Update app.py

app.py

@@ -1,3 +1,5 @@
+import os
+import random
 import gradio as gr
 from langchain.chains import LLMChain
 from langchain_core.prompts import (
@@ -20,10 +22,40 @@ from langchain_groq import ChatGroq
 
 # Function to generate responses
 def generate_response(user_input, history, locale, model, temperature, max_tokens, top_p, seed):
-    # response = conversation.run(user_input)
-    print("user_input", user_input)
-    print("history", history)
-    return user_input
+
+    # Initialize Groq Langchain chat object and conversation
+    groq_chat = ChatGroq(
+            groq_api_key=os.environ.get("GROQ_API_KEY")
+            model_name=model
+    )
+
+    # Manages the chat history, ensuring the AI remembers the specified number of history messages, in this case 5.
+    memory = ConversationBufferWindowMemory(k=5, memory_key="chat_history", return_messages=True)
+
+    prompt = ChatPromptTemplate.from_messages(
+        [
+            # This is the persistent system prompt, sets the initial context for the AI.
+            SystemMessage(content='You are a helpful AI assistant.'),
+            # This placeholder will take care of chat history.
+            MessagesPlaceholder(variable_name="chat_history"),
+            # This template is where the user's current input will be injected into the prompt.
+            HumanMessagePromptTemplate.from_template("{human_input}"),
+        ]
+    )    
+
+    # Create a conversation sequence using RunnableSequence
+    conversation = prompt | groq_chat
+
+    # Load chat_history
+    chat_history = memory.load_memory_variables({})["chat_history"]
+    
+    # The chatbot's answer is generated by sending the full prompt to the LLM
+    response = conversation.invoke({"human_input": user_question, "chat_history": chat_history})
+
+    # Update the memory with the new interaction
+    memory.save_context({"input": user_question}, {"output": response.content})
+.   
+    return response.content
 
 # Define additional inputs and examples if needed
 additional_inputs = [