Upload app.py with huggingface_hub

app.py

@@ -1,37 +1,121 @@
-# CardioLab AI v25.1 - PIV CSV TGT CSV tabs
 import gradio as gr
-import os, requests, io
+import os, requests, io, json
 import numpy as np
 import pandas as pd
 import matplotlib
 matplotlib.use("Agg")
 import matplotlib.pyplot as plt
-import matplotlib.patches as mpatches
 from groq import Groq
 from PIL import Image
+from datetime import datetime
+from huggingface_hub import HfApi, hf_hub_download
+from huggingface_hub.utils import EntryNotFoundError
 
 GROQ_KEY = os.environ.get("GROQ_API_KEY", "")
 HF_TOKEN = os.environ.get("HF_TOKEN", "")
+HISTORY_REPO = "Saicharan21/cardiolab-chat-history"
 
 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.")
+    "MHV: 27mm SJM Regent bileaflet also trileaflet monoleaflet pediatric. "
+    "Equipment: Heska HT5 hematology analyzer time-resolved PIV Tygon tubing Arduino Uno.")
 
 CSS = """
 body, .gradio-container { background: #f0f4f8 !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 { 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; }
+.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; white-space: nowrap !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; }
 button.secondary { background: #edf2f7 !important; color: #4a5568 !important; border: 1px solid #cbd5e0 !important; border-radius: 8px !important; }
-textarea, input[type=number] { background: #f7fafc !important; color: #1a202c !important; border: 1px solid #cbd5e0 !important; border-radius: 8px !important; }
+textarea, input[type=number], input[type=text] { background: #f7fafc !important; color: #1a202c !important; border: 1px solid #cbd5e0 !important; border-radius: 8px !important; }
 .message.user { background: linear-gradient(135deg, #e63946, #c1121f) !important; color: white !important; }
 .message.bot { background: #ebf4ff !important; color: #1a202c !important; border: 1px solid #bee3f8 !important; }
 label span { color: #2b6cb0 !important; font-weight: 600 !important; font-size: 0.85em !important; text-transform: uppercase !important; }
 """
 
+# ── PERSISTENT HISTORY FUNCTIONS ──────────────────────────────────
+def get_history_api():
+    if not HF_TOKEN: return None
+    return HfApi(token=HF_TOKEN)
+
+def load_all_sessions():
+    if not HF_TOKEN: return {}
+    try:
+        api = get_history_api()
+        path = hf_hub_download(
+            repo_id=HISTORY_REPO,
+            filename="chat_history.json",
+            repo_type="dataset",
+            token=HF_TOKEN
+        )
+        with open(path, "r") as f:
+            return json.load(f)
+    except Exception:
+        return {}
+
+def save_all_sessions(sessions):
+    if not HF_TOKEN: return False
+    try:
+        api = get_history_api()
+        content = json.dumps(sessions, indent=2)
+        api.upload_file(
+            path_or_fileobj=content.encode(),
+            path_in_repo="chat_history.json",
+            repo_id=HISTORY_REPO,
+            repo_type="dataset",
+            token=HF_TOKEN,
+            commit_message="Update chat history"
+        )
+        return True
+    except Exception as e:
+        print("Save error:", e)
+        return False
+
+def get_session_list():
+    sessions = load_all_sessions()
+    if not sessions:
+        return ["No saved sessions yet"]
+    return list(sessions.keys())
+
+def load_session(session_name):
+    if not session_name or session_name == "No saved sessions yet":
+        return [], "No session loaded"
+    sessions = load_all_sessions()
+    if session_name in sessions:
+        history = sessions[session_name]["messages"]
+        return history, "Loaded: " + session_name + " (" + str(len(history)) + " messages)"
+    return [], "Session not found"
+
+def save_session(history, session_name):
+    if not history:
+        return "Nothing to save — chat is empty", gr.update()
+    if not session_name.strip():
+        session_name = "Session " + datetime.now().strftime("%Y-%m-%d %H:%M")
+    sessions = load_all_sessions()
+    sessions[session_name] = {
+        "messages": history,
+        "saved_at": datetime.now().isoformat(),
+        "message_count": len(history)
+    }
+    success = save_all_sessions(sessions)
+    if success:
+        return "Saved: " + session_name, gr.update(choices=get_session_list(), value=session_name)
+    return "Save failed — check HF_TOKEN in Space secrets", gr.update()
+
+def delete_session(session_name):
+    if not session_name or session_name == "No saved sessions yet":
+        return "No session selected", gr.update()
+    sessions = load_all_sessions()
+    if session_name in sessions:
+        del sessions[session_name]
+        save_all_sessions(sessions)
+        new_list = get_session_list()
+        return "Deleted: " + session_name, gr.update(choices=new_list, value=new_list[0] if new_list else None)
+    return "Session not found", gr.update()
+
+# ── CHAT FUNCTIONS ────────────────────────────────────────────────
 def get_pubmed(query, n=5):
     try:
         r = requests.get("https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi",
@@ -46,7 +130,7 @@ def quick_search(query):
     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)
+            params={"query":query+" biomedical","limit":5,"fields":"title,year,url"},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 = ""
@@ -55,11 +139,11 @@ def quick_search(query):
 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."})
+        history.append({"role":"assistant","content":"Error: Add GROQ_API_KEY to Space Settings Secrets."})
         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}]
+        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})
@@ -95,388 +179,182 @@ def voice_chat(audio, history):
         history.append({"role":"assistant","content":"Voice error: "+str(e)})
         return history
 
+# ── 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,G,B = float(np.mean(zone[:,:,0])),float(np.mean(zone[:,:,1])),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 needed."
+        result_img = img.copy()
+        import PIL.ImageDraw as D
+        draw = D.Draw(result_img)
+        draw.rectangle([x1,y1,x2,y2], outline=(0,255,0), width=3)
+        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(c)+" mg/dL"+chr(10)+"CKD STAGE: "+s+chr(10)+
+            "ACTION: "+a+chr(10)+"Confirm: Heska Element HT5")
+    except Exception as e: return None, "Error: "+str(e)
+
 def analyze_piv_csv(file, theme="White"):
-    if file is None:
-        return None, None, None, None, "Please upload a PIV CSV file first."
+    if file is None: return None,None,None,None,"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 = plt.figure(figsize=(16, 11))
-        fig.patch.set_facecolor("#0a1628")
-        fig.suptitle("PIV Data Analysis — SJSU CardioLab MCL", color="white", fontsize=18, fontweight="bold", y=0.98)
-
-        gs = fig.add_gridspec(2, 2, hspace=0.38, wspace=0.32, left=0.08, right=0.97, top=0.93, bottom=0.08)
-        axes = [fig.add_subplot(gs[0,0]), fig.add_subplot(gs[0,1]), fig.add_subplot(gs[1,0]), fig.add_subplot(gs[1,1])]
-
-        def style_ax(ax, title, xlabel, ylabel):
-            ax.set_facecolor("#132340")
-            ax.set_title(title, color="white", fontweight="bold", fontsize=13, pad=10)
-            ax.set_xlabel(xlabel, color="#7eb8f7", fontsize=11)
-            ax.set_ylabel(ylabel, color="#7eb8f7", fontsize=11)
-            ax.tick_params(colors="#a8b2d8", labelsize=10)
-            ax.grid(True, alpha=0.25, color="#2d4a8a", linestyle="--")
-            for spine in ["top","right"]: ax.spines[spine].set_visible(False)
-            for spine in ["bottom","left"]: ax.spines[spine].set_color("#2d4a8a")
-
-        x = np.arange(len(df))
-        time_col = next((c for c in cols if "time" in c or "frame" in c or "x" == c), None)
-        vel_col = next((c for c in cols if any(k in c for k in ["vel","speed","v_mag","magnitude"])), 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)
-        x_vals = df[time_col] if time_col else x
-        x_label = time_col.title() if time_col else "Sample Index"
-
-        # Plot 1 - Velocity profile - large filled area chart
-        ax1 = axes[0]
-        if vel_col:
-            v_data = df[vel_col].values
-            ax1.fill_between(x_vals, v_data, alpha=0.25, color="#e63946")
-            ax1.plot(x_vals, v_data, color="#e63946", linewidth=3, marker="o", markersize=5, label="Velocity")
-            ax1.axhline(y=2.0, color="#ffd700", linestyle="--", linewidth=2, label="Risk threshold: 2.0 m/s")
-            max_v = v_data.max()
-            max_i = v_data.argmax()
-            ax1.annotate(f"Peak: {max_v:.2f} m/s", xy=(x_vals.iloc[max_i] if time_col else max_i, max_v),
-                xytext=(10, 10), textcoords="offset points", color="#ffd700", fontsize=10, fontweight="bold",
-                arrowprops=dict(arrowstyle="->", color="#ffd700", lw=1.5))
-            ax1.legend(fontsize=10, labelcolor="white", facecolor="#132340", framealpha=0.8)
-            ax1.set_ylim(bottom=0)
-        style_ax(ax1, "Velocity Profile", x_label, "Velocity (m/s)")
-
-        # Plot 2 - Shear stress with risk zones
-        ax2 = axes[1]
-        if shear_col:
-            s_data = df[shear_col].values
-            x_plot = x_vals.values if time_col else x
-            ax2.fill_between(x_plot, s_data, alpha=0.25, color="#4361ee")
-            ax2.fill_between(x_plot, s_data, 10, where=s_data>10, alpha=0.4, color="#e63946", label="High risk zone")
-            ax2.fill_between(x_plot, s_data, 5, where=(s_data>5)&(s_data<=10), alpha=0.3, color="#ffd700", label="Caution zone")
-            ax2.plot(x_plot, s_data, color="#4361ee", linewidth=3, marker="s", markersize=5)
-            ax2.axhline(y=5, color="#ffd700", linestyle="--", linewidth=2, label="Caution: 5 Pa")
-            ax2.axhline(y=10, color="#e63946", linestyle="--", linewidth=2, label="High risk: 10 Pa")
-            ax2.legend(fontsize=9, labelcolor="white", facecolor="#132340", framealpha=0.8)
-            ax2.set_ylim(bottom=0)
-        style_ax(ax2, "Wall Shear Stress", x_label, "Shear Stress (Pa)")
-
-        # Plot 3 - Velocity vs Shear scatter plot
-        ax3 = axes[2]
-        if vel_col and shear_col:
-            sc = ax3.scatter(df[vel_col], df[shear_col], c=x, cmap="RdYlGn_r", s=80, edgecolors="white", linewidth=0.5, zorder=5)
-            plt.colorbar(sc, ax=ax3, label="Time progression").ax.yaxis.label.set_color("white")
-            ax3.axvline(x=2.0, color="#ffd700", linestyle="--", linewidth=2, label="Vel. risk: 2.0")
-            ax3.axhline(y=10, color="#e63946", linestyle="--", linewidth=2, label="Shear risk: 10")
-            ax3.legend(fontsize=9, labelcolor="white", facecolor="#132340", framealpha=0.8)
-        style_ax(ax3, "Velocity vs Shear Stress", "Velocity (m/s)", "Shear Stress (Pa)")
-
-        # Plot 4 - Clinical summary dashboard
-        ax4 = axes[3]
-        ax4.set_facecolor("#0d1b3e")
-        ax4.axis("off")
-        risk = []
-        summary = "CLINICAL SUMMARY"+chr(10)+"━"*24+chr(10)+chr(10)
-        for col in num_cols[:4]:
-            mn = round(df[col].mean(),3)
-            mx = round(df[col].max(),3)
-            mn_v = round(df[col].min(),3)
-            summary += f"{col[:14]:14s}"+chr(10)+f"  Mean: {mn:8.3f}"+chr(10)+f"  Max:  {mx:8.3f}"+chr(10)+f"  Min:  {mn_v:8.3f}"+chr(10)+chr(10)
-            if "vel" in col and mx > 2.0: risk.append("HIGH VELOCITY (>2.0 m/s)")
-            if "shear" in col and mx > 10: risk.append("HIGH SHEAR (>10 Pa)")
-        summary += "━"*24+chr(10)
-        if risk:
-            summary += "RISK FLAGS DETECTED:"+chr(10)
-            for r in risk: summary += "  ⚠ "+r+chr(10)
-            overall = "HIGH RISK — Clinical review needed"
-        else:
-            summary += "STATUS: All values normal"+chr(10)
-            overall = "LOW RISK — Continue monitoring"
-        summary += chr(10)+"OVERALL: "+overall
-        ax4.text(0.05, 0.97, summary, transform=ax4.transAxes, color="white", fontsize=10,
-            va="top", fontfamily="monospace",
-            bbox=dict(boxstyle="round,pad=0.8", facecolor="#132340", edgecolor="#e63946" if risk else "#2ecc71", linewidth=2))
-
-        plt.tight_layout()
-        buf = io.BytesIO()
-        plt.savefig(buf, format="png", facecolor=fig.get_facecolor(), bbox_inches="tight", dpi=120)
-        buf.seek(0)
-        img = Image.open(buf)
-        plt.close()
-
-        ai_text = ""
-        if GROQ_KEY:
-            try:
-                client = Groq(api_key=GROQ_KEY)
-                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
-
-        # Generate 4 separate charts
+        if not num_cols: return None,None,None,None,"No numeric columns found."
         bg = "#ffffff" if theme=="White" else "#0a1628"
         fg = "#1a202c" if theme=="White" else "white"
-        grid_color = "#e2e8f0" if theme=="White" else "#2d4a8a"
-        ax_color = "#4a5568" if theme=="White" else "#a8b2d8"
-        plot_bg = "#f7fafc" if theme=="White" else "#132340"
-
-        def make_single_chart(plot_fn, title):
-            fig2, ax = plt.subplots(figsize=(8, 5))
-            fig2.patch.set_facecolor(bg)
-            ax.set_facecolor(plot_bg)
-            plot_fn(ax)
-            ax.set_title(title, color=fg, fontweight="bold", fontsize=14, pad=10)
-            ax.tick_params(colors=ax_color, labelsize=11)
-            ax.grid(True, alpha=0.3, color=grid_color, linestyle="--")
-            for spine in ["top","right"]: ax.spines[spine].set_visible(False)
-            for spine in ["bottom","left"]: ax.spines[spine].set_color(grid_color)
-            plt.tight_layout()
-            buf2 = io.BytesIO()
-            plt.savefig(buf2, format="png", facecolor=bg, bbox_inches="tight", dpi=130)
-            buf2.seek(0)
-            result = Image.open(buf2).copy()
-            plt.close()
-            return result
-
+        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))
-        vel_col2 = 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)
-        shear_col2 = 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)
-        time_col2 = next((c for c in cols if "time" in c or "frame" in c), None)
-        x_vals2 = df[time_col2] if time_col2 else x
-
-        def plot_velocity(ax):
-            if vel_col2:
-                ax.plot(x_vals2, df[vel_col2], color="#e63946", linewidth=2.5, marker="o", markersize=5)
-                ax.fill_between(x_vals2, df[vel_col2], alpha=0.2, color="#e63946")
-                ax.axhline(y=2.0, color="#f59e0b", linestyle="--", linewidth=2, label="Risk: 2.0 m/s")
-                ax.set_ylabel("Velocity (m/s)", color=ax_color, fontsize=12)
-                ax.set_xlabel(time_col2 or "Sample", color=ax_color, fontsize=12)
-                ax.legend(fontsize=10, labelcolor=fg, facecolor=plot_bg)
-
-        def plot_shear(ax):
-            if shear_col2:
-                xp = x_vals2.values if time_col2 else x
-                ax.plot(xp, df[shear_col2], color="#4361ee", linewidth=2.5, marker="s", markersize=5)
-                ax.fill_between(xp, df[shear_col2], alpha=0.2, color="#4361ee")
-                ax.axhline(y=5, color="#f59e0b", linestyle="--", linewidth=2, label="Caution: 5 Pa")
-                ax.axhline(y=10, color="#e63946", linestyle="--", linewidth=2, label="High risk: 10 Pa")
-                ax.set_ylabel("Shear Stress (Pa)", color=ax_color, fontsize=12)
-                ax.set_xlabel(time_col2 or "Sample", color=ax_color, fontsize=12)
-                ax.legend(fontsize=10, labelcolor=fg, facecolor=plot_bg)
-
-        def plot_scatter(ax):
-            if vel_col2 and shear_col2:
-                sc = ax.scatter(df[vel_col2], df[shear_col2], c=x, cmap="RdYlGn_r", s=100, edgecolors=fg, linewidth=0.5, zorder=5)
-                cb = plt.colorbar(sc, ax=ax, label="Time")
-                cb.ax.yaxis.label.set_color(fg)
-                cb.ax.tick_params(colors=ax_color)
-                ax.axvline(x=2.0, color="#f59e0b", linestyle="--", linewidth=2, label="Vel risk: 2.0")
-                ax.axhline(y=10, color="#e63946", linestyle="--", linewidth=2, label="Shear risk: 10")
-                ax.set_xlabel("Velocity (m/s)", color=ax_color, fontsize=12)
-                ax.set_ylabel("Shear Stress (Pa)", color=ax_color, fontsize=12)
-                ax.legend(fontsize=10, labelcolor=fg, facecolor=plot_bg)
-
-        def plot_summary(ax):
-            ax.axis("off")
-            risk = []
-            stats = "CLINICAL SUMMARY"+chr(10)+"━"*22+chr(10)+chr(10)
+        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)
+        sc = 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 mk(fn, title):
+            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()
+            buf2=io.BytesIO(); plt.savefig(buf2,format="png",facecolor=bg,bbox_inches="tight",dpi=130); buf2.seek(0)
+            res=Image.open(buf2).copy(); plt.close(); return res
+        def pv(ax):
+            if vc:
+                ax.plot(xv,df[vc],color="#e63946",linewidth=2.5,marker="o",markersize=5)
+                ax.fill_between(xv,df[vc],alpha=0.2,color="#e63946")
+                ax.axhline(y=2.0,color="#f59e0b",linestyle="--",linewidth=2,label="Risk: 2.0 m/s")
+                ax.set_ylabel("Velocity (m/s)",color=ac,fontsize=11)
+                ax.set_xlabel(tc or "Sample",color=ac,fontsize=11)
+                ax.legend(fontsize=9,labelcolor=fg,facecolor=pb)
+        def ps(ax):
+            if sc:
+                xp = xv.values if tc else x
+                ax.plot(xp,df[sc],color="#4361ee",linewidth=2.5,marker="s",markersize=5)
+                ax.fill_between(xp,df[sc],alpha=0.2,color="#4361ee")
+                ax.axhline(y=5,color="#f59e0b",linestyle="--",linewidth=2,label="Caution: 5 Pa")
+                ax.axhline(y=10,color="#e63946",linestyle="--",linewidth=2,label="High risk: 10 Pa")
+                ax.set_ylabel("Shear Stress (Pa)",color=ac,fontsize=11)
+                ax.set_xlabel(tc or "Sample",color=ac,fontsize=11)
+                ax.legend(fontsize=9,labelcolor=fg,facecolor=pb)
+        def psc(ax):
+            if vc and sc:
+                s2 = ax.scatter(df[vc],df[sc],c=x,cmap="RdYlGn_r",s=90,edgecolors=fg,linewidth=0.5,zorder=5)
+                cb=plt.colorbar(s2,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,label="Vel risk")
+                ax.axhline(y=10,color="#e63946",linestyle="--",linewidth=2,label="Shear risk")
+                ax.set_xlabel("Velocity (m/s)",color=ac,fontsize=11)
+                ax.set_ylabel("Shear Stress (Pa)",color=ac,fontsize=11)
+                ax.legend(fontsize=9,labelcolor=fg,facecolor=pb)
+        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)
-                mn_v = round(df[col].min(),3)
-                stats += col[:14]+":"+chr(10)+"  Mean: "+str(mn)+chr(10)+"  Max:  "+str(mx)+chr(10)+"  Min:  "+str(mn_v)+chr(10)+chr(10)
-                if "vel" in col and mx > 2.0: risk.append("HIGH VELOCITY (>2.0 m/s)")
-                if "shear" in col and mx > 10: risk.append("HIGH SHEAR (>10 Pa)")
-            stats += "━"*22+chr(10)
+                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 (>2.0 m/s)")
+                if "shear" in col and mx>10: risk.append("HIGH SHEAR (>10 Pa)")
+            st+="━"*20+chr(10)
             if risk:
-                stats += "RISK FLAGS:"+chr(10)
-                for r in risk: stats += "  ⚠ "+r+chr(10)
-                stats += chr(10)+"OVERALL: HIGH RISK"
-                border_color = "#e63946"
+                st+="RISK FLAGS:"+chr(10)+"".join(["  ⚠ "+r+chr(10) for r in risk])
+                st+="OVERALL: HIGH RISK"; bc="#e63946"
             else:
-                stats += "STATUS: All values normal"+chr(10)+"OVERALL: LOW RISK"
-                border_color = "#2ecc71"
-            ax.text(0.05, 0.97, stats, transform=ax.transAxes, color=fg, fontsize=11,
-                va="top", fontfamily="monospace",
-                bbox=dict(boxstyle="round,pad=0.8", facecolor=plot_bg, edgecolor=border_color, linewidth=2.5))
-
-        img1 = make_single_chart(plot_velocity, "Velocity Profile")
-        img2 = make_single_chart(plot_shear, "Wall Shear Stress")
-        img3 = make_single_chart(plot_scatter, "Velocity vs Shear Stress")
-        img4 = make_single_chart(plot_summary, "Clinical Summary")
-
-        return img1, img2, img3, img4, "PIV CSV LOADED: "+str(len(df))+" rows | Columns: "+", ".join(df.columns.tolist())+ai_text
-    except Exception as e:
-        return None, None, None, None, "Error: "+str(e)
+                st+="OVERALL: LOW RISK"; bc="#2ecc71"
+            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(pv,"Velocity Profile"); i2=mk(ps,"Wall Shear Stress")
+        i3=mk(psc,"Velocity vs Shear"); i4=mk(psum,"Clinical Summary")
+        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. Analyze PIV stats give clinical interpretation."},
+                    {"role":"user","content":"PIV data from 27mm SJM Regent MHV 70bpm 5L/min:"+chr(10)+df.describe().to_string()[:600]}],max_tokens=300)
+                ai=chr(10)+"━"*20+chr(10)+"AI:"+chr(10)+resp.choices[0].message.content
+            except: pass
+        return i1,i2,i3,i4,"PIV LOADED: "+str(len(df))+" rows | "+", ".join(df.columns.tolist())+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, "Please upload a TGT CSV file first."
+    if file is None: return None,None,None,None,"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")
-
-        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), 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.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")
-
-        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, "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, "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)", "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", "10³/μL")
-
-        plt.tight_layout()
-        buf = io.BytesIO()
-        plt.savefig(buf, format="png", facecolor=fig.get_facecolor(), bbox_inches="tight", dpi=120)
-        buf.seek(0)
-        img = Image.open(buf)
-        plt.close()
-
-        ai_text = ""
+        bg="#ffffff" 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 or "hgb" 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)
+        xv=df[tc] if tc else range(len(df))
+        def mk(dc,color,yl,lim,ll,title,bar=False):
+            fig2,ax=plt.subplots(figsize=(8,5))
+            fig2.patch.set_facecolor(bg); ax.set_facecolor(pb)
+            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=0.2,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,fontsize=11); ax.set_xlabel(tc or "Sample",color=ac,fontsize=11)
+                mv=round(float(np.max(yp)),2); st="HIGH" if mv>lim else "NORMAL"
+                ax.set_title(title+chr(10)+"Max: "+str(mv)+"  Status: "+st,color=fg,fontweight="bold",fontsize=12)
+            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()
+            buf2=io.BytesIO(); plt.savefig(buf2,format="png",facecolor=bg,bbox_inches="tight",dpi=130); buf2.seek(0)
+            res=Image.open(buf2).copy(); plt.close(); return res
+        i1=mk(tatc,"#e63946","TAT (ng/mL)",8,"Normal: 8 ng/mL","Thrombin-Antithrombin TAT")
+        i2=mk(pfc,"#4361ee","PF1.2 (nmol/L)",2.0,"Normal: 2.0","Prothrombin Fragment PF1.2")
+        i3=mk(hc,"#2ecc71","Free Hemoglobin (mg/L)",20,"Normal: 20 mg/L","Free Hemoglobin Hemolysis",bar=True)
+        i4=mk(plc,"#e67e22","Platelet Count (10³/μL)",150,"Normal min: 150","Platelet Count")
+        ai=""
         if GROQ_KEY:
             try:
-                client = Groq(api_key=GROQ_KEY)
-                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
+                client=Groq(api_key=GROQ_KEY)
+                resp=client.chat.completions.create(model="llama-3.3-70b-versatile",
+                    messages=[{"role":"system","content":"Hematology expert SJSU CardioLab. Analyze TGT data give thrombogenicity risk LOW MODERATE or HIGH. Normal: TAT<8, PF1.2<2.0, Hemo<20, Plt>150."},
+                    {"role":"user","content":"TGT from 27mm SJM Regent MHV:"+chr(10)+df.describe().to_string()[:600]}],max_tokens=300)
+                ai=chr(10)+"━"*20+chr(10)+"AI:"+chr(10)+resp.choices[0].message.content
             except: pass
-
-        bg = "#ffffff" if theme=="White" else "#0a1628"
-        fg = "#1a202c" if theme=="White" else "white"
-        grid_color = "#e2e8f0" if theme=="White" else "#2d4a8a"
-        ax_color = "#4a5568" if theme=="White" else "#a8b2d8"
-        plot_bg = "#f7fafc" if theme=="White" else "#132340"
-
-        def make_tgt_chart(data_col, color, ylabel, limit, limit_label, title, chart_type="line"):
-            fig2, ax = plt.subplots(figsize=(8, 5))
-            fig2.patch.set_facecolor(bg)
-            ax.set_facecolor(plot_bg)
-            if data_col and data_col in df.columns:
-                xp = df[time_col].values if time_col else range(len(df))
-                yp = df[data_col].values
-                if chart_type == "bar":
-                    bars = ax.bar(range(len(yp)), yp, color=color, alpha=0.85, edgecolor=bg, width=0.6)
-                    for bar, val in zip(bars, yp):
-                        ax.text(bar.get_x()+bar.get_width()/2, bar.get_height()+0.5, str(round(val,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=0.2, color=color)
-                    for i,(xi,yi) in enumerate(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=limit, color="#f59e0b", linestyle="--", linewidth=2.5, label=limit_label)
-                ax.legend(fontsize=11, labelcolor=fg, facecolor=plot_bg)
-                ax.set_ylabel(ylabel, color=ax_color, fontsize=12)
-                ax.set_xlabel(time_col or "Sample", color=ax_color, fontsize=12)
-                mean_val = round(float(np.mean(yp)),2)
-                max_val = round(float(np.max(yp)),2)
-                status = "HIGH" if max_val > limit else "NORMAL"
-                ax.set_title(title+chr(10)+"Mean: "+str(mean_val)+"  Max: "+str(max_val)+"  Status: "+status,
-                    color=fg, fontweight="bold", fontsize=12, pad=8)
-            ax.tick_params(colors=ax_color, labelsize=11)
-            ax.grid(True, alpha=0.3, color=grid_color, linestyle="--")
-            for spine in ["top","right"]: ax.spines[spine].set_visible(False)
-            for spine in ["bottom","left"]: ax.spines[spine].set_color(grid_color)
-            plt.tight_layout()
-            buf2 = io.BytesIO()
-            plt.savefig(buf2, format="png", facecolor=bg, bbox_inches="tight", dpi=130)
-            buf2.seek(0)
-            result = Image.open(buf2).copy()
-            plt.close()
-            return result
-
-        img1 = make_tgt_chart(tat_col, "#e63946", "TAT (ng/mL)", 8, "Normal limit: 8 ng/mL", "Thrombin-Antithrombin (TAT)")
-        img2 = make_tgt_chart(pf_col, "#4361ee", "PF1.2 (nmol/L)", 2.0, "Normal limit: 2.0", "Prothrombin Fragment PF1.2")
-        img3 = make_tgt_chart(hemo_col, "#2ecc71", "Free Hemoglobin (mg/L)", 20, "Normal limit: 20 mg/L", "Free Hemoglobin - Hemolysis", "bar")
-        img4 = make_tgt_chart(plt_col, "#e67e22", "Platelet Count (10³/μL)", 150, "Normal minimum: 150", "Platelet Count")
-
-        return img1, img2, img3, img4, "TGT CSV LOADED: "+str(len(df))+" rows | Columns: "+", ".join(df.columns.tolist())+ai_text
-    except Exception as e:
-        return None, None, None, None, "Error: "+str(e)
-
-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,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 D
-        draw = D.Draw(result_img)
-        draw.rectangle([x1,y1,x2,y2], outline=(0,255,0), width=3)
-        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)+"ACTION: "+action+chr(10)+
-            "Confirm: Heska Element HT5")
-    except Exception as e: return None, "Error: "+str(e)
+        return i1,i2,i3,i4,"TGT LOADED: "+str(len(df))+" rows | "+", ".join(df.columns.tolist())+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 to Space secrets.","";
     try:
-        enhanced,desc = prompt,""
+        enhanced,desc=prompt,""
         if GROQ_KEY:
             try:
                 client=Groq(api_key=GROQ_KEY)
@@ -507,20 +385,42 @@ 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")
 
+# ── UI ─────────────────────────────────────────────────────────────
 with gr.Blocks(title="CardioLab AI", css=CSS) as demo:
     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=420)
+            gr.Markdown("### Chat with memory — saves conversations like ChatGPT")
             with gr.Row():
-                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")
+                with gr.Column(scale=3):
+                    chatbot = gr.Chatbot(label="", height=420)
+                    with gr.Row():
+                        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")
+                with gr.Column(scale=1, min_width=200):
+                    gr.Markdown("### Saved Sessions")
+                    session_dropdown = gr.Dropdown(
+                        choices=get_session_list(),
+                        label="Load a saved session",
+                        interactive=True
+                    )
+                    load_btn = gr.Button("Load Session", variant="primary")
+                    session_status = gr.Textbox(label="Status", lines=1, interactive=False)
+                    gr.Markdown("### Save Current Chat")
+                    session_name_box = gr.Textbox(label="Session name", placeholder="e.g. TGT Research May 2026")
+                    save_btn = gr.Button("Save Chat", variant="primary")
+                    delete_btn = gr.Button("Delete Session", variant="secondary")
+
             send_btn.click(research_chat, inputs=[msg_box, chatbot], outputs=[msg_box, chatbot])
             msg_box.submit(research_chat, inputs=[msg_box, chatbot], outputs=[msg_box, chatbot])
             clear_btn.click(lambda: ([], ""), outputs=[chatbot, msg_box])
+            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=320)
@@ -540,89 +440,85 @@ with gr.Blocks(title="CardioLab AI", css=CSS) as demo:
             search_input.submit(quick_search, inputs=search_input, outputs=search_output)
 
         with gr.Tab("PIV CSV"):
-            gr.Markdown("### Upload PIV CSV file — AI generates separate charts + clinical analysis")
-            gr.Markdown("CSV columns: **time, velocity, shear_stress** (any column names work)")
+            gr.Markdown("### Upload PIV CSV — 4 separate charts + AI analysis")
             with gr.Row():
-                piv_file = gr.File(label="CLICK HERE TO UPLOAD PIV CSV", file_types=[".csv"], scale=3)
-                piv_theme = gr.Radio(["Dark", "White"], value="White", label="Chart Theme", scale=1)
+                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 Clinical Analysis", lines=6)
-            gr.Markdown("### Charts")
+            piv_result = gr.Textbox(label="AI Analysis", lines=5)
             with gr.Row():
-                piv_chart1 = gr.Image(label="Velocity Profile", type="pil")
-                piv_chart2 = gr.Image(label="Shear Stress", type="pil")
+                piv_c1 = gr.Image(label="Velocity Profile", type="pil")
+                piv_c2 = gr.Image(label="Shear Stress", type="pil")
             with gr.Row():
-                piv_chart3 = gr.Image(label="Velocity vs Shear", type="pil")
-                piv_chart4 = gr.Image(label="Clinical Summary", type="pil")
-            piv_btn.click(analyze_piv_csv, inputs=[piv_file, piv_theme], outputs=[piv_chart1, piv_chart2, piv_chart3, piv_chart4, piv_result])
+                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"):
-            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)")
+            gr.Markdown("### Upload TGT CSV — blood biomarker charts + thrombogenicity assessment")
             with gr.Row():
-                tgt_file = gr.File(label="CLICK HERE TO UPLOAD TGT CSV", file_types=[".csv"], scale=3)
-                tgt_theme = gr.Radio(["Dark", "White"], value="White", label="Chart Theme", scale=1)
+                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 Thrombogenicity Assessment", lines=6)
-            gr.Markdown("### Charts")
+            tgt_result = gr.Textbox(label="AI Assessment", lines=5)
             with gr.Row():
-                tgt_chart1 = gr.Image(label="TAT Over Time", type="pil")
-                tgt_chart2 = gr.Image(label="PF1.2 Over Time", type="pil")
+                tgt_c1 = gr.Image(label="TAT Over Time", type="pil")
+                tgt_c2 = gr.Image(label="PF1.2 Over Time", type="pil")
             with gr.Row():
-                tgt_chart3 = gr.Image(label="Free Hemoglobin", type="pil")
-                tgt_chart4 = gr.Image(label="Platelet Count", type="pil")
-            tgt_btn.click(analyze_tgt_csv, inputs=[tgt_file, tgt_theme], outputs=[tgt_chart1, tgt_chart2, tgt_chart3, tgt_chart4, tgt_result])
+                tgt_c3 = gr.Image(label="Free Hemoglobin", type="pil")
+                tgt_c4 = gr.Image(label="Platelet Count", 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 Photo"):
-            gr.Markdown("### Upload uPAD Photo — Instant CKD diagnosis from Jaffe reaction color")
+            gr.Markdown("### Upload uPAD Photo — Instant CKD diagnosis")
             with gr.Row():
                 with gr.Column():
                     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_img = gr.Image(label="Detection Zone", 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 device | Arduino TGT circuit", label="Describe image", lines=2, scale=4)
+                img_prompt = gr.Textbox(placeholder="e.g. bileaflet heart valve | uPAD device | Arduino TGT", 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=380)
-            img_btn.click(generate_image, inputs=img_prompt, outputs=[img_output, img_status, img_desc])
+            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 Stress Pa", value=6.5)
-                    h=gr.Number(label="Heart Rate bpm", value=72)
-                    piv_out=gr.Textbox(label="Result", lines=4)
-                    gr.Button("Analyze", variant="primary").click(piv_manual,inputs=[v,s,h],outputs=piv_out)
+                    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=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", variant="primary").click(tgt_manual,inputs=[t1,t2,t3,t4,t5],outputs=out2)
+                    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("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)
+                    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()