add smoothing

app.py

@@ -210,6 +210,15 @@ def model_predict(feat: np.ndarray, cond: np.ndarray) -> float:
     return float(model.predict_proba(x)[0, 1])
 
 
+# ── Smoothing (same as LLPSXG.py's moving_average) ────────────────────────────
+def moving_average(y: np.ndarray, window_size: int) -> np.ndarray:
+    if window_size % 2 == 0:
+        raise ValueError("Window size should be odd to ensure symmetry.")
+    window = np.ones(int(window_size)) / float(window_size)
+    y_padded = np.pad(y, (window_size // 2, window_size // 2), mode="edge")
+    return np.convolve(y_padded, window, "valid")
+
+
 # ── Matplotlib helpers ────────────────────────────────────────────────────────
 def prob_gauge_figure(prob: float) -> plt.Figure:
     """Horizontal probability bar gauge."""
@@ -236,21 +245,34 @@ def prob_gauge_figure(prob: float) -> plt.Figure:
 
 
 def screening_figure(xvals: np.ndarray, probs: np.ndarray,
-                     screen_name: str) -> plt.Figure:
-    """Line graph for condition screening result."""
+                     screen_name: str, probs_smooth: np.ndarray = None) -> plt.Figure:
+    """Line graph for condition screening result.
+
+    If probs_smooth differs from probs (smoothing window > 1), the raw curve
+    is drawn as a faint dotted reference line and the smoothed curve becomes
+    the main plotted/filled line.
+    """
     xlabel = SCREEN_LABELS[screen_name]
     LLPS_COLOR = "#e74c3c"
     NON_COLOR  = "#2980b9"
 
+    smoothed = probs_smooth is not None and not np.array_equal(probs, probs_smooth)
+    plot_probs = probs_smooth if smoothed else probs
+
     fig, ax = plt.subplots(figsize=(9, 5))
-    ax.plot(xvals, probs, lw=2.5, color=NON_COLOR, label="LLPS Probability", zorder=3)
+    if smoothed:
+        ax.plot(xvals, probs, lw=1.2, color=NON_COLOR, alpha=0.35, ls=":",
+                label="Raw", zorder=2)
+    ax.plot(xvals, plot_probs, lw=2.5, color=NON_COLOR,
+            label="Smoothed LLPS Probability" if smoothed else "LLPS Probability",
+            zorder=3)
     ax.axhline(0.5, color=LLPS_COLOR, lw=1.8, ls="--",
                label="Threshold 0.5", zorder=4)
-    ax.fill_between(xvals, probs, 0.5,
-                    where=(probs >= 0.5), alpha=0.22,
+    ax.fill_between(xvals, plot_probs, 0.5,
+                    where=(plot_probs >= 0.5), alpha=0.22,
                     color=LLPS_COLOR, label="LLPS region", zorder=2)
-    ax.fill_between(xvals, probs, 0.5,
-                    where=(probs < 0.5),  alpha=0.15,
+    ax.fill_between(xvals, plot_probs, 0.5,
+                    where=(plot_probs < 0.5),  alpha=0.15,
                     color=NON_COLOR, label="Non-LLPS region", zorder=2)
     ax.set_xlim(xvals[0], xvals[-1])
     ax.set_ylim(0, 1)
@@ -350,7 +372,8 @@ def cb_predict(feat,
 def cb_screen(feat, screen_name,
               fix_temp, fix_conc, fix_pH,
               nacl, mgcl2, kcl, glyc,
-              peg1, peg2, peg3, ficoll, dext40, dext70):
+              peg1, peg2, peg3, ficoll, dext40, dext70,
+              smooth_window):
     """Tab 2: Screen LLPS across a range of one condition."""
     if feat is None:
         return None, "⚠️ Please extract the T5 feature first (Step 2)."
@@ -368,15 +391,22 @@ def cb_screen(feat, screen_name,
             probs.append(model_predict(feat, cond))
 
         probs = np.array(probs)
-        fig   = screening_figure(xvals, probs, screen_name)
 
-        peak_idx = probs.argmax()
+        # Slider step keeps this odd (1, 3, 5, ...); window=1 is a no-op average.
+        window = int(smooth_window)
+        probs_plot = moving_average(probs, window) if window > 1 else probs
+
+        fig = screening_figure(xvals, probs, screen_name, probs_plot)
+
+        peak_idx = probs_plot.argmax()
         xlabel   = SCREEN_LABELS[screen_name]
         txt = (f"Screening completed.  \n"
-               f"Peak probability **{probs[peak_idx]:.4f}** "
+               f"Peak probability **{probs_plot[peak_idx]:.4f}** "
                f"at {xlabel} = **{xvals[peak_idx]:.1f}**  \n"
                f"LLPS-positive range: "
-               f"**{(probs >= 0.5).sum()}** / {len(probs)} points ≥ 0.5")
+               f"**{(probs_plot >= 0.5).sum()}** / {len(probs_plot)} points ≥ 0.5")
+        if window > 1:
+            txt += f"  \n*(Smoothed with moving-average window size {window})*"
         return fig, txt
     except Exception as e:
         return None, f"❌ Screening failed: {e}"
@@ -614,6 +644,12 @@ with gr.Blocks(
                         s_dext40 = gr.Slider(0, 50, value=0, step=1, label="Dextran ≤40 kDa (%)")
                         s_dext70 = gr.Slider(0, 50, value=0, step=1, label="Dextran ≥70 kDa (%)")
 
+                s_smooth = gr.Slider(
+                    1, 21, value=15, step=2,
+                    label="Smoothing Window Size",
+                    info="Odd window size for moving-average smoothing of the screening curve (1 = no smoothing).",
+                )
+
                 screen_btn  = gr.Button("📊 Run Condition Screening", variant="primary")
                 screen_plot = gr.Plot(label="Screening Result")
                 screen_text = gr.Markdown("")
@@ -625,6 +661,7 @@ with gr.Blocks(
                         s_temp, s_conc, s_pH,
                         s_nacl, s_mgcl2, s_kcl, s_glyc,
                         s_peg1, s_peg2, s_peg3, s_ficoll, s_dext40, s_dext70,
+                        s_smooth,
                     ],
                     outputs=[screen_plot, screen_text],
                 )