Update app.py

app.py

@@ -1,35 +1,30 @@
-import os
 import gradio as gr
-from langchain.agents import initialize_agent
-#from langchain.chat_models import ChatOpenAI
+import os
 from langchain_community.chat_models import ChatOpenAI
 from langchain.memory import ConversationBufferMemory
+from langchain.agents import AgentExecutor, initialize_agent
 from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
 from langchain.tools import Tool
 
-# Define the tool
-
+# Define tools
 def create_your_own(query: str) -> str:
     """This function can do whatever you would like once you fill it in"""
     return query[::-1]
 
-# Define other tools (example placeholders for context)
 def get_current_temperature(query: str) -> str:
     return "It's sunny and 75°F."
 
 def search_wikipedia(query: str) -> str:
     return "Wikipedia search results for: " + query
 
-# Add the new tool to the list of available tools
 tools = [
     Tool(name="Temperature", func=get_current_temperature, description="Get current temperature"),
     Tool(name="Search Wikipedia", func=search_wikipedia, description="Search Wikipedia"),
     Tool(name="Create Your Own", func=create_your_own, description="Custom tool for processing input")
 ]
 
-# Define the cbfs class for handling the agent
+# Define chatbot class
 class cbfs:
-    
     def __init__(self, tools):
         self.model = ChatOpenAI(temperature=0, openai_api_key=os.getenv("OPENAI_API_KEY"))
         self.memory = ConversationBufferMemory(return_messages=True, memory_key="chat_history")
@@ -39,47 +34,39 @@ class cbfs:
             ("user", "{input}"),
             MessagesPlaceholder(variable_name="agent_scratchpad")
         ])
-        
         self.chain = initialize_agent(
-            tools=tools, 
-            llm=self.model, 
-            #agent="chat-conversational-react-description",
+            tools=tools,
+            llm=self.model,
             agent="zero-shot-react-description",
-            memory=self.memory,
-            verbose=True
+            verbose=True,
+            memory=self.memory
         )
     
-    #def convchain(self, query):
-        #if not query:
-            #return "Please enter a query."
-        #result = self.chain.invoke({"input": query})
-        #return result.get('output_text', "No response generated.")
-
     def convchain(self, query):
         if not query:
             return "Please enter a query."
-            try:
-                result = self.chain.invoke({"input": query})
-                print("Agent Result:", result)  # Debugging line
-                return result.get('output_text', "No response generated.")
-            except Exception as e:
-                return f"Error: {str(e)}"
+        try:
+            result = self.chain.invoke({"input": query})
+            print("Agent Execution Result:", result)  # Debugging output
+            return result.get("output", "No response generated.")
+        except Exception as e:
+            print("Execution Error:", str(e))
+            return f"Error: {str(e)}"
 
-# Create an instance of the agent
+# Create chatbot instance
 cb = cbfs(tools)
 
-# Create the Gradio interface
 def process_query(query):
     return cb.convchain(query)
 
-# Set up the Gradio interface
+# Define Gradio UI
 with gr.Blocks() as demo:
     with gr.Row():
         inp = gr.Textbox(placeholder="Enter text here…", label="User Input")
         output = gr.Textbox(placeholder="Response...", label="ChatBot Output", interactive=False)
-    
     inp.submit(process_query, inputs=inp, outputs=output)
 
 demo.launch(share=True)
 
 
+