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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("π Document Conversation π€") | |
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']) | |