Spaces:
Sleeping
Sleeping
File size: 2,311 Bytes
eceebf5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 |
import streamlit as st
def main():
st.title("Model Selection and Configuration")
# Introduction
st.write("Select the embedding model and the large language model (LLM) for processing.")
# Embedding Model Selection
embedding_models = ["thenlper/gte-small", "sentence-transformers/all-MiniLM-L6-v2", "other"]
selected_embedding_model = st.selectbox("Select Embedding Model", options=embedding_models)
# LLM Model Selection
llm_models = ["mistralai/Mistral-7B-Instruct-v0.2", "gpt-3.5-turbo", "other"]
selected_llm_model = st.selectbox("Select LLM Model", options=llm_models)
# Display selections (for demonstration)
st.write("Selected Embedding Model:", selected_embedding_model)
st.write("Selected LLM Model:", selected_llm_model)
# Configuration options for the selected models
st.header("Model Configuration")
# Embedding Model Configuration (example)
if selected_embedding_model == "thenlper/gte-small":
# Placeholder for model-specific configuration options
st.write("No additional configuration required for this model.")
else:
# Configuration for other models
st.write("Configuration options for other models will appear here.")
# LLM Model Configuration (example)
if selected_llm_model == "mistralai/Mistral-7B-Instruct-v0.2":
max_tokens = st.slider("Max Tokens", min_value=100, max_value=1000, value=250)
temperature = st.slider("Temperature", min_value=0.0, max_value=1.0, value=0.7, step=0.01)
else:
# Configuration for other models
st.write("Configuration options for other models will appear here.")
# Save model selections and configurations
if st.button("Save Model Configuration"):
st.session_state['selected_embedding_model'] = selected_embedding_model
st.session_state['selected_llm_model'] = selected_llm_model
# Assuming configurations are more complex and vary per model, you might want to store them differently
st.session_state['llm_model_config'] = {"max_tokens": max_tokens, "temperature": temperature}
st.success("Model configurations saved.")
# Optional: Proceed to the next step
# st.session_state.page = 'processing_embedding'
if __name__ == "__main__":
main()
|