feat(ui): add handling complex json

app.py

@@ -7,30 +7,56 @@ import pandas as pd
 
 def get_data():
     file_path = "predictions.json"
-    if os.path.exists(file_path):
-        try:
-            with open(file_path, "r") as f:
-                data = json.load(f)
-            # Assuming predictions.json is a list of dicts
-            return pd.DataFrame(data)
-        except Exception as e:
-            return pd.DataFrame({"Error": [f"Failed to parse JSON: {e}"]})
-    return pd.DataFrame(
-        {"Status": ["predictions.json not found. Run your GitHub Action to push data!"]}
-    )
+    if not os.path.exists(file_path):
+        return (
+            pd.DataFrame({"Status": ["File not found yet. Run GitHub Action."]}),
+            "N/A",
+        )
+
+    try:
+        with open(file_path, "r") as f:
+            raw_data = json.load(f)
+
+        # 1. Extract the nested list
+        predictions_list = raw_data.get("predictions", [])
+        date = raw_data.get("prediction_date", "Unknown Date")
+
+        # 2. Convert to DataFrame
+        df = pd.DataFrame(predictions_list)
+
+        # 3. Clean up the data (Optional but recommended)
+        if not df.empty:
+            # Sort by highest probability
+            df = df.sort_values(by="probability", ascending=False)
+            # Format probability to 2 decimal percentage (e.g., 0.953 -> 95.39%)
+            df["probability"] = df["probability"].apply(lambda x: f"{x:.2%}")
+            # Rename columns for a nicer UI
+            df.columns = ["Repository", "Main Language", "Trend Probability"]
+
+        return df, date
+
+    except Exception as e:
+        return pd.DataFrame({"Error": [str(e)]}), "Error"
 
 
 # Create the Gradio Interface
-with gr.Blocks(theme=gr.themes.Soft()) as demo:
+with gr.Blocks(theme=gr.themes.Default(primary_hue="blue")) as demo:
     gr.Markdown("# 🚀 GitHub Trend Predictor")
-    gr.Markdown("Visualizing trending repositories pushed from GitHub Actions.")
 
     with gr.Row():
-        # This will automatically load the data when the page opens
-        table = gr.Dataframe(value=get_data, interactive=False)
+        date_display = gr.Textbox(label="Prediction Date", interactive=False)
+
+    with gr.Row():
+        # interactive=False removes the editing capability
+        table = gr.Dataframe(interactive=False)
+
+    refresh_btn = gr.Button("🔄 Refresh Data")
+
+    # Load data on startup
+    demo.load(fn=get_data, outputs=[table, date_display])
 
-    refresh_btn = gr.Button("🔄 Refresh Table")
-    refresh_btn.click(fn=get_data, outputs=table)
+    # Reload on button click
+    refresh_btn.click(fn=get_data, outputs=[table, date_display])
 
 if __name__ == "__main__":
     demo.launch()