import streamlit as st from lida import Manager, TextGenerationConfig , llm from dotenv import load_dotenv import os import openai from PIL import Image from io import BytesIO import base64 from accelerate import disk_offload load_dotenv() openai.api_key = os.getenv('OPENAI_API_KEY') def base64_to_image(base64_string): # Decode the base64 string byte_data = base64.b64decode(base64_string) # Use BytesIO to convert the byte data to image return Image.open(BytesIO(byte_data)) lida = Manager(text_gen = llm(provider="hf", model="uukuguy/speechless-llama2-hermes-orca-platypus-13b", device_map="auto")) disk_offload(model=lida, offload_dir="offload") textgen_config = TextGenerationConfig(n=1, temperature=0.5, use_cache=True) menu = st.sidebar.selectbox("Choose an Option", ["Summarize", "Question based Graph"]) if menu == "Summarize": st.subheader("Summarization of your Data") file_uploader = st.file_uploader("Upload your CSV", type="csv") if file_uploader is not None: path_to_save = "filename.csv" with open(path_to_save, "wb") as f: f.write(file_uploader.getvalue()) summary = lida.summarize("filename.csv", summary_method="default", textgen_config=textgen_config) st.write(summary) goals = lida.goals(summary, n=2, textgen_config=textgen_config) for goal in goals: st.write(goal) i = 0 library = "seaborn" textgen_config = TextGenerationConfig(n=1, temperature=0.2, use_cache=True) charts = lida.visualize(summary=summary, goal=goals[i], textgen_config=textgen_config, library=library) img_base64_string = charts[0].raster img = base64_to_image(img_base64_string) st.image(img) elif menu == "Question based Graph": st.subheader("Query your Data to Generate Graph") file_uploader = st.file_uploader("Upload your CSV", type="csv") if file_uploader is not None: path_to_save = "filename1.csv" with open(path_to_save, "wb") as f: f.write(file_uploader.getvalue()) text_area = st.text_area("Query your Data to Generate Graph", height=200) if st.button("Generate Graph"): if len(text_area) > 0: st.info("Your Query: " + text_area) lida = Manager(text_gen = llm("openai")) textgen_config = TextGenerationConfig(n=1, temperature=0.2, use_cache=True) summary = lida.summarize("filename1.csv", summary_method="default", textgen_config=textgen_config) user_query = text_area charts = lida.visualize(summary=summary, goal=user_query, textgen_config=textgen_config) charts[0] image_base64 = charts[0].raster img = base64_to_image(image_base64) st.image(img)