update readme

app.py

@@ -0,0 +1,30 @@
+import pandas as pd
+import gradio as gr
+
+# Load the CSV file
+file_path = 'iclr2024_reviews_20231110.csv'
+df = pd.read_csv(file_path)
+
+# Define a function to filter the DataFrame based on a search query
+def search_papers(query=""):
+    if query:  # If there is a search query, filter the DataFrame
+        filtered_df = df[df.apply(lambda row: row.astype(str).str.contains(query, case=False).any(), axis=1)]
+        return filtered_df
+    return df  # If no query, return the original DataFrame
+
+with gr.Blocks() as demo:
+    gr.Markdown("# ICLR 2024 Paper Review Explorer")
+    gr.Markdown("Explore and search through the paper reviews for ICLR 2024. The papers are ranked based on their average score and standard deviation. More data and analysis at [GitHub Repository](https://github.com/ranpox/iclr2024-openreview-submissions)")
+    search_bar = gr.Textbox(placeholder="Enter search terms here...", label="Search Reviews")
+    
+    # Initialize the reviews table with all the data
+    reviews_table = gr.Dataframe(df)
+
+    # When the search bar changes, update the reviews table with the filtered results
+    search_bar.change(
+        fn=search_papers,
+        inputs=search_bar,
+        outputs=reviews_table
+    )
+
+demo.launch()