feat(gradio): switch to gradio

README.md

@@ -4,9 +4,7 @@ emoji: 💻
 colorFrom: blue
 colorTo: red
 sdk: gradio
-sdk_version: 6.2.0
+sdk_version: 4.19.2
 app_file: app.py
 pinned: false
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,30 +1,36 @@
 import json
 import os
 
+import gradio as gr
 import pandas as pd
-import streamlit as st
-
-st.set_page_config(page_title="GitHub Trend Predictor", page_icon="📈")
-st.title("📈 GitHub Trend Predictions")
-
-# Path to the file pushed by your GitHub Action
-file_path = "predictions.json"
-
-if os.path.exists(file_path):
-    with open(file_path, "r") as f:
-        data = json.load(f)
-
-    st.success(f"Last updated: {os.path.getmtime(file_path)}")
-
-    # Display as a searchable/sortable table if it's a list
-    if isinstance(data, list):
-        df = pd.DataFrame(data)
-        st.dataframe(df, use_container_width=True)
-    else:
-        # Fallback for nested JSON
-        st.json(data)
-else:
-    st.info("Waiting for the first data sync from GitHub...")
-    st.image(
-        "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/wait.png"
+
+
+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!"]}
     )
+
+
+# Create the Gradio Interface
+with gr.Blocks(theme=gr.themes.Soft()) 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)
+
+    refresh_btn = gr.Button("🔄 Refresh Table")
+    refresh_btn.click(fn=get_data, outputs=table)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -1,2 +1,2 @@
-streamlit
+gradio
 pandas