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| import streamlit as st | |
| from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline | |
| import pandas as pd | |
| # Initialize the LLM from HuggingFace | |
| tokenizer = AutoTokenizer.from_pretrained("upstage/SOLAR-0-70b-16bit") | |
| model = AutoModelForCausalLM.from_pretrained("upstage/SOLAR-0-70b-16bit") | |
| pipe = pipeline("text-generation", model=model, tokenizer=tokenizer) | |
| st.title("📄 Document Conversation 🤖") | |
| uploaded_file = st.file_uploader("Upload a CSV file", type="csv") | |
| if uploaded_file is not None: | |
| df = pd.read_csv(uploaded_file) | |
| st.write(f"Loaded CSV with {df.shape[0]} rows and {df.shape[1]} columns.") | |
| st.write("Columns:", df.columns.tolist()) | |
| # Allow user to select columns to focus on | |
| selected_columns = st.multiselect("Select columns to focus on:", df.columns.tolist()) | |
| if selected_columns: | |
| st.write(df[selected_columns].head()) # Display first few rows of selected columns | |
| # Generate a textual representation of the selected data | |
| context = f"The selected data has columns: {', '.join(selected_columns)}. Here are the first few entries: {df[selected_columns].head().to_string(index=False)}" | |
| # Query through LLM | |
| question = st.text_input("Ask something about the selected data", placeholder="What is the average of ...?") | |
| if question: | |
| full_query = context + " " + question | |
| response = pipe(full_query, max_length=250, do_sample=True, top_k=50) | |
| st.write(response[0]['generated_text']) | |