app.py

import gradio as gr
import os
import wikipedia
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 tools
def create_your_own(query: str) -> str:
    """This function can do whatever you would like once you fill it in"""
    return query[::-1]

def get_current_temperature(query: str) -> str:
    return "It's sunny and 75°F."

def search_wikipedia(query: str) -> str:
    try:
        summary = wikipedia.summary(query, sentences=2)
        return summary
    except wikipedia.exceptions.DisambiguationError as e:
        return f"Multiple results found: {', '.join(e.options[:5])}"
    except wikipedia.exceptions.PageError:
        return "No relevant Wikipedia page found."

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 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", ai_prefix="Assistant")
        self.prompt = ChatPromptTemplate.from_messages([
            ("system", "You are a helpful but sassy assistant. Remember what the user tells you in the conversation."),
            MessagesPlaceholder(variable_name="chat_history"),
            ("user", "{input}"),
            MessagesPlaceholder(variable_name="agent_scratchpad")
        ])
        self.chain = initialize_agent(
            tools=tools,
            llm=self.model,
            agent="zero-shot-react-description",
            verbose=True,
            memory=self.memory
        )
    
    def convchain(self, query):
        if not query:
            return "Please enter a query."
        try:
            result = self.chain.invoke({"input": query})
            response = result.get("output", "No response generated.")
            self.memory.save_context({"input": query}, {"output": response})
            print("Agent Execution Result:", response)  # Debugging output
            return response
        except Exception as e:
            print("Execution Error:", str(e))
            return f"Error: {str(e)}"

# Create chatbot instance
cb = cbfs(tools)

def process_query(query):
    return cb.convchain(query)

# 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)