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app.py

@@ -1,3 +1,4 @@
+\
 import os
 import io
 import pandas as pd
@@ -8,6 +9,7 @@ import gradio as gr
 import matplotlib
 matplotlib.use("Agg")
 import matplotlib.pyplot as plt
+from PIL import Image  # <-- add PIL for returning PIL.Image
 
 DEFAULT_CSV = "Trend_of_Top_10_Hard_Skills.csv"  # Place at repo root
 
@@ -26,15 +28,12 @@ def prepare_dataframe(df):
     """
     if "YearMonth" not in df.columns:
         raise gr.Error("CSV must have a 'YearMonth' column.")
-    # Parse YearMonth to datetime (coerce errors to NaT)
     dt = pd.to_datetime(df["YearMonth"], format="%Y-%m", errors="coerce")
-    # If parsing fails, try general parse
     if dt.isna().any():
         dt = pd.to_datetime(df["YearMonth"], errors="coerce")
     df = df.copy()
     df["_dt"] = dt
     df = df.sort_values("_dt")
-    # Coerce numeric columns
     for c in df.columns:
         if c not in ["YearMonth", "_dt"]:
             df[c] = pd.to_numeric(df[c], errors="coerce")
@@ -45,7 +44,7 @@ def list_skill_columns(df):
 
 def apply_smoothing(series, window):
     if window and window > 1:
-        return series.rolling(window=window, min_periods=1, center=False).mean()
+        return series.rolling(window=window, min_periods=1).mean()
     return series
 
 def normalize_series(series, mode):
@@ -54,27 +53,19 @@ def normalize_series(series, mode):
     s = series.copy()
     if mode == "min-max (per skill)":
         mn, mx = s.min(), s.max()
-        if mx > mn:
-            return (s - mn) / (mx - mn)
-        else:
-            return s.fillna(0.0)
+        return (s - mn) / (mx - mn) if mx > mn else s.fillna(0.0)
     if mode == "z-score (per skill)":
         mu, sd = s.mean(), s.std(ddof=0)
-        if sd > 0:
-            return (s - mu) / sd
-        else:
-            return s.fillna(0.0)
+        return (s - mu) / sd if sd > 0 else s.fillna(0.0)
     return series
 
 def plot_lines(df, selected_skills, smoothing_window, normalize_mode, show_markers, y_label):
     if not selected_skills:
         raise gr.Error("Please select at least one skill.")
 
-    # Prepare x
     x_dt = df["_dt"]
     x_labels = df["YearMonth"].astype(str).tolist()
 
-    # Create single chart
     fig = plt.figure(figsize=(10, 5.5))
     ax = fig.add_subplot(111)
 
@@ -95,40 +86,31 @@ def plot_lines(df, selected_skills, smoothing_window, normalize_mode, show_marke
     ax.legend(loc="best")
     ax.grid(True, which="both", axis="both", alpha=0.35)
 
-    # Format x ticks with Month labels
     ax.set_xticks(x_dt)
     ax.set_xticklabels(x_labels, rotation=45, ha="right")
 
     fig.tight_layout()
-    import io as _io
-    buf = _io.BytesIO()
+    buf = io.BytesIO()
     fig.savefig(buf, format="png", dpi=160, bbox_inches="tight")
     plt.close(fig)
     buf.seek(0)
-    return buf  # return BytesIO; Gradio Image can accept bytes-like
+    # Return PIL Image instead of BytesIO to satisfy gr.Image postprocess
+    return Image.open(buf)
 
-def run(
-    csv_file, selected_skills, smoothing_window, normalize_mode, show_markers, y_label
-):
+def run(csv_file, selected_skills, smoothing_window, normalize_mode, show_markers, y_label):
     df = load_dataframe(csv_file)
     df = prepare_dataframe(df)
     available = list_skill_columns(df)
-
-    # Auto-select if user didn't pick yet
     if not selected_skills:
         selected_skills = available
-
-    # Keep only existing skills
     selected_skills = [s for s in selected_skills if s in available]
-
-    img_buf = plot_lines(df, selected_skills, smoothing_window, normalize_mode, show_markers, y_label)
-    # Also return a preview table for selected columns
+    img = plot_lines(df, selected_skills, smoothing_window, normalize_mode, show_markers, y_label)
     preview_cols = ["YearMonth"] + selected_skills
     preview = df[preview_cols].reset_index(drop=True)
-    return img_buf, gr.update(choices=available, value=selected_skills), preview
+    return img, gr.update(choices=available, value=selected_skills), preview
 
 with gr.Blocks(title="Hard Skills Trend Line Chart") as demo:
-    gr.Markdown("# Hard Skills Trend — Line Chart\nUpload a CSV or place **Trend_of_Top_10_Hard_Skills.csv** in the repo root.")
+    gr.Markdown("# Hard Skills Trend — Line Chart\\nUpload a CSV or place **Trend_of_Top_10_Hard_Skills.csv** in the repo root.")
 
     with gr.Row():
         with gr.Column(scale=1):
@@ -141,10 +123,9 @@ with gr.Blocks(title="Hard Skills Trend Line Chart") as demo:
             btn = gr.Button("Plot", variant="primary")
 
         with gr.Column(scale=1):
-            out_img = gr.Image(label="Line Chart")
+            out_img = gr.Image(label="Line Chart", type="pil")  # explicitly set type=pil
             out_table = gr.Dataframe(label="Data preview")
 
-    # On click, return image, refresh skill choices, and table
     btn.click(
         fn=run,
         inputs=[csv_file, selected_skills, smoothing_window, normalize_mode, show_markers, y_label],

requirements.txt

@@ -2,3 +2,4 @@ gradio>=4.26.0
 pandas>=2.0.0
 matplotlib>=3.8
 numpy
+Pillow