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import gradio as gr
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)
# Function to generate responses
def generate_response(user_input):
# response = conversation.run(user_input)
return user_input
# Define additional inputs and examples if needed
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,
chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"),
additional_inputs=additional_inputs,
examples=example1,
cache_examples=False,
)
# Launch the app
interface.launch()
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