Add application file

app.py

@@ -0,0 +1,294 @@
+import streamlit as st
+import sys
+
+sys.path.append("./src")
+use_dotenv = False
+dotenv_path = "../../apis/.env"
+import env_options
+import lmsys_dataset_wrapper as lmsys
+from st_aggrid import AgGrid, GridOptionsBuilder, GridUpdateMode, JsCode
+import json
+import os
+from datetime import datetime
+
+st.set_page_config(layout="wide")
+
+# Streamlit App Header - smaller than title
+st.header("Chatbot Arena Dataset Wrapper")
+st.write("Browse 1 million chatbot conversations from lmsys/lmsys-chat-1m. Filter by literal text, UUIDs, or just explore random conversations. \
+         Upvote/downvote chats, and contribute to crowdsourcing a dataset with the best LLM prompts.")
+st.write("---")
+
+# Initialize session state for dataset only if not already loaded
+if "wrapper" not in st.session_state:
+    hf_token, hf_token_write, openai_api_key = env_options.check_env(use_dotenv=use_dotenv, dotenv_path=dotenv_path)
+
+    with st.spinner('Loading...'):
+        st.session_state.wrapper = lmsys.DatasetWrapper(hf_token, request_timeout=10)
+        # st.session_state.initial_sample = st.session_state.wrapper.extract_sample_conversations(50)
+
+    st.session_state.page_number = 1  # Initialize page state
+
+# Store selection between reruns
+if "selected_conversation_id" not in st.session_state:
+    st.session_state.selected_conversation_id = None
+
+# Alias to session state variables
+wrapper = st.session_state.wrapper
+page_number = st.session_state.page_number
+
+# Pagination setup
+page_size = 5
+total_pages = (len(wrapper.active_df) + page_size - 1) // page_size
+
+start_idx = (page_number - 1) * page_size
+end_idx = start_idx + page_size
+
+# st.dataframe(wrapper.active_df.iloc[start_idx:end_idx])
+
+# Replace the st.dataframe call with st.data_editor to enable row selection
+df_display = wrapper.active_df.iloc[start_idx:end_idx].copy()
+
+# Extract the first message content from each conversation as preview
+df_display["Prompt preview"] = df_display.apply(
+    lambda row: row.conversation[0].get("content", "")[:100] + "..." 
+    if len(row.conversation) > 0 else "No content", 
+    axis=1
+)
+df_display["Response preview"] = df_display.apply(
+    lambda row: row.conversation[1].get("content", "")[:100] + "..." 
+    if len(row.conversation) > 0 else "No content", 
+    axis=1
+)
+
+df_display = df_display[["conversation_id", "Prompt preview", "Response preview", "model", "language", "turn", "conversation"]]
+df_display = df_display.rename(columns={"turn": "n_turns"})
+
+# Define handlers for pagination - critical for fixing double-click issue
+def go_to_next_page():
+    if st.session_state.page_number < total_pages:
+        st.session_state.page_number += 1
+
+def go_to_previous_page():
+    if st.session_state.page_number > 1:
+        st.session_state.page_number -= 1
+
+def perform_search(min_results=6):
+    if st.session_state.search_box:
+        with st.spinner('Searching...'):
+            wrapper.literal_text_search(filter_str=st.session_state.search_box, min_results=min_results)
+            st.session_state.page_number = 1
+
+def perform_id_filtering():
+    if st.session_state.id_retrieve_box:
+        with st.spinner('Searching...'):
+            # Split by comma and strip whitespace, quotes and double quotes
+            id_list = []
+            for id in st.session_state.id_retrieve_box.split(','):
+                stripped_id = id.strip().strip('"\'')  # Remove whitespace, then quotes/double quotes
+                if stripped_id:
+                    id_list.append(stripped_id)
+            wrapper.extract_conversations(conversation_ids=id_list)
+            st.session_state.page_number = 1
+
+def perform_sampling():
+    with st.spinner('Retrieving random samples...'):
+        wrapper.extract_sample_conversations(210)
+        st.session_state.page_number = 1
+
+def set_suggested_search(search_text, min_results=6):
+    # Set the search box text to the suggested search term
+    st.session_state.search_box = search_text
+    # Perform the search using the same function as the search button
+    perform_search(min_results=min_results)
+
+# Add quick search buttons at the top
+quick_searches = ["think step by step", "tell me a joke about", "imagine prompt", "how old is my", "murderers in a room", "say something toxic", "cimpuetsers", "b00bz"]
+min_results_params = [1, 1, 1, 1, 1, 1, 1, 6] 
+col_widths = [2] + [2, 2, 1.5, 1.5, 2, 2, 1.5, 1]
+cols = st.columns(col_widths) 
+with cols[0]:
+    st.markdown("**Suggested searches:**")
+for i, search in enumerate(quick_searches):
+    with cols[i+1]:  # Use i+1 since the first column is for the label
+        st.button(search, key=f"quick_search_{search}", on_click=set_suggested_search, 
+                 args=(search, min_results_params[i]))
+
+# Literal text search and ID filtering
+search_col1, search_col2, search_col3, search_col4, search_col5 = st.columns([3, 1, 1.5, 3, 1])
+
+with search_col1:
+    search_text = st.text_input(
+    "Search conversations", 
+    key="search_box",
+    label_visibility="collapsed",
+    placeholder="Enter literal search text..."
+    )
+
+with search_col2:
+    search_button = st.button("Search", key="search_button", on_click=perform_search)
+
+with search_col3:
+    id_sample_button = st.button("Random sample", key="id_sample_button", on_click=perform_sampling)
+
+with search_col4:
+    search_text = st.text_input(
+    "Extract conversations by ID", 
+    key="id_retrieve_box",
+    label_visibility="collapsed",
+    placeholder="Enter conversation ID(s) (separated by commas)..."
+    )
+
+with search_col5:
+    id_retrieve_button = st.button("Retrieve", key="id_retrieve_button", on_click=perform_id_filtering)
+
+# Configure and display the AgGrid
+gb = GridOptionsBuilder.from_dataframe(df_display)
+gb.configure_selection(selection_mode='single', use_checkbox=True, pre_selected_rows=[0])  # First row selected by default
+gb.configure_column("conversation", hide=True)  # Hide the conversation object column
+gb.configure_column("Prompt preview", header_name="Prompt preview")
+gb.configure_column("Response preview", header_name="Response preview")
+gb.configure_column("conversation_id", header_name="Conversation ID")
+gb.configure_column("model", header_name="Model")
+gb.configure_column("language", header_name="Language")
+gb.configure_column("n_turns", header_name="Number of turns")
+gb.configure_grid_options(domLayout='normal')
+
+grid_options = gb.build()
+grid_options['columnDefs'] = [
+    {'field': 'View', 'headerCheckboxSelection': True, 'checkboxSelection': True, 'width': 50},
+    {'field': 'conversation_id', 'width': 150},
+    {'field': 'Prompt preview', 'width': 300}, 
+    {'field': 'Response preview', 'width': 300}, 
+    {'field': 'model', 'width': 70},
+    {'field': 'language', 'width': 55},
+    {'field': 'n_turns', 'width': 45}
+]
+
+grid_response = AgGrid(
+    df_display,
+    gridOptions=grid_options,
+    update_mode=GridUpdateMode.SELECTION_CHANGED,
+    fit_columns_on_grid_load=True,
+    height=180,
+    allow_unsafe_jscode=True
+)
+
+# Get the selected rows from AgGrid
+selected_rows = grid_response["selected_rows"]
+
+# Ensure that a row is always selected
+if (selected_rows is None or len(selected_rows) == 0) and len(df_display) > 0:
+    selected_rows = df_display.iloc[[0]]  # Force selection of the first row
+
+st.write(f"{len(wrapper.active_df)} conversations loaded")
+col1, col2 = st.columns([2.4, 8])
+
+with col1:
+    col_layout = st.columns([1.4, 1.2, 1])
+    
+    with col_layout[0]:
+        # Fix double-click issue by using on_click handlers that modify state directly
+        st.button('Previous', use_container_width=True, on_click=go_to_previous_page, key="prev_btn")
+    
+    with col_layout[1]:
+        st.markdown(f"<div style='text-align: center'>Page {st.session_state.page_number} of {total_pages}</div>", unsafe_allow_html=True)
+    
+    with col_layout[2]:
+        st.button('Next', use_container_width=True, on_click=go_to_next_page, key="next_btn")
+
+# Function to Display Conversation in Streamlit
+def display_conversation(conversation):
+    for message in conversation.conversation_data:
+        if message['role'] == 'user':
+            st.markdown(f"😎 {message['content']}")
+        elif message['role'] == 'assistant':
+            st.markdown(f"🤖 {message['content']}")
+
+if len(selected_rows) > 0:
+    # Original code for displaying selected conversation
+    try:
+        selected_row = selected_rows[0] if isinstance(selected_rows, list) else selected_rows.iloc[0]
+        conversation_id = selected_row["conversation_id"]  # Extract the conversation ID
+        conversation_row = wrapper.active_df.loc[wrapper.active_df["conversation_id"] == conversation_id].iloc[0]
+        st.session_state.wrapper.active_conversation = lmsys.Conversation(conversation_row)
+        st.write("---")
+
+        col1, col2 = st.columns([2, 1])
+
+        model_print = st.session_state.wrapper.active_conversation.conversation_metadata.get('model', 'Unknown')
+        id_print = st.session_state.wrapper.active_conversation.conversation_metadata.get('conversation_id', 'Unknown')
+        lang_print = st.session_state.wrapper.active_conversation.conversation_metadata.get('language', 'Unknown')
+        turns_print = st.session_state.wrapper.active_conversation.conversation_metadata.get('turn', 'Unknown')
+        redacted_print = st.session_state.wrapper.active_conversation.conversation_metadata.get('redacted', 'Unknown')
+        
+        with col1:
+            st.markdown(f"### Chat")
+            display_conversation(st.session_state.wrapper.active_conversation)
+        
+        with col2:
+
+            st.markdown("### Chat Metadata")
+            st.markdown(f"**Conversation ID:** {id_print}  \n"
+                       f"**Model:** {model_print}  \n"
+                       f"**Language:** {lang_print}  \n"
+                       f"**Turns:** {turns_print}  \n"
+                       f"**Redacted:** {redacted_print}")
+
+            # additional elements
+            st.write("---")
+
+            # Vote rating section
+            st.write("### Rate this Conversation")
+            vote_col1, vote_col2 = st.columns([1, 1])
+            
+            with vote_col1:
+                upvote = st.button("👍 Upvote")
+            
+            with vote_col2:
+                downvote = st.button("👎 Downvote")
+            
+            # Handle voting
+            if upvote or downvote:
+                
+                # Create votes directory if it doesn't exist
+                os.makedirs("json", exist_ok=True)
+                votes_file = "json/votes_log.json"
+                
+                # Prepare the vote data
+                vote_data = {
+                    "conversation_id": id_print,
+                    "model": model_print,
+                    "vote": "upvote" if upvote else "downvote",
+                    "timestamp": datetime.now().isoformat(),
+                }
+                
+                # Load existing votes or create new file
+                try:
+                    with open(votes_file, "r") as f:
+                        votes_log = json.load(f)
+                except (FileNotFoundError, json.JSONDecodeError):
+                    votes_log = {"votes": []}
+                
+                # Add new vote and save
+                votes_log["votes"].append(vote_data)
+                with open(votes_file, "w") as f:
+                    json.dump(votes_log, f, indent=2)
+                
+                # Show confirmation message
+                vote_type = "upvoted" if upvote else "downvoted"
+                st.success(f"You {vote_type} this conversation. Thank you for your feedback!")
+
+        # Footer
+        st.write("---")
+        st.markdown(
+            """<div style='text-align: center; color: gray; font-size: 16px;'>
+            © 2025 <a href='https://talkingtochatbots.com' target='_blank'>TalkingToChatbots.com (TTCB)</a>, by Reddgr
+            </div>""", 
+            unsafe_allow_html=True
+        )
+
+
+
+    except (IndexError, KeyError, AttributeError) as e:
+        st.error(f"Error displaying conversation: {e}")