Update app.py

app.py

@@ -1,3 +1,4 @@
+'''
 import gradio as gr
 from huggingface_hub import InferenceClient
 
@@ -62,3 +63,71 @@ demo = gr.ChatInterface(
 
 if __name__ == "__main__":
     demo.launch()
+'''
+
+import gradio as gr
+from langchain.chains import LLMChain
+from langchain.prompts import PromptTemplate
+from langchain_huggingface import HuggingFaceEndpoint
+from langgraph.graph import StateGraph
+
+# Define the LLM models
+llm1 = HuggingFaceEndpoint(model='t5-small')
+llm2 = HuggingFaceEndpoint(model='t5-large')
+
+# Define the agent functions
+def agent1(query):
+    return f"Agent 1: {query}"
+
+def agent2(query):
+    return f"Agent 2: {query}"
+
+# Define the states
+s1 = StateGraph("s1")
+s2 = StateGraph("s2")
+
+# Define the LLM chains
+chain1 = LLMChain(llm=llm1, prompt=PromptTemplate(input_variables=["query"], template="You are in state s1. {{query}}"))
+chain2 = LLMChain(llm=llm2, prompt=PromptTemplate(input_variables=["query"], template="You are in state s2. {{query}}"))
+
+# Define the transition functions
+def transition_s1(query):
+    output = chain1.invoke(query=query)
+    return agent1(output), s2
+
+def transition_s2(query):
+    output = chain2.invoke(query=query)
+    return agent2(output), s1
+
+# Define the respond function
+def respond(input, history, current_state):
+    if current_state == s1:
+        response, next_state = transition_s1(input)
+    elif current_state == s2:
+        response, next_state = transition_s2(input)
+    history.append((input, response))
+    return history, next_state
+
+# Create the Gradio interface
+current_state = s1  # Define current_state here
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Chatbot Interface")
+    chatbot_interface = gr.Chatbot()
+    user_input = gr.Textbox(label="Your Message", placeholder="Type something...")
+    submit_btn = gr.Button("Send")
+
+    # Define the behavior of the submit button
+    def submit_click(input, history):
+        global current_state  # Use global instead of nonlocal
+        history, current_state = respond(input, history, current_state)
+        return history
+    
+    submit_btn.click(
+        fn=submit_click,
+        inputs=[user_input, chatbot_interface],
+        outputs=chatbot_interface
+    )
+
+# Launch the Gradio application
+demo.launch()