Update app.py

app.py

@@ -1,28 +1,42 @@
 import gradio as gr
-from langchain.chains import ConversationChain
-from langchain.memory import ConversationBufferMemory
-from langchain.llms import Groq
+from langchain.chains import LLMChain
+from langchain_core.prompts import (
+    ChatPromptTemplate,
+    HumanMessagePromptTemplate,
+    MessagesPlaceholder,
+)
+from langchain_core.messages import SystemMessage
+from langchain.chains.conversation.memory import ConversationBufferWindowMemory
+from langchain_groq import ChatGroq
+
 
-# Initialize the language model and memory
-llm = Groq(api_key="your_groq_api_key")
-memory = ConversationBufferMemory()
 
-# Define the conversation chain
-conversation = ConversationChain(llm=llm, memory=memory)
+# # Initialize the language model and memory
+# llm = Groq(api_key="your_groq_api_key")
+# memory = ConversationBufferMemory()
+
+# # Define the conversation chain
+# conversation = ConversationChain(llm=llm, memory=memory)
 
 # Function to generate responses
 def generate_response(user_input):
-    response = conversation.run(user_input)
-    return response
+    # response = conversation.run(user_input)
+    return user_input
 
 # Define additional inputs and examples if needed
-additional_inputs = []
-example1 = []
+additional_inputs = [
+    gr.Dropdown(choices=["llama-3.1-70b-versatile", "llama-3.1-8b-instant", "llama3-70b-8192", "llama3-8b-8192", "mixtral-8x7b-32768", "gemma2-9b-it", "gemma-7b-it"], value="llama-3.1-70b-versatile", label="Model"),
+    gr.Slider(minimum=0.0, maximum=1.0, step=0.01, value=0.5, label="Temperature", info="Controls diversity of the generated text. Lower is more deterministic, higher is more creative."),
+    gr.Slider(minimum=1, maximum=8000, step=1, value=8000, label="Max Tokens", info="The maximum number of tokens that the model can process in a single response.<br>Maximums: 8k for gemma 7b it, gemma2 9b it, llama 7b & 70b, 32k for mixtral 8x7b, 132k for llama 3.1."),
+    gr.Slider(minimum=0.0, maximum=1.0, step=0.01, value=0.5, label="Top P", info="A method of text generation where a model will only consider the most probable next tokens that make up the probability p."),
+    gr.Number(precision=0, value=0, label="Seed", info="A starting point to initiate generation, use 0 for random")
+]
+
+example1 = [[Who are you?]]
 
 # Create the Gradio interface
 interface = gr.ChatInterface(
     fn=generate_response, 
-    theme="Nymbo/Alyx_Theme",
     chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"),
     additional_inputs=additional_inputs,
     examples=example1,