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| import streamlit as st | |
| import pandas as pd | |
| import os | |
| from utils import save_json, load_json | |
| from markdown import markdown | |
| from utils import load_json | |
| from autoviz import AutoViz_Class | |
| import base64 | |
| from google.cloud import aiplatform | |
| import base64 | |
| import vertexai | |
| from vertexai.preview.generative_models import GenerativeModel, Part | |
| #setup cloud | |
| aiplatform.init( | |
| project = "ultra-heading-407815", | |
| location="us-central1" | |
| ) | |
| os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "credentials.json" | |
| dataframe = None | |
| st.title("GemInsights") | |
| file = st.file_uploader( | |
| "Pick a dataframe", type=["csv", "xlsx"], accept_multiple_files=False | |
| ) | |
| if file is not None: | |
| _, extension = os.path.splitext(file.name) | |
| if extension == ".csv": | |
| dataframe = pd.read_csv(file) | |
| else: | |
| dataframe = pd.read_excel(file) | |
| st.write(dataframe.head()) | |
| st.write(f"updated a dataframe with shape {dataframe.shape}") | |
| if file is not None: | |
| text_input = st.text_input( | |
| "Enter something about the data 👇", | |
| label_visibility="visible", | |
| disabled=False, | |
| placeholder="eg:- This is a sales dataframe", | |
| ) | |
| option = st.selectbox( | |
| "Which is the target column?", | |
| tuple(list(dataframe.columns)), | |
| index=None, | |
| placeholder="Select one column in here", | |
| ) | |
| def plot(dataframe, target): | |
| AV = AutoViz_Class() | |
| dft = AV.AutoViz( | |
| "", | |
| sep=",", | |
| depVar=target, | |
| dfte=dataframe, | |
| header=0, | |
| verbose=2, | |
| lowess=False, | |
| chart_format="jpg", | |
| max_rows_analyzed=500, | |
| max_cols_analyzed=20, | |
| save_plot_dir="plots", | |
| ) | |
| def prompt_make(dataframe, target, info): | |
| images = [] | |
| image_dir = f"plots/{target}" | |
| image_files = os.listdir(image_dir) | |
| for image_file in image_files: | |
| image_path = os.path.join(image_dir, image_file) | |
| img = open(image_path, "rb").read() | |
| img_bytes = Part.from_data( | |
| base64.b64decode(base64.encodebytes(img)), mime_type="image/jpeg" | |
| ) | |
| images.append(img_bytes) | |
| with open("prompt.txt", "rb") as file: | |
| data = file.read() | |
| prompt = f"{data}\n Here are some of the informations related to the dataset - '{info}'" | |
| # print(f"{prompt}") | |
| # print(images) | |
| return prompt, images | |
| def generate_res(prompt, images): | |
| print("prompting ...") | |
| model = GenerativeModel("gemini-pro-vision") | |
| responses = model.generate_content( | |
| [prompt]+images, | |
| generation_config={ | |
| "max_output_tokens": 2048, | |
| "temperature": 0.4, | |
| "top_p": 1, | |
| "top_k": 32 | |
| }, | |
| ) | |
| return responses.text | |
| def generate(dataframe, text_input, option): | |
| plot(dataframe, option) | |
| prompt, images = prompt_make(dataframe, option, text_input) | |
| res = generate_res(prompt, images) | |
| return res | |
| if st.button("Get Insights", type="primary"): | |
| st.write("Visualising the data into various plots 📊...") | |
| st.write("Generating insights from the visualization of the data 💡...") | |
| # running the pipeline | |
| response = generate(dataframe, text_input, option) | |
| res = markdown(response) | |
| st.markdown(res, unsafe_allow_html=True) | |
| else: | |
| st.write("") | |