Create app.py

app.py

@@ -0,0 +1,170 @@
+import gradio as gr
+import pandas as pd
+import numpy as np
+from typing import List, Dict, Any, Tuple, Optional
+
+def create_cluster_browser_app():
+    """
+    Create a simple Gradio app for browsing prompts by cluster from uploaded CSV file.
+    """
+    
+    def load_and_validate_csv(file) -> Tuple[Optional[pd.DataFrame], str, List[str], str]:
+        """
+        Load and validate the uploaded CSV file.
+        
+        Args:
+            file: Uploaded file object from Gradio
+            
+        Returns:
+            Tuple of (dataframe, status_message, cluster_options, cluster_stats)
+        """
+        if file is None:
+            return None, "Please upload a CSV file with 'prompt' and 'cluster' columns.", ["(No data loaded)"], ""
+        
+        try:
+            df = pd.read_csv(file.name)
+            
+            # Validate required columns
+            required_cols = ['prompt', 'cluster']
+            missing_cols = [col for col in required_cols if col not in df.columns]
+            
+            if missing_cols:
+                return None, f"Missing required columns: {missing_cols}. Please ensure your CSV has 'prompt' and 'cluster' columns.", ["(No data loaded)"], ""
+            
+            # Validate data types
+            if not pd.api.types.is_numeric_dtype(df['cluster']):
+                return None, "The 'cluster' column must contain numeric values.", ["(No data loaded)"], ""
+            
+            # Get cluster options
+            unique_clusters = sorted(df['cluster'].unique())
+            cluster_options = ["(All Clusters)"] + [f"Cluster {c}" for c in unique_clusters]
+            
+            # Get cluster statistics
+            stats = []
+            for cluster_num in unique_clusters:
+                count = len(df[df['cluster'] == cluster_num])
+                stats.append(f"Cluster {cluster_num}: {count} prompts")
+            
+            total_prompts = len(df)
+            stats_text = f"**Total Prompts:** {total_prompts}\n\n**Cluster Distribution:**\n" + "\n".join(stats)
+            
+            return df, f"✅ Successfully loaded {len(df)} prompts with {len(unique_clusters)} clusters.", cluster_options, stats_text
+            
+        except Exception as e:
+            return None, f"Error loading CSV file: {str(e)}", ["(No data loaded)"], ""
+    
+    def filter_by_cluster(df: pd.DataFrame, cluster_sel: str) -> pd.DataFrame:
+        """Filter dataframe by selected cluster."""
+        if df is None or cluster_sel == "(All Clusters)" or cluster_sel == "(No data loaded)":
+            return df if df is not None else pd.DataFrame()
+        
+        cluster_num = int(cluster_sel.split()[-1])  # Extract number from "Cluster X"
+        return df[df['cluster'] == cluster_num].reset_index(drop=True)
+    
+    def format_prompt_cell(prompt_text: str) -> str:
+        """Format a single prompt in its own cell."""
+        return f"""
+        <div style="
+            background: #f8f9fa; 
+            border: 1px solid #e9ecef; 
+            border-radius: 8px; 
+            padding: 16px; 
+            margin: 8px 0; 
+            box-shadow: 0 1px 3px rgba(0,0,0,0.1);
+        ">
+            <div style="font-size: 14px; line-height: 1.5; color: #333;">
+                {prompt_text}
+            </div>
+        </div>
+        """
+    
+    def format_prompts(df: pd.DataFrame) -> str:
+        """Format all prompts in the dataframe as individual cells."""
+        if df is None or len(df) == 0:
+            return "No prompts to display."
+        
+        formatted_prompts = []
+        for idx, row in df.iterrows():
+            prompt_text = str(row['prompt']).strip()
+            formatted_prompts.append(format_prompt_cell(prompt_text))
+        
+        return "\n".join(formatted_prompts)
+    
+    def on_file_upload(file):
+        """Handle file upload and validation."""
+        df, status_msg, cluster_options, cluster_stats = load_and_validate_csv(file)
+        
+        if df is not None:
+            # Show all prompts initially
+            prompts_html = format_prompts(df)
+            return df, status_msg, gr.Dropdown(choices=cluster_options, value="(All Clusters)", interactive=True), prompts_html, cluster_stats
+        else:
+            return None, status_msg, gr.Dropdown(choices=cluster_options, value="(No data loaded)", interactive=False), "No data loaded.", ""
+    
+    def on_cluster_change(df, cluster_sel):
+        """Handle cluster selection change."""
+        if df is None:
+            return "No data loaded."
+        
+        filtered_df = filter_by_cluster(df, cluster_sel)
+        return format_prompts(filtered_df)
+    
+    # Create the Gradio interface
+    with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue")) as demo:
+        gr.Markdown("# Prompt Cluster Browser")
+        
+        # Store the loaded dataframe
+        df_state = gr.State(None)
+        
+        with gr.Row():
+            # Sidebar
+            with gr.Column(scale=1):
+                # File upload section
+                file_upload = gr.File(
+                    label="Upload Clustered Prompts CSV", 
+                    file_types=[".csv"],
+                    file_count="single"
+                )
+                
+                # Status
+                status_md = gr.Markdown("Please upload a CSV file to get started.")
+                
+                # Cluster statistics
+                stats_md = gr.Markdown("")
+                
+                # Cluster selection
+                cluster_dropdown = gr.Dropdown(
+                    ["(No data loaded)"], 
+                    label="Select Cluster", 
+                    value="(No data loaded)",
+                    interactive=False
+                )
+                
+            # Main content area
+            with gr.Column(scale=3):
+                prompts_html = gr.HTML("Upload a CSV file to browse clusters")
+        
+        # Connect event handlers
+        file_upload.change(
+            on_file_upload,
+            [file_upload],
+            [df_state, status_md, cluster_dropdown, prompts_html, stats_md]
+        )
+        
+        cluster_dropdown.change(
+            on_cluster_change,
+            [df_state, cluster_dropdown],
+            [prompts_html]
+        )
+    
+    return demo
+
+def launch_cluster_browser():
+    """
+    Launch the cluster browser app.
+    """
+    app = create_cluster_browser_app()
+    app.launch()
+
+if __name__ == "__main__":
+    launch_cluster_browser()