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| 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() | |