Update app.py

app.py

@@ -1,97 +1,97 @@
-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 dotenv import load_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
-
-def initialize_components():
-    global llm, chat_memory, query_memory
-    load_dotenv()
-    api_key = os.environ['OA_API']           
-    os.environ['OPENAI_API_KEY'] = api_key
-    
-    llm = ChatOpenAI(
-        model_name="gpt-3.5-turbo",
-        temperature=0,
-        api_key=api_key
-    )
-
-    # Initialize memories
-    chat_memory = ConversationBufferMemory(
-        memory_key="chat_history",
-        return_messages=True
-    )
-    query_memory = SimpleMemory()
-
-    # Initialize planning agent with both memories
-    planning_agent.initialize_planning_agent(llm, chat_memory, query_memory)
-
-    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)
-        
-        # 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 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)
-    except Exception as e:
-        logger.error(f"Error in main: {str(e)}")
-        raise
-
-if __name__ == "__main__":
+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 dotenv import load_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
+
+def initialize_components():
+    global llm, chat_memory, query_memory
+    load_dotenv()
+    api_key = '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'           
+    os.environ['OPENAI_API_KEY'] = api_key
+    
+    llm = ChatOpenAI(
+        model_name="gpt-3.5-turbo",
+        temperature=0,
+        api_key=api_key
+    )
+
+    # Initialize memories
+    chat_memory = ConversationBufferMemory(
+        memory_key="chat_history",
+        return_messages=True
+    )
+    query_memory = SimpleMemory()
+
+    # Initialize planning agent with both memories
+    planning_agent.initialize_planning_agent(llm, chat_memory, query_memory)
+
+    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)
+        
+        # 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 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)
+    except Exception as e:
+        logger.error(f"Error in main: {str(e)}")
+        raise
+
+if __name__ == "__main__":
     main()