Update app.py

app.py

@@ -63,7 +63,8 @@ demo = gr.ChatInterface(
 
 if __name__ == "__main__":
     demo.launch()
-'''
+
+    
 
 import gradio as gr
 from langchain.chains import LLMChain
@@ -131,3 +132,69 @@ with gr.Blocks() as demo:
 
 # Launch the Gradio application
 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, END, START
+
+# Define the LLM models
+llm1 = HuggingFaceEndpoint(model='t5-small')
+llm2 = HuggingFaceEndpoint(model='t5-large')
+
+# Define the agent functions
+def agent1(response):
+    return f"Agent 1: {response}"
+
+def agent2(response):
+    return f"Agent 2: {response}"
+
+# Define the prompts and 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}}"))
+
+# State definitions
+s1 = StateGraph("s1")
+s2 = StateGraph("s2")
+
+# Create transitions in the states
+s1.add_edge(s2, 'next')  # From state s1 to s2
+s2.add_edge(s1, 'back')   # From state s2 to s1
+
+# Initialize the current state
+current_state = s1
+
+def handle_input(query):
+    global current_state
+    output = ''
+    
+    if current_state == s1:
+        # Use LLM Chain in s1
+        output = chain1.invoke(input=query)  # Invoke chain1 with the user input
+        response = agent1(output)  # Process output through Agent 1
+        current_state = s2  # Transition to state s2
+    elif current_state == s2:
+        # Use LLM Chain in s2
+        output = chain2.invoke(input=query)  # Invoke chain2 with the user input
+        response = agent2(output)  # Process output through Agent 2
+        current_state = s1  # Transition back to state s1
+
+    return response
+
+# Create the Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Chatbot Interface")
+    chatbot_interface = gr.Chatbot()
+    user_input = gr.Textbox(label="Your Message", placeholder="Type something here...")
+    submit_btn = gr.Button("Send")
+
+    # Define the behavior of the submit button
+    submit_btn.click(
+        fn=lambda input_text: handle_input(input_text),  # Handle user input
+        inputs=[user_input],
+        outputs=chatbot_interface
+    )
+
+# Launch the Gradio application
+demo.launch()