working initial version

.cursorrules

@@ -1,4 +1,5 @@
 You use Python 3.12 and Frameworks: gradio. 
+
 Here are some best practices and rules you must follow:
 
 
@@ -15,4 +16,8 @@ Here are some best practices and rules you must follow:
 11. **Minimize Global Variables**: Limit the use of global variables to reduce side effects and improve code maintainability. Use function parameters and return values to pass data instead.
 12. **Use Context Managers**: Employ context managers (with statements) for resource management, such as file operations or database connections, to ensure proper cleanup.
 
-These rules will help you write clean, efficient, and maintainable Python code.
+These rules will help you write clean, efficient, and maintainable Python code.
+
+Common mistakes you make:
+
+1. you hallucinate Gradio APIs. doublecheck the docs. or list a few options and ask user to pick.

app.py

@@ -1,27 +1,152 @@
 import gradio as gr
+import asyncio
+from typing import List, Dict, Any, Tuple, Generator
+from beeai import Bee
 from huggingface_hub import InferenceClient
+import logging
+from datetime import datetime
+import pytz
+import pandas as pd
+from functools import partial
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+# Set up logging with a higher level
+logging.basicConfig(level=logging.INFO,
+                    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
+                    filename='app.log',
+                    filemode='w')
+
+# Global variable to track the current page
+current_page = 1
+total_pages = 1
+
+async def fetch_conversations(api_key: str, page: int = 1) -> Dict[str, Any]:
+    bee = Bee(api_key)
+    logging.info(f"Fetching conversations for user 'me', page {page}")
+    conversations = await bee.get_conversations("me", page=page)
+    return conversations
+
+def format_end_time(end_time: str) -> str:
+    utc_time = datetime.fromisoformat(end_time.replace('Z', '+00:00'))
+    pacific_time = utc_time.astimezone(pytz.timezone('US/Pacific'))
+    return pacific_time.strftime("%Y-%m-%d %I:%M:%S %p PT")
+
+async def fetch_conversation(api_key: str, conversation_id: int) -> Dict[str, Any]:
+    bee = Bee(api_key)
+    try:
+        logging.info(f"Fetching conversation with ID: {conversation_id}")
+        full_conversation = await bee.get_conversation("me", conversation_id)
+        logging.debug(f"Raw conversation data: {full_conversation}")
+        return full_conversation
+    except Exception as e:
+        logging.error(f"Error fetching conversation {conversation_id}: {str(e)}")
+        return {"error": f"Failed to fetch conversation: {str(e)}"}
+
+def format_conversation(data: Dict[str, Any]) -> str:
+    try:
+        conversation = data.get("conversation", {})
+        logging.debug(f"Conversation keys: {conversation.keys()}")
+        formatted = f"# Conversation Details {conversation['id']}\n\n"
+        # Format start_time and end_time
+        start_time = conversation.get('start_time')
+        end_time = conversation.get('end_time')
+        if start_time and end_time:
+            start_dt = datetime.fromisoformat(start_time.replace('Z', '+00:00'))
+            end_dt = datetime.fromisoformat(end_time.replace('Z', '+00:00'))
+            pacific_tz = pytz.timezone('US/Pacific')
+            start_pacific = start_dt.astimezone(pacific_tz)
+            end_pacific = end_dt.astimezone(pacific_tz)
+            
+            if start_pacific.date() == end_pacific.date():
+                formatted += f"**Time**: {start_pacific.strftime('%I:%M %p')} - {end_pacific.strftime('%I:%M %p')} PT\n"
+            else:
+                formatted += f"**Start Time**: {start_pacific.strftime('%Y-%m-%d %I:%M %p')} PT\n"
+                formatted += f"**End Time**: {end_pacific.strftime('%Y-%m-%d %I:%M %p')} PT\n"
+        elif start_time:
+            start_time_formatted = format_end_time(start_time)
+            formatted += f"**Start Time**: {start_time_formatted}\n"
+        elif end_time:
+            end_time_formatted = format_end_time(end_time)
+            formatted += f"**End Time**: {end_time_formatted}\n"
+        
+        # Display short_summary nicely
+        if 'short_summary' in conversation:
+            formatted += f"\n## Short Summary\n\n{conversation['short_summary']}\n"
+
+        formatted += "\n"  # Add a newline for better readability
+
+        formatted += f"\n{conversation['summary']}"
+        # for key in ['summary']: #, 'short_summary', 'state', 'created_at', 'updated_at']:
+        #     if key in conversation:
+        #         formatted += f"**{key}**: {conversation[key]}\n"
+        
+        if 'transcriptions' in conversation and conversation['transcriptions']:
+            formatted += "\n\n## Transcriptions\n\n"
+            last_timestamp = None
+            for utterance in conversation['transcriptions'][0].get('utterances', []):
+                current_timestamp = utterance.get('spoken_at')
+                speaker = utterance.get('speaker')
+                text = utterance.get('text')
+                
+                formatted += f"Speaker **[{speaker}]({current_timestamp})**: {text}\n\n"
+        
+        return formatted
+    except Exception as e:
+        logging.error(f"Error formatting conversation: {str(e)}")
+        return f"Error formatting conversation: {str(e)}\n\nRaw data: {conversation}"
+
+async def list_conversations(api_key: str) -> Tuple[pd.DataFrame, str, int, int]:
+    global current_page, total_pages
+    conversations_data = await fetch_conversations(api_key, current_page)
+    conversations = conversations_data.get("conversations", [])
+    total_pages = conversations_data.get("totalPages", 1)
+    df = pd.DataFrame([
+        {
+            "ID": c['id'],
+            "End Time": format_end_time(c['end_time']),
+            "Summary": c['short_summary'][1:50] + "..."
+        }
+        for c in conversations
+    ])
+    df = df[["ID", "End Time", "Summary"]]  # Reorder columns to ensure ID is first
+    info = f"Page {current_page} of {total_pages}"
+    return df, info, current_page, total_pages
+
+async def display_conversation(api_key: str, conversation_id: int) -> str:
+    full_conversation = await fetch_conversation(api_key, conversation_id)
+    if "error" in full_conversation:
+        logging.error(f"Error in full_conversation: {full_conversation['error']}")
+        return full_conversation["error"]
+    formatted_conversation = format_conversation(full_conversation)
+    return formatted_conversation
+
+async def delete_conversation(api_key: str, conversation_id: int) -> str:
+    bee = Bee(api_key)
+    try:
+        await bee.delete_conversation("me", conversation_id)
+        return f"Conversation {conversation_id} deleted successfully."
+    except Exception as e:
+        logging.error(f"Error deleting conversation {conversation_id}: {str(e)}")
+        return f"Failed to delete conversation: {str(e)}"
 
+client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
 def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+    message: str,
+    history: List[Tuple[str, str]],
+    system_message: str,
+    max_tokens: int,
+    temperature: float,
+    top_p: float,
+    conversation_context: str
+) -> Generator[str, None, None]:
+    messages = [
+        {"role": "system", "content": system_message},
+        {"role": "system", "content": f"Here's the context of the conversation: {conversation_context}"}
+    ]
+
+    for human, assistant in history:
+        messages.append({"role": "user", "content": human})
+        messages.append({"role": "assistant", "content": assistant})
 
     messages.append({"role": "user", "content": message})
 
@@ -35,30 +160,109 @@ def respond(
         top_p=top_p,
     ):
         token = message.choices[0].delta.content
-
         response += token
         yield response
 
+# Add this new function
+def get_selected_conversation_id(table_data):
+    if table_data and len(table_data) > 0:
+        # Assuming the ID is in the first column
+        return table_data[0][0]
+    return None
+
+async def delete_selected_conversation(api_key: str, conversation_id: int):
+    if not api_key or not conversation_id:
+        return "No conversation selected or API key missing", None, None, gr.update(visible=False), ""
+    
+    logging.info(f"Deleting conversation with ID: {conversation_id}")
+    
+    try:
+        result = await delete_conversation(api_key, conversation_id)
+        df, info, current_page, total_pages = await list_conversations(api_key)
+        return result, df, info, gr.update(visible=False), ""
+    except Exception as e:
+        error_message = f"Error deleting conversation: {str(e)}"
+        logging.error(error_message)
+        return error_message, None, None, gr.update(visible=False), ""
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Bee AI Conversation Viewer and Chat")
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            api_key = gr.Textbox(label="Enter your Bee API Key", type="password")
+            load_button = gr.Button("Load Conversations")
+            conversation_table = gr.Dataframe(label="Select a conversation (CLICK ON THE ID)", interactive=True)
+            info_text = gr.Textbox(label="Info", interactive=False)
+            prev_page = gr.Button("Previous Page")
+            next_page = gr.Button("Next Page")
+        
+        with gr.Column(scale=2):
+            conversation_details = gr.Markdown(
+                label="Conversation Details",
+                value="Enter your Bee API Key, click 'Load Conversations', then select a conversation to view details here."
+            )
+            delete_button = gr.Button("Delete Conversation", visible=False)
+    
+    selected_conversation_id = gr.State(None)
+
+    async def load_conversations(api_key):
+        try:
+            df, info, current_page, total_pages = await list_conversations(api_key)
+            prev_disabled = current_page == 1
+            next_disabled = current_page == total_pages
+            return df, info, gr.update(visible=True), gr.update(interactive=not prev_disabled), gr.update(interactive=not next_disabled)
+        except Exception as e:
+            error_message = f"Error loading conversations: {str(e)}"
+            logging.error(error_message)
+            return None, error_message, gr.update(visible=False), gr.update(interactive=False), gr.update(interactive=False)
+
+    load_button.click(load_conversations, inputs=[api_key], outputs=[conversation_table, info_text, delete_button, prev_page, next_page])
+
+    async def update_conversation(api_key, evt: gr.SelectData):
+        try:
+            logging.info(f"SelectData event: index={evt.index}, value={evt.value}")
+            conversation_id = int(evt.value)
+            logging.info(f"Updating conversation with ID: {conversation_id}")
+            formatted_conversation = await display_conversation(api_key, conversation_id)
+            return formatted_conversation, gr.update(visible=True), conversation_id
+        except Exception as e:
+            error_message = f"Error updating conversation: {str(e)}"
+            logging.error(error_message)
+            return error_message, gr.update(visible=False), None
+
+    conversation_table.select(update_conversation, inputs=[api_key], outputs=[conversation_details, delete_button, selected_conversation_id])
+
+    delete_button.click(
+        delete_selected_conversation,
+        inputs=[api_key, selected_conversation_id],
+        outputs=[conversation_details, conversation_table, info_text, delete_button, conversation_details]
+    )
+
+    async def change_page(api_key: str, direction: int) -> Tuple[pd.DataFrame, str, gr.update, gr.update]:
+        global current_page, total_pages
+        current_page += direction
+        current_page = max(1, min(current_page, total_pages))  # Ensure page is within bounds
+        df, info, current_page, total_pages = await list_conversations(api_key)
+        prev_disabled = current_page == 1
+        next_disabled = current_page == total_pages
+        return df, info, gr.update(interactive=not prev_disabled), gr.update(interactive=not next_disabled)
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
+    prev_page.click(partial(change_page, direction=-1), inputs=[api_key], outputs=[conversation_table, info_text, prev_page, next_page])
+    next_page.click(partial(change_page, direction=1), inputs=[api_key], outputs=[conversation_table, info_text, prev_page, next_page])
 
+    gr.Markdown("## Chat about the conversation")
+    
+    chat_interface = gr.ChatInterface(
+        respond,
+        additional_inputs=[
+            gr.Textbox(value="You are a friendly Chatbot. Analyze and discuss the given conversation context.", label="System message"),
+            gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
+            gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
+            gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
+            conversation_details
+        ],
+    )
 
 if __name__ == "__main__":
     demo.launch()

requirements.txt

@@ -1,4 +1,6 @@
 huggingface_hub==0.25.2
 beeai==1.0.1
 gradio==5.1.0
-gradio-tools==0.0.9
+gradio-tools==0.0.9
+pytz==2024.2
+pandas==2.2.3