Upload app.py with huggingface_hub

app.py

@@ -106,67 +106,96 @@ def analyze_piv_csv(file):
         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")
-
-        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")
+        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 = 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)
+        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
-        ax1 = axes[0,0]
+        # Plot 1 - Velocity profile - large filled area chart
+        ax1 = axes[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]
+            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:
-            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")
-        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")
+            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")
-        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"))
+        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()