Upload app.py with huggingface_hub

app.py

@@ -95,9 +95,9 @@ def voice_chat(audio, history):
         history.append({"role":"assistant","content":"Voice error: "+str(e)})
         return history
 
-def analyze_piv_csv(file):
+def analyze_piv_csv(file, theme="White"):
     if file is None:
-        return None, "Please upload a PIV CSV file first."
+        return None, None, None, None, "Please upload a PIV CSV file first."
     try:
         df = pd.read_csv(file.name)
         cols = [c.lower().strip() for c in df.columns]
@@ -214,11 +214,103 @@ def analyze_piv_csv(file):
                 ai_text = chr(10)+"━"*25+chr(10)+"AI ANALYSIS:"+chr(10)+resp.choices[0].message.content
             except: pass
 
-        return img, "PIV CSV LOADED: "+str(len(df))+" rows, "+str(len(df.columns))+" columns"+chr(10)+"Columns: "+", ".join(df.columns.tolist())+ai_text
+        # Generate 4 separate charts
+        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
+
+        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)
+            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)
+            if risk:
+                stats += "RISK FLAGS:"+chr(10)
+                for r in risk: stats += "  ⚠ "+r+chr(10)
+                stats += chr(10)+"OVERALL: HIGH RISK"
+                border_color = "#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, "Error: "+str(e)
+        return None, None, None, None, "Error: "+str(e)
 
-def analyze_tgt_csv(file):
+def analyze_tgt_csv(file, theme="White"):
     if file is None:
         return None, "Please upload a TGT CSV file first."
     try:
@@ -299,9 +391,59 @@ def analyze_tgt_csv(file):
                 ai_text = chr(10)+"━"*25+chr(10)+"AI ASSESSMENT:"+chr(10)+resp.choices[0].message.content
             except: pass
 
-        return img, "TGT CSV LOADED: "+str(len(df))+" rows"+chr(10)+"Columns: "+", ".join(df.columns.tolist())+ai_text
+        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, "Error: "+str(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."
@@ -398,28 +540,38 @@ 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 4 charts + clinical analysis")
+            gr.Markdown("### Upload PIV CSV file — AI generates separate 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="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")
-            piv_btn.click(analyze_piv_csv, inputs=piv_file, outputs=[piv_chart, piv_result])
+                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_btn = gr.Button("Analyze PIV Data", variant="primary")
+            piv_result = gr.Textbox(label="AI Clinical Analysis", lines=6)
+            gr.Markdown("### Charts")
+            with gr.Row():
+                piv_chart1 = gr.Image(label="Velocity Profile", type="pil")
+                piv_chart2 = 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])
 
         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="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 Charts", type="pil")
-            tgt_btn.click(analyze_tgt_csv, inputs=tgt_file, outputs=[tgt_chart, tgt_result])
+                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_btn = gr.Button("Analyze TGT Data", variant="primary")
+            tgt_result = gr.Textbox(label="AI Thrombogenicity Assessment", lines=6)
+            gr.Markdown("### Charts")
+            with gr.Row():
+                tgt_chart1 = gr.Image(label="TAT Over Time", type="pil")
+                tgt_chart2 = 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])
 
         with gr.Tab("uPAD Photo"):
             gr.Markdown("### Upload uPAD Photo — Instant CKD diagnosis from Jaffe reaction color")