax1.grid(True, alpha=0.2, color="#2d4a8a")
-        ax1.spines["bottom"].set_color("#2d4a8a")
-        ax1.spines["left"].set_color("#2d4a8a")
-        ax1.spines["top"].set_visible(False)
-        ax1.spines["right"].set_visible(False)
-
-        # Plot 2 — Shear stress if available
-        ax2 = axes[0, 1]
-        ax2.set_facecolor("#1a2744")
-        shear_col = next((c for c in cols if "shear" in c or "stress" in c or "tau" in c or "wss" in c), None)
+        fig.suptitle("PIV Data Analysis — SJSU CardioLab MCL", color="white", fontsize=16, fontweight="bold")
+
+        def style_ax(ax, title, ylabel):
+            ax.set_facecolor("#1a2744")
+            ax.set_title(title, color="white", fontweight="bold")
+            ax.set_ylabel(ylabel, color="#a8b2d8")
+            ax.tick_params(colors="#a8b2d8")
+            ax.grid(True, alpha=0.2, color="#2d4a8a")
+            for spine in ["top","right"]: ax.spines[spine].set_visible(False)
+            for spine in ["bottom","left"]: ax.spines[spine].set_color("#2d4a8a")
+
+        x = range(len(df))
+        vel_col = next((c for c in cols if any(k in c for k in ["vel","speed","u","v_mag"])), num_cols[0] if num_cols else None)
+        shear_col = 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)
+
+        # Plot 1 - Velocity
+        ax1 = axes[0,0]
+        if vel_col:
+            ax1.plot(df[vel_col], color="#e63946", linewidth=2.5, marker="o", markersize=4)
+            ax1.axhline(y=2.0, color="#ffd700", linestyle="--", linewidth=1.5, label="Risk (2.0 m/s)")
+            ax1.fill_between(x, df[vel_col], alpha=0.2, color="#e63946")
+            ax1.legend(fontsize=8, labelcolor="white", facecolor="#1a2744")
+        style_ax(ax1, "Velocity Profile", "Velocity (m/s)")
+
+        # Plot 2 - Shear
+        ax2 = axes[0,1]
         if shear_col:
-            ax2.fill_between(range(len(df)), df[shear_col], alpha=0.7, color="#4361ee")
-            ax2.plot(df[shear_col], color="#4361ee", linewidth=2)
-            ax2.axhline(y=5, color="#e63946", linestyle="--", linewidth=1.5, label="Risk threshold (5 Pa)")
-            ax2.axhline(y=10, color="#ff4444", linestyle="--", linewidth=1.5, label="High risk (10 Pa)")
-            ax2.set_ylabel("Shear Stress (Pa)", color="#a8b2d8")
+            ax2.plot(df[shear_col], color="#4361ee", linewidth=2.5, marker="s", markersize=4)
+            ax2.axhline(y=5, color="#ffd700", linestyle="--", linewidth=1.5, label="Caution (5 Pa)")
+            ax2.axhline(y=10, color="#e63946", linestyle="--", linewidth=1.5, label="High risk (10 Pa)")
+            ax2.fill_between(x, df[shear_col], alpha=0.2, color="#4361ee")
             ax2.legend(fontsize=8, labelcolor="white", facecolor="#1a2744")
-        else:
-            # Plot second numeric column
-            num_cols = df.select_dtypes(include=[np.number]).columns.tolist()
-            if len(num_cols) >= 2:
-                ax2.fill_between(range(len(df)), df[num_cols[1]], alpha=0.7, color="#4361ee")
-                ax2.plot(df[num_cols[1]], color="#4361ee", linewidth=2)
-                ax2.set_ylabel(num_cols[1], color="#a8b2d8")
-        ax2.set_title("Shear Stress / Secondary Variable", color="white", fontweight="bold")
-        ax2.tick_params(colors="#a8b2d8")
-        ax2.grid(True, alpha=0.2, color="#2d4a8a")
-        ax2.spines["bottom"].set_color("#2d4a8a")
-        ax2.spines["left"].set_color("#2d4a8a")
-        ax2.spines["top"].set_visible(False)
-        ax2.spines["right"].set_visible(False)
-
-        # Plot 3 — Distribution histogram
-        ax3 = axes[1, 0]
-        ax3.set_facecolor("#1a2744")
-        num_cols = df.select_dtypes(include=[np.number]).columns.tolist()
-        if num_cols:
-            ax3.hist(df[num_cols[0]].dropna(), bins=30, color="#2ecc71", alpha=0.8, edgecolor="#1a2744")
-            ax3.set_xlabel(num_cols[0], color="#a8b2d8")
-            ax3.set_ylabel("Count", color="#a8b2d8")
-        ax3.set_title("Value Distribution", color="white", fontweight="bold")
-        ax3.tick_params(colors="#a8b2d8")
-        ax3.grid(True, alpha=0.2, color="#2d4a8a")
-        ax3.spines["bottom"].set_color("#2d4a8a")
-        ax3.spines["left"].set_color("#2d4a8a")
-        ax3.spines["top"].set_visible(False)
-        ax3.spines["right"].set_visible(False)
-
-        # Plot 4 — Summary stats
-        ax4 = axes[1, 1]
+        elif len(num_cols)>1:
+            ax2.plot(df[num_cols[1]], color="#4361ee", linewidth=2.5)
+        style_ax(ax2, "Shear Stress", "Shear Stress (Pa)")
+
+        # Plot 3 - Distribution
+        ax3 = axes[1,0]
+        if vel_col:
+            ax3.hist(df[vel_col].dropna(), bins=20, color="#2ecc71", alpha=0.8, edgecolor="#0d1b3e")
+        style_ax(ax3, "Velocity Distribution", "Count")
+        ax3.set_xlabel("Value", color="#a8b2d8")
+
+        # Plot 4 - Stats
+        ax4 = axes[1,1]
         ax4.set_facecolor("#1a2744")
         ax4.axis("off")
-        num_cols = df.select_dtypes(include=[np.number]).columns.tolist()
-        summary_text = "SUMMARY STATISTICS" + chr(10) + "━"*22 + chr(10)
-        risk_flags = []
-        for col in num_cols[:4]:
-            mean_val = df[col].mean()
-            max_val = df[col].max()
-            min_val = df[col].min()
-            summary_text += f"{col[:15]}:" + chr(10)
-            summary_text += f"  Mean: {mean_val:.3f}" + chr(10)
-            summary_text += f"  Max:  {max_val:.3f}" + chr(10)
-            summary_text += f"  Min:  {min_val:.3f}" + chr(10)
-            if "vel" in col.lower() and max_val > 2.0:
-                risk_flags.append("HIGH VELOCITY - stenosis risk")
-            if "shear" in col.lower() and max_val > 10:
-                risk_flags.append("HIGH SHEAR - thrombosis risk")
-        if risk_flags:
-            summary_text += chr(10) + "RISK FLAGS:" + chr(10)
-            for flag in risk_flags:
-                summary_text += "  ⚠ " + flag + chr(10)
-        ax4.text(0.05, 0.95, summary_text, transform=ax4.transAxes,
-                color="white", fontsize=9, verticalalignment="top",
-                fontfamily="monospace",
-                bbox=dict(boxstyle="round", facecolor="#0d1b3e", edgecolor="#4361ee", alpha=0.8))
+        stats = ""
+        risk = []
+        for col in num_cols[:3]:
+            mn = df[col].mean()
+            mx = df[col].max()
+            stats += col[:12]+":"+chr(10)+"  Mean: "+str(round(mn,3))+chr(10)+"  Max:  "+str(round(mx,3))+chr(10)+chr(10)
+            if "vel" in col and mx > 2.0: risk.append("HIGH VELOCITY: stenosis risk")
+            if "shear" in col and mx > 10: risk.append("HIGH SHEAR: thrombosis risk")
+        if risk: stats += "RISK FLAGS:"+chr(10)+"  "+chr(10)+"  ".join(risk)
+        ax4.text(0.05, 0.95, "SUMMARY STATS"+chr(10)+"━"*18+chr(10)+stats, transform=ax4.transAxes,
+            color="white", fontsize=9, va="top", fontfamily="monospace",
+            bbox=dict(boxstyle="round", facecolor="#0d1b3e", edgecolor="#4361ee"))
 
         plt.tight_layout()
         buf = io.BytesIO()
@@ -147,106 +174,83 @@ def analyze_piv_csv(file):
         img = Image.open(buf)
         plt.close()
 
-        # AI analysis
-        ai_summary = ""
+        ai_text = ""
         if GROQ_KEY:
             try:
                 client = Groq(api_key=GROQ_KEY)
-                stats = df.describe().to_string()
-                msgs = [{"role":"system","content":"You are a PIV flow analysis expert for SJSU CardioLab. Analyze the statistics from the PIV CSV data and provide a clinical interpretation. Mention velocity ranges, shear stress levels, risk of stenosis or thrombosis, and recommendations."}]
-                msgs.append({"role":"user","content":"Analyze this PIV data from our Mock Circulatory Loop with 27mm SJM Regent MHV at 70bpm 5L/min:"+chr(10)+stats[:1000]})
-                resp = client.chat.completions.create(model="llama-3.3-70b-versatile",messages=msgs,max_tokens=400)
-                ai_summary = chr(10)+"━"*30+chr(10)+"AI CLINICAL INTERPRETATION:"+chr(10)+resp.choices[0].message.content
+                msgs = [{"role":"system","content":"You are a PIV expert for SJSU CardioLab. Analyze PIV statistics and give clinical interpretation about velocity, shear stress, stenosis and thrombosis risk."}]
+                msgs.append({"role":"user","content":"PIV data stats from 27mm SJM Regent MHV at 70bpm 5L/min:"+chr(10)+df.describe().to_string()[:800]})
+                resp = client.chat.completions.create(model="llama-3.3-70b-versatile",messages=msgs,max_tokens=350)
+                ai_text = chr(10)+"━"*25+chr(10)+"AI ANALYSIS:"+chr(10)+resp.choices[0].message.content
             except: pass
 
-        result_text = ("PIV CSV ANALYSIS COMPLETE"+chr(10)+
-            "Rows: "+str(len(df))+" | Columns: "+str(len(df.columns))+chr(10)+
-            "Columns: "+", ".join(df.columns.tolist())+chr(10)+ai_summary)
-
-        return img, result_text
-
+        return img, "PIV CSV LOADED: "+str(len(df))+" rows, "+str(len(df.columns))+" columns"+chr(10)+"Columns: "+", ".join(df.columns.tolist())+ai_text
     except Exception as e:
-        return None, "Error reading CSV: "+str(e)+chr(10)+"Make sure your CSV has headers and numeric data."
+        return None, "Error: "+str(e)
 
-# ─── TGT CSV ANALYSIS ────────────────────────────────────────────
 def analyze_tgt_csv(file):
     if file is None:
-        return None, "Please upload a TGT CSV file."
+        return None, "Please upload a TGT CSV file 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, "No numeric columns found."
 
         fig, axes = plt.subplots(2, 2, figsize=(14, 10))
         fig.patch.set_facecolor("#0d1b3e")
-        fig.suptitle("TGT Blood Analysis — SJSU CardioLab", color="white", fontsize=16, fontweight="bold", y=1.02)
+        fig.suptitle("TGT Blood Analysis — SJSU CardioLab", color="white", fontsize=16, fontweight="bold")
 
-        # Expected TGT columns: time, TAT, PF12, hemoglobin, platelets
         time_col = next((c for c in cols if "time" in c or "min" in c), None)
-        tat_col = next((c for c in cols if "tat" in c or "thrombin" in c), None)
-        pf_col = next((c for c in cols if "pf" in c or "pf1" in c or "prothrombin" in c), None)
-        hemo_col = next((c for c in cols if "hemo" in c or "hemoglobin" in c or "hgb" in c), None)
-        plt_col = next((c for c in cols if "platelet" in c or "plt" in c), None)
-
-        num_cols = df.select_dtypes(include=[np.number]).columns.tolist()
-        x_axis = df[time_col] if time_col else range(len(df))
-        x_label = time_col if time_col else "Sample Number"
-
-        normal_limits = {"tat":8, "pf":2.0, "hemo":20, "platelet":150}
-
-        def style_ax(ax, title):
+        tat_col = next((c for c in cols if "tat" in c or "thrombin" in c), num_cols[0] if num_cols else None)
+        pf_col = next((c for c in cols if "pf" in c or "prothrombin" in c), num_cols[1] if len(num_cols)>1 else None)
+        hemo_col = next((c for c in cols if "hemo" in c or "hgb" in c), num_cols[2] if len(num_cols)>2 else None)
+        plt_col = next((c for c in cols if "platelet" in c or "plt" in c), num_cols[3] if len(num_cols)>3 else None)
+        x = df[time_col] if time_col else range(len(df))
+        xl = time_col if time_col else "Sample"
+
+        def style_ax(ax, title, ylabel):
             ax.set_facecolor("#1a2744")
             ax.set_title(title, color="white", fontweight="bold")
+            ax.set_ylabel(ylabel, color="#a8b2d8")
+            ax.set_xlabel(xl, color="#a8b2d8")
             ax.tick_params(colors="#a8b2d8")
-            ax.set_xlabel(x_label, color="#a8b2d8")
             ax.grid(True, alpha=0.2, color="#2d4a8a")
-            ax.spines["bottom"].set_color("#2d4a8a")
-            ax.spines["left"].set_color("#2d4a8a")
-            ax.spines["top"].set_visible(False)
-            ax.spines["right"].set_visible(False)
-
-        # Plot 1 — TAT
-        ax1 = axes[0, 0]
-        col = tat_col if tat_col else (num_cols[0] if num_cols else None)
-        if col:
-            ax1.plot(x_axis, df[col], color="#e63946", linewidth=2.5, marker="o", markersize=6, label=col)
-            ax1.axhline(y=8, color="#ffd700", linestyle="--", linewidth=1.5, label="Normal limit (8 ng/mL)")
-            ax1.fill_between(x_axis, df[col], alpha=0.3, color="#e63946")
-            ax1.set_ylabel("TAT (ng/mL)", color="#a8b2d8")
+            for spine in ["top","right"]: ax.spines[spine].set_visible(False)
+            for spine in ["bottom","left"]: ax.spines[spine].set_color("#2d4a8a")
+
+        ax1 = axes[0,0]
+        if tat_col:
+            ax1.plot(x, df[tat_col], color="#e63946", linewidth=2.5, marker="o", markersize=6)
+            ax1.axhline(y=8, color="#ffd700", linestyle="--", linewidth=1.5, label="Normal (8 ng/mL)")
+            ax1.fill_between(x, df[tat_col], alpha=0.3, color="#e63946")
             ax1.legend(fontsize=8, labelcolor="white", facecolor="#1a2744")
-        style_ax(ax1, "Thrombin-Antithrombin (TAT)")
-
-        # Plot 2 — PF1.2
-        ax2 = axes[0, 1]
-        col2 = pf_col if pf_col else (num_cols[1] if len(num_cols)>1 else None)
-        if col2:
-            ax2.plot(x_axis, df[col2], color="#4361ee", linewidth=2.5, marker="s", markersize=6, label=col2)
-            ax2.axhline(y=2.0, color="#ffd700", linestyle="--", linewidth=1.5, label="Normal limit (2.0)")
-            ax2.fill_between(x_axis, df[col2], alpha=0.3, color="#4361ee")
-            ax2.set_ylabel("PF1.2 (nmol/L)", color="#a8b2d8")
+        style_ax(ax1, "TAT (Thrombin-Antithrombin)", "ng/mL")
+
+        ax2 = axes[0,1]
+        if pf_col:
+            ax2.plot(x, df[pf_col], color="#4361ee", linewidth=2.5, marker="s", markersize=6)
+            ax2.axhline(y=2.0, color="#ffd700", linestyle="--", linewidth=1.5, label="Normal (2.0)")
+            ax2.fill_between(x, df[pf_col], alpha=0.3, color="#4361ee")
             ax2.legend(fontsize=8, labelcolor="white", facecolor="#1a2744")
-        style_ax(ax2, "Prothrombin Fragment (PF1.2)")
-
-        # Plot 3 — Hemoglobin / Hemolysis
-        ax3 = axes[1, 0]
-        col3 = hemo_col if hemo_col else (num_cols[2] if len(num_cols)>2 else None)
-        if col3:
-            ax3.bar(range(len(df)), df[col3], color="#2ecc71", alpha=0.8, edgecolor="#1a2744", label=col3)
-            ax3.axhline(y=20, color="#ffd700", linestyle="--", linewidth=1.5, label="Normal limit (20 mg/L)")
-            ax3.set_ylabel("Free Hemoglobin (mg/L)", color="#a8b2d8")
+        style_ax(ax2, "PF1.2 (Prothrombin Fragment)", "nmol/L")
+
+        ax3 = axes[1,0]
+        if hemo_col:
+            ax3.bar(range(len(df)), df[hemo_col], color="#2ecc71", alpha=0.85, edgecolor="#0d1b3e")
+            ax3.axhline(y=20, color="#ffd700", linestyle="--", linewidth=1.5, label="Normal (20 mg/L)")
             ax3.legend(fontsize=8, labelcolor="white", facecolor="#1a2744")
-        style_ax(ax3, "Free Hemoglobin (Hemolysis)")
-
-        # Plot 4 — Platelets
-        ax4 = axes[1, 1]
-        col4 = plt_col if plt_col else (num_cols[3] if len(num_cols)>3 else None)
-        if col4:
-            ax4.plot(x_axis, df[col4], color="#e67e22", linewidth=2.5, marker="^", markersize=6, label=col4)
-            ax4.axhline(y=150, color="#ffd700", linestyle="--", linewidth=1.5, label="Normal minimum (150)")
-            ax4.fill_between(x_axis, df[col4], 150, where=df[col4]<150, alpha=0.3, color="#e63946", label="Below normal")
-            ax4.set_ylabel("Platelet Count (10³/μL)", color="#a8b2d8")
+        style_ax(ax3, "Free Hemoglobin (Hemolysis)", "mg/L")
+
+        ax4 = axes[1,1]
+        if plt_col:
+            ax4.plot(x, df[plt_col], color="#e67e22", linewidth=2.5, marker="^", markersize=6)
+            ax4.axhline(y=150, color="#ffd700", linestyle="--", linewidth=1.5, label="Normal min (150)")
+            ax4.fill_between(x, df[plt_col], 150, where=df[plt_col]<150, alpha=0.3, color="#e63946", label="Below normal")
             ax4.legend(fontsize=8, labelcolor="white", facecolor="#1a2744")
-        style_ax(ax4, "Platelet Count")
+        style_ax(ax4, "Platelet Count", "10³/μL")
 
         plt.tight_layout()
         buf = io.BytesIO()
@@ -255,181 +259,91 @@ def analyze_tgt_csv(file):
         img = Image.open(buf)
         plt.close()
 
-        # AI analysis
-        ai_summary = ""
+        ai_text = ""
         if GROQ_KEY:
             try:
                 client = Groq(api_key=GROQ_KEY)
-                stats = df.describe().to_string()
-                msgs = [{"role":"system","content":"You are a hematology expert for SJSU CardioLab TGT testing. Analyze the blood biomarker data and provide thrombogenicity assessment. Comment on TAT levels, PF1.2, hemolysis, platelet consumption. Give overall thrombogenic risk: LOW MODERATE or HIGH. Reference normal ranges: TAT below 8 ng/mL, PF1.2 below 2.0 nmol/L, Hemoglobin below 20 mg/L, Platelets above 150."}]
-                msgs.append({"role":"user","content":"Analyze TGT blood data from 27mm SJM Regent MHV tested in our MCL at 70bpm 5L/min:"+chr(10)+stats[:1000]})
-                resp = client.chat.completions.create(model="llama-3.3-70b-versatile",messages=msgs,max_tokens=400)
-                ai_summary = chr(10)+"━"*30+chr(10)+"AI THROMBOGENICITY ASSESSMENT:"+chr(10)+resp.choices[0].message.content
+                msgs = [{"role":"system","content":"You are a hematology expert for SJSU CardioLab. Analyze TGT blood biomarker data. Give thrombogenicity risk: LOW MODERATE or HIGH. Normal: TAT<8, PF1.2<2.0, Hemo<20, Platelets>150."}]
+                msgs.append({"role":"user","content":"TGT data from 27mm SJM Regent MHV:"+chr(10)+df.describe().to_string()[:800]})
+                resp = client.chat.completions.create(model="llama-3.3-70b-versatile",messages=msgs,max_tokens=350)
+                ai_text = chr(10)+"━"*25+chr(10)+"AI ASSESSMENT:"+chr(10)+resp.choices[0].message.content
             except: pass
 
-        result_text = ("TGT CSV ANALYSIS COMPLETE"+chr(10)+
-            "Rows: "+str(len(df))+" | Columns: "+str(len(df.columns))+chr(10)+
-            "Columns detected: "+", ".join(df.columns.tolist())+chr(10)+ai_summary)
-
-        return img, result_text
-
+        return img, "TGT CSV LOADED: "+str(len(df))+" rows"+chr(10)+"Columns: "+", ".join(df.columns.tolist())+ai_text
     except Exception as e:
-        return None, "Error reading CSV: "+str(e)+chr(10)+"Make sure your CSV has headers like: time, TAT, PF12, hemoglobin, platelets"
-
-# ─── OTHER FUNCTIONS ──────────────────────────────────────────────
-def get_pubmed(query, n=5):
-    try:
-        r = requests.get("https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi",
-            params={"db":"pubmed","term":query+" AND (mechanical heart valve OR microfluidic OR CKD OR thrombogenicity)","retmax":n,"retmode":"json","sort":"date"},timeout=10)
-        ids = r.json()["esearchresult"]["idlist"]
-        if not ids: return ""
-        return chr(10).join(["https://pubmed.ncbi.nlm.nih.gov/"+i for i in ids])
-    except: return ""
-
-def get_scholar(query, n=5):
-    try:
-        r = requests.get("https://api.semanticscholar.org/graph/v1/paper/search",
-            params={"query":query+" biomedical","limit":n,"fields":"title,year,url,citationCount"},timeout=10)
-        papers = r.json().get("data",[])
-        out = []
-        for p in papers:
-            url = p.get("url","")
-            if url: out.append(p.get("title","")[:80]+" ("+str(p.get("year",""))+") - "+str(p.get("citationCount",0))+" citations"+chr(10)+"   "+url)
-        return chr(10).join(out)
-    except: return ""
-
-def quick_search(query):
-    if not query.strip(): return "Please enter a research topic."
-    pubmed = get_pubmed(query, n=8)
-    scholar = get_scholar(query, n=5)
-    return "PUBMED RESULTS:"+chr(10)+pubmed+chr(10)+chr(10)+"SEMANTIC SCHOLAR:"+chr(10)+scholar
-
-def research_chat(message, history):
-    if not GROQ_KEY:
-        history.append({"role":"user","content":message})
-        history.append({"role":"assistant","content":"Error: Add GROQ_API_KEY to Space Settings Secrets."})
-        return "", history
-    try:
-        client = Groq(api_key=GROQ_KEY)
-        pubmed = get_pubmed(message, n=3)
-        msgs = [{"role":"system","content":"You are CardioLab AI. Expert in MHV MCL PIV TGT uPAD CKD FSI. Remember full conversation. Never invent URLs. "+KNOWHOW}]
-        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="llama-3.3-70b-versatile",messages=msgs,max_tokens=700)
-        answer = resp.choices[0].message.content
-        if pubmed: answer += chr(10)+chr(10)+"PUBMED LINKS:"+chr(10)+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 your question 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")
-        text = tx.text
-        msgs = [{"role":"system","content":"You are CardioLab AI. "+KNOWHOW}]
-        for item in history:
-            if isinstance(item, dict): msgs.append({"role":item["role"],"content":item["content"]})
-        msgs.append({"role":"user","content":text})
-        resp = client.chat.completions.create(model="llama-3.3-70b-versatile",messages=msgs,max_tokens=500)
-        history.append({"role":"user","content":"[Voice] "+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
+        return None, "Error: "+str(e)
 
 def analyze_upad_photo(image):
-    if image is None:
-        return None, "Please upload a uPAD photo first."
+    if image is None: return None, "Upload a uPAD photo first."
     try:
         img = Image.fromarray(image) if not isinstance(image, Image.Image) else image
-        img_array = np.array(img)
-        h, w = img_array.shape[:2]
-        y1,y2 = int(h*0.35),int(h*0.65)
-        x1,x2 = int(w*0.35),int(w*0.65)
-        zone = img_array[y1:y2, x1:x2]
-        R = float(np.mean(zone[:,:,0]))
-        G = float(np.mean(zone[:,:,1]))
-        B = float(np.mean(zone[:,:,2]))
-        orange_score = R - B
-        creatinine = max(0, round(0.018 * orange_score - 0.3, 2))
-        if creatinine < 1.2: stage,action = "Normal","No CKD. Monitor annually."
-        elif creatinine < 1.5: stage,action = "Borderline","Repeat in 3 months. Consult physician."
-        elif creatinine < 3.0: stage,action = "Stage 2 CKD","Consult nephrologist. Confirm with Heska HT5."
-        elif creatinine < 6.0: stage,action = "Stage 3-4 CKD","Advanced CKD. Immediate medical consultation."
-        else: stage,action = "Stage 5 CKD","Kidney failure range. Emergency care needed."
+        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,G,B = float(np.mean(zone[:,:,0])),float(np.mean(zone[:,:,1])),float(np.mean(zone[:,:,2]))
+        creatinine = max(0, round(0.018*(R-B)-0.3, 2))
+        if creatinine < 1.2: stage,action = "Normal","Monitor annually."
+        elif creatinine < 1.5: stage,action = "Borderline","Repeat in 3 months."
+        elif creatinine < 3.0: stage,action = "Stage 2 CKD","Consult nephrologist."
+        elif creatinine < 6.0: stage,action = "Stage 3-4 CKD","Immediate consultation."
+        else: stage,action = "Stage 5 CKD","Emergency care needed."
         result_img = img.copy()
-        import PIL.ImageDraw as ImageDraw
-        draw = ImageDraw.Draw(result_img)
+        import PIL.ImageDraw as D
+        draw = D.Draw(result_img)
         draw.rectangle([x1,y1,x2,y2], outline=(0,255,0), width=3)
-        result = ("uPAD PHOTO ANALYSIS"+chr(10)+"━"*27+chr(10)+
-            "R: "+str(round(R,1))+" G: "+str(round(G,1))+" B: "+str(round(B,1))+chr(10)+
-            "Orange Score: "+str(round(orange_score,1))+chr(10)+"━"*27+chr(10)+
+        return result_img, ("uPAD ANALYSIS"+chr(10)+"━"*22+chr(10)+
+            "R:"+str(round(R,1))+" G:"+str(round(G,1))+" B:"+str(round(B,1))+chr(10)+
+            "Orange Score: "+str(round(R-B,1))+chr(10)+"━"*22+chr(10)+
             "CREATININE: "+str(creatinine)+" mg/dL"+chr(10)+
-            "CKD STAGE: "+stage+chr(10)+"━"*27+chr(10)+
-            "ACTION: "+action+chr(10)+"Confirm with: Heska Element HT5")
-        return result_img, result
-    except Exception as e:
-        return None, "Error: "+str(e)
-
-def piv_tool(velocity, shear, hr):
-    v = "HIGH - stenosis" if float(velocity)>2.0 else "NORMAL"
-    s = "HIGH - thrombosis" if float(shear)>10 else "ELEVATED" if float(shear)>5 else "NORMAL"
-    return "PIV: Velocity "+str(velocity)+" m/s - "+v+chr(10)+"Shear "+str(shear)+" Pa - "+s+chr(10)+"HR "+str(hr)+" bpm"
-
-def tgt_tool(tat,pf12,hemo,platelets,time):
-    risk=sum([float(tat)>15,float(pf12)>2.0,float(hemo)>50,float(platelets)<150])
-    r="HIGH RISK" if risk>=3 else "MODERATE" if risk>=2 else "LOW RISK"
-    return "TGT: TAT "+str(tat)+" PF1.2 "+str(pf12)+chr(10)+"Hemo "+str(hemo)+" Plt "+str(platelets)+chr(10)+"Time "+str(time)+" min"+chr(10)+"RESULT: "+r
+            "CKD STAGE: "+stage+chr(10)+"ACTION: "+action+chr(10)+
+            "Confirm: Heska Element HT5")
+    except Exception as e: return None, "Error: "+str(e)
 
 def generate_image(prompt):
     if not prompt.strip(): return None,"Enter description.","";
-    if not HF_TOKEN: return None,"Error: Add HF_TOKEN to Space secrets.","";
+    if not HF_TOKEN: return None,"Add HF_TOKEN to Space secrets.","";
     try:
-        enhanced=prompt
-        description=""
+        enhanced,desc = prompt,""
         if GROQ_KEY:
             try:
                 client=Groq(api_key=GROQ_KEY)
-                msgs=[{"role":"system","content":"Biomedical visualization expert. Format: DESCRIPTION: [desc] PROMPT: [prompt]"},{"role":"user","content":"Create image for: "+prompt}]
-                resp=client.chat.completions.create(model="llama-3.3-70b-versatile",messages=msgs,max_tokens=200)
+                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 for CardioLab: "+prompt}],max_tokens=200)
                 full=resp.choices[0].message.content
                 if "DESCRIPTION:" in full and "PROMPT:" in full:
-                    description=full.split("DESCRIPTION:")[1].split("PROMPT:")[0].strip()
+                    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"}
-        payload={"inputs":enhanced,"parameters":{"num_inference_steps":8}}
-        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"]:
+        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=payload,timeout=60)
-                if r.status_code==200:
-                    return Image.open(io.BytesIO(r.content)),"Generated!",description
+                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. Try again.",description
+        return None,"Models busy. Try again.",desc
     except Exception as e: return None,"Error: "+str(e),""
 
-# ─── UI ───────────────────────────────────────────────────────────
+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)+" - "+vr+chr(10)+"Shear: "+str(s)+" - "+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)+"Time:"+str(tm)+"min"+chr(10)+"RESULT: "+("HIGH RISK" if risk>=3 else "MODERATE" if risk>=2 else "LOW RISK")
+
 with gr.Blocks(title="CardioLab AI", css=CSS) as demo:
-    gr.HTML('''<div style="background:linear-gradient(135deg,#1a237e,#b71c1c);padding:25px;text-align:center;border-radius:12px 12px 0 0"><div style="font-size:2.8em;font-weight:900;color:#fff;letter-spacing:3px">CardioLab AI</div></div>''')
+    gr.HTML('''<div style="background:linear-gradient(135deg,#1a237e,#b71c1c);padding:20px;text-align:center;border-radius:12px 12px 0 0"><div style="font-size:2.5em;font-weight:900;color:#fff;letter-spacing:3px">CardioLab AI</div></div>''')
 
     with gr.Tabs():
-
         with gr.Tab("Chat"):
-            chatbot = gr.Chatbot(label="", height=450)
+            chatbot = gr.Chatbot(label="", height=420)
             with gr.Row():
-                msg_box = gr.Textbox(placeholder="Ask anything about CardioLab research...", label="", lines=2, scale=4)
-                with gr.Column(scale=1, min_width=100):
+                msg_box = gr.Textbox(placeholder="Ask about CardioLab research...", label="", lines=2, scale=4)
+                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], outputs=[msg_box, chatbot])
@@ -437,8 +351,7 @@ with gr.Blocks(title="CardioLab AI", css=CSS) as demo:
             clear_btn.click(lambda: ([], ""), outputs=[chatbot, msg_box])
 
         with gr.Tab("Voice"):
-            gr.Markdown("### Speak your question - Groq Whisper AI")
-            voice_chatbot = gr.Chatbot(label="", height=350)
+            voice_chatbot = gr.Chatbot(label="", height=320)
             audio_input = gr.Audio(sources=["microphone"], type="filepath", label="Record Question")
             with gr.Row():
                 voice_btn = gr.Button("Ask by Voice", variant="primary")
@@ -454,64 +367,49 @@ with gr.Blocks(title="CardioLab AI", css=CSS) as demo:
             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 Data"):
-            gr.Markdown("### Upload PIV CSV — AI generates charts and clinical interpretation")
-            gr.Markdown("**Expected columns:** time/x, velocity, shear_stress (any names work — AI detects automatically)")
+        with gr.Tab("PIV CSV"):
+            gr.Markdown("### Upload PIV CSV file — AI generates 4 charts + clinical analysis")
+            gr.Markdown("CSV columns: **time, velocity, shear_stress** (any column names work)")
             with gr.Row():
                 with gr.Column(scale=1):
-                    piv_file = gr.File(label="Upload PIV CSV File", file_types=[".csv"])
-                    piv_analyze_btn = gr.Button("Analyze PIV Data", variant="primary")
-                    gr.Markdown("**Sample CSV format:**")
-                    gr.Markdown("```\ntime,velocity,shear_stress\n0,0.5,2.1\n1,1.2,4.5\n2,1.8,7.2\n```")
+                    piv_file = gr.File(label="CLICK HERE TO UPLOAD PIV CSV", file_types=[".csv"])
+                    piv_btn = gr.Button("Analyze PIV Data", variant="primary")
+                    piv_result = gr.Textbox(label="AI Analysis", lines=10)
                 with gr.Column(scale=2):
-                    piv_chart = gr.Image(label="PIV Charts", type="pil", height=450)
-                    piv_ai_result = gr.Textbox(label="AI Clinical Analysis", lines=10)
-            piv_analyze_btn.click(analyze_piv_csv, inputs=piv_file, outputs=[piv_chart, piv_ai_result])
+                    piv_chart = gr.Image(label="PIV Charts", type="pil")
+            piv_btn.click(analyze_piv_csv, inputs=piv_file, outputs=[piv_chart, piv_result])
 
-        with gr.Tab("TGT Data"):
-            gr.Markdown("### Upload TGT CSV — AI generates blood biomarker charts and thrombogenicity assessment")
-            gr.Markdown("**Expected columns:** time, TAT, PF12, hemoglobin, platelets (any names work — AI detects automatically)")
+        with gr.Tab("TGT CSV"):
+            gr.Markdown("### Upload TGT CSV file — AI generates blood biomarker charts + thrombogenicity assessment")
+            gr.Markdown("CSV columns: **time, TAT, PF12, hemoglobin, platelets** (any column names work)")
             with gr.Row():
                 with gr.Column(scale=1):
-                    tgt_file = gr.File(label="Upload TGT CSV File", file_types=[".csv"])
-                    tgt_analyze_btn = gr.Button("Analyze TGT Data", variant="primary")
-                    gr.Markdown("**Sample CSV format:**")
-                    gr.Markdown("```\ntime,TAT,PF12,hemoglobin,platelets\n0,5.2,1.1,12,210\n20,9.8,1.8,18,195\n40,14.2,2.4,35,178\n60,18.6,3.1,62,145\n```")
+                    tgt_file = gr.File(label="CLICK HERE TO UPLOAD TGT CSV", file_types=[".csv"])
+                    tgt_btn = gr.Button("Analyze TGT Data", variant="primary")
+                    tgt_result = gr.Textbox(label="AI Assessment", lines=10)
                 with gr.Column(scale=2):
-                    tgt_chart = gr.Image(label="TGT Blood Analysis Charts", type="pil", height=450)
-                    tgt_ai_result = gr.Textbox(label="AI Thrombogenicity Assessment", lines=10)
-            tgt_analyze_btn.click(analyze_tgt_csv, inputs=tgt_file, outputs=[tgt_chart, tgt_ai_result])
+                    tgt_chart = gr.Image(label="TGT Blood Charts", type="pil")
+            tgt_btn.click(analyze_tgt_csv, inputs=tgt_file, outputs=[tgt_chart, tgt_result])
 
         with gr.Tab("uPAD Photo"):
             gr.Markdown("### Upload uPAD Photo — Instant CKD diagnosis from Jaffe reaction color")
-            with gr.Row():
-                with gr.Column(scale=1):
-                    photo_input = gr.Image(label="Upload uPAD Photo", type="numpy", height=300)
-                    analyze_btn = gr.Button("Analyze uPAD Photo", variant="primary")
-                with gr.Column(scale=1):
-                    photo_result_img = gr.Image(label="Analyzed Image", type="pil", height=300)
-                    photo_result_text = gr.Textbox(label="CKD Result", lines=12)
-            analyze_btn.click(analyze_upad_photo, inputs=photo_input, outputs=[photo_result_img, photo_result_text])
-
-        with gr.Tab("uPAD Manual"):
             with gr.Row():
                 with gr.Column():
-                    r=gr.Number(label="R value", value=210)
-                    g=gr.Number(label="G value", value=140)
-                    b=gr.Number(label="B value", value=80)
-                    out3=gr.Textbox(label="Result", lines=6)
-                    gr.Button("Analyze", variant="primary").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)
+                    photo_input = gr.Image(label="Upload uPAD Photo", type="numpy", height=280)
+                    analyze_btn = gr.Button("Analyze uPAD", variant="primary")
+                with gr.Column():
+                    photo_img = gr.Image(label="Detection Zone (green box)", type="pil", height=280)
+                    photo_text = gr.Textbox(label="CKD Result", lines=10)
+            analyze_btn.click(analyze_upad_photo, inputs=photo_input, outputs=[photo_img, photo_text])
 
         with gr.Tab("AI Image"):
             with gr.Row():
-                img_prompt = gr.Textbox(placeholder="e.g. bileaflet heart valve | uPAD microfluidic | Arduino TGT circuit", label="Describe image", lines=3, scale=4)
+                img_prompt = gr.Textbox(placeholder="e.g. bileaflet heart valve | uPAD device | Arduino TGT circuit", 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=2)
+                    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_output = gr.Image(label="Generated Image", type="pil", height=380)
             img_btn.click(generate_image, inputs=img_prompt, outputs=[img_output, img_status, img_desc])
 
         with gr.Tab("PIV Manual"):
@@ -520,18 +418,29 @@ with gr.Blocks(title="CardioLab AI", css=CSS) as demo:
                     v=gr.Number(label="Max Velocity m/s", value=1.8)
                     s=gr.Number(label="Wall Shear Stress Pa", value=6.5)
                     h=gr.Number(label="Heart Rate bpm", value=72)
-                    piv_out=gr.Textbox(label="Result", lines=5)
-                    gr.Button("Analyze PIV", variant="primary").click(piv_tool,inputs=[v,s,h],outputs=piv_out)
+                    piv_out=gr.Textbox(label="Result", lines=4)
+                    gr.Button("Analyze", 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 nmol/L", value=2.5)
-                    t3=gr.Number(label="Free Hemoglobin mg/L", value=60)
-                    t4=gr.Number(label="Platelet Count", value=140)
-                    t5=gr.Number(label="Time minutes", value=40)
-                    out2=gr.Textbox(label="Result", lines=8)
-                    gr.Button("Analyze TGT", variant="primary").click(tgt_tool,inputs=[t1,t2,t3,t4,t5],outputs=out2)
+                    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", variant="primary").click(tgt_manual,inputs=[t1,t2,t3,t4,t5],outputs=out2)
+
+        with gr.Tab("uPAD Manual"):
+            with gr.Row():
+                with gr.Column():
+                    r=gr.Number(label="R value", value=210)
+                    g=gr.Number(label="G value", value=140)
+                    b=gr.Number(label="B value", value=80)
+                    out3=gr.Textbox(label="Result", lines=4)
+                    gr.Button("Analyze", variant="primary").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 Stage 2+" ),
+                        inputs=[r,g,b], outputs=out3)
 
 demo.launch()