Create app.py

app.py

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+import gradio as gr
+from langchain.agents import initialize_agent, AgentType
+from langchain.chat_models import ChatOpenAI
+from langchain.memory import ConversationBufferMemory
+from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain.agents import AgentExecutor
+from langchain.tools import Tool
+
+# Define the tool
+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
+class cbfs:
+    
+    def __init__(self, tools):
+        self.panels = []
+        self.functions = [tool.func for tool in tools]
+        self.model = ChatOpenAI(temperature=0)
+        self.memory = ConversationBufferMemory(return_messages=True, memory_key="chat_history")
+        self.prompt = ChatPromptTemplate.from_messages([
+            ("system", "You are helpful but sassy assistant"),
+            MessagesPlaceholder(variable_name="chat_history"),
+            ("user", "{input}"),
+            MessagesPlaceholder(variable_name="agent_scratchpad")
+        ])
+        self.chain = self.model.bind_tools(tools)  # Connecting the tools to the model
+        self.qa = AgentExecutor(agent=self.chain, tools=tools, verbose=False, memory=self.memory)
+    
+    def convchain(self, query):
+        if not query:
+            return "Please enter a query."
+        result = self.qa.invoke({"input": query})
+        self.answer = result['output'] 
+        return self.answer
+
+# Create an instance of the agent
+cb = cbfs(tools)
+
+# Create the Gradio interface
+def process_query(query):
+    return cb.convchain(query)
+
+# Set up the Gradio interface
+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)