Delete app.py

app.py

@@ -1,142 +0,0 @@
-import gradio as gr
-from langchain_community.chat_models import ChatOpenAI
-from langchain.memory import ConversationBufferMemory, SimpleMemory
-from langchain.agents import initialize_agent, AgentType
-from langchain_community.callbacks import ClearMLCallbackHandler
-from langchain_core.callbacks import StdOutCallbackHandler
-from clearml import Logger, Task
-from dotenv import load_dotenv
-from dotenv import load_dotenv, find_dotenv
-import os
-import agent.planning_agent as planning_agent
-import logging
-
-# Configure logging
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-# Global variables
-llm = None
-chat_memory = None
-query_memory = None
-clearml_callback = None
-
-def initialize_components():
-    global llm, chat_memory, query_memory, clearml_callback
-    load_dotenv()
-    
-    OPENAI_API_KEY="sk-ah4Ar22m-1gdxwT5lg0KAodXVK5wt19cOzMBCV1mZ0T3BlbkFJzIhW66UefL-sl55mh4W-bcP1jUymqLHFuFEWC5bl8A"
-    CLEARML_API_ACCESS_KEY="L7F2UFV6OK9FN3XLK18F4Q166EK591F"
-    CLEARML_API_SECRET_KEY="L7zkP23yCOFFfFjX_21YV7ijyfe8a681YejTGF--f6Ovf98VCafY-84JAzwyiLphIBI"
-    SERPAPI_API_KEY="619f2302253fbe56448bcf82565caf2a3263d845944682533f10b09a0d1650e6"
-    
-    # openai.api_key=OPENAI_API_KEY
-    
-    llm = ChatOpenAI(
-        model_name="gpt-3.5-turbo",
-        temperature=0,
-        openai_api_key="sk-ah4Ar22m-1gdxwT5lg0KAodXVK5wt19cOzMBCV1mZ0T3BlbkFJzIhW66UefL-sl55mh4W-bcP1jUymqLHFuFEWC5bl8A"
-    )
-
-    # Initialize memories
-    chat_memory = ConversationBufferMemory(
-        memory_key="chat_history",
-        return_messages=True
-    )
-    query_memory = SimpleMemory()
-
-    # Setup and use the ClearML Callback
-    clearml_callback = ClearMLCallbackHandler(
-    task_type="inference",
-    project_name="langchain_callback_demo",
-    task_name="llm",
-    tags=["test"],
-    # Change the following parameters based on the amount of detail you want tracked
-    visualize=True,
-    complexity_metrics=True,
-    stream_logs=True,)
-    
-    callbacks = [StdOutCallbackHandler(), clearml_callback]
-    
-    # Initialize planning agent with both memories
-    planning_agent.initialize_planning_agent(llm, chat_memory, query_memory, callbacks)
-
-    logger.info("Components initialized successfully")
-
-def process_query(query, history):
-    try:
-        # Restore chat history from Gradio's history
-        if history:
-            for human_msg, ai_msg in history:
-                if chat_memory and hasattr(chat_memory, 'chat_memory'):
-                    chat_memory.chat_memory.add_user_message(human_msg)
-                    chat_memory.chat_memory.add_ai_message(ai_msg)
-        
-        # Store original query in query memory
-        query_memory.memories['original_query'] = query
-        
-        # Execute query through planning agent
-        response = planning_agent.execute(query)
-        #clearml_callback.flush_tracker(name="Planning agent", finish=True)
-        #clearml_callback.flush_tracker(langchain_asset=llm, name="simple_sequential")
-        
-        # Add current interaction to chat memory
-        if chat_memory and hasattr(chat_memory, 'chat_memory'):
-            chat_memory.chat_memory.add_user_message(query)
-            chat_memory.chat_memory.add_ai_message(response)
-        
-        return response
-
-    except Exception as e:
-        error_msg = f"Error processing query: {str(e)}"
-        logger.error(f"Error details: {str(e)}")
-
-        if chat_memory and hasattr(chat_memory, 'chat_memory'):
-            chat_memory.chat_memory.add_user_message(query)
-            chat_memory.chat_memory.add_ai_message(error_msg)
-
-        return error_msg
-
-def clear_context():
-    planning_agent.clear_context()
-    chat_memory.clear()
-    query_memory.memories.clear()
-    return [], []
-
-def create_gradio_app():
-    from interface import create_interface
-    return create_interface(process_query, clear_context)
-
-def report_table(loer, iteration=0):
-    # type: (Logger, int) -> ()
-    """
-    reporting tables to the plots section
-    :param logger: The task.logger to use for sending the plots
-    :param iteration: The iteration number of the current reports
-    """
-    # report table
-    # Report table - CSV from path
-    csv_path = './data/cleaned_dataset_full.csv'
-    loer.report_table("Data Set Capstone", "remote csv", iteration=iteration, csv=csv_path)
-
-def main():
-    """Main application entry point"""
-    try:
-        initialize_components()
-        app = create_gradio_app()
-        app.queue()
-        app.launch(server_name="0.0.0.0", server_port=7860, share=True)
-        a_task = Task.get_task(project_name='langchain_callback_demo', task_name='llm')
-        loer = a_task.get_logger()
-        report_logs(loer)
-        # report text as debug example
-        report_debug_text(loer)
-        # report graphs
-        report_table(loer)
-        loer.flush()
-    except Exception as e:
-        logger.error(f"Error in main: {str(e)}")
-        raise
-
-if __name__ == "__main__":
-    main()