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| import streamlit as st | |
| import pandas as pd | |
| import json | |
| import io | |
| from utils.dataset_utils import get_dataset_info, detect_dataset_format | |
| def render_dataset_uploader(): | |
| """ | |
| Renders the dataset upload component that supports CSV and JSON formats. | |
| """ | |
| st.markdown(""" | |
| <div class="upload-container"> | |
| <p>Upload your dataset in CSV or JSON format</p> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| # File uploader | |
| uploaded_file = st.file_uploader( | |
| "Choose a file", | |
| type=["csv", "json"], | |
| help="Upload a CSV or JSON file containing your dataset" | |
| ) | |
| # Sample dataset option | |
| st.markdown("Or use a sample dataset:") | |
| sample_dataset = st.selectbox( | |
| "Select a sample dataset", | |
| ["None", "Iris Dataset", "Titanic Dataset", "Boston Housing Dataset"] | |
| ) | |
| # Process uploaded file | |
| if uploaded_file is not None: | |
| try: | |
| # Check file extension | |
| file_extension = uploaded_file.name.split(".")[-1].lower() | |
| if file_extension == "csv": | |
| df = pd.read_csv(uploaded_file) | |
| dataset_type = "csv" | |
| elif file_extension == "json": | |
| # Try different JSON formats | |
| try: | |
| # First try parsing as a regular JSON with records orientation | |
| df = pd.read_json(uploaded_file) | |
| dataset_type = "json" | |
| except: | |
| # If that fails, try to parse as JSON Lines | |
| try: | |
| df = pd.read_json(uploaded_file, lines=True) | |
| dataset_type = "jsonl" | |
| except: | |
| # If that also fails, load raw JSON and convert | |
| content = json.loads(uploaded_file.getvalue().decode("utf-8")) | |
| if isinstance(content, list): | |
| df = pd.DataFrame(content) | |
| elif isinstance(content, dict): | |
| # Handle nested dict structures | |
| if any(isinstance(v, list) for v in content.values()): | |
| # Find the list field and use it | |
| for key, value in content.items(): | |
| if isinstance(value, list): | |
| df = pd.DataFrame(value) | |
| break | |
| else: | |
| # Flat dict or dict of dicts | |
| df = pd.DataFrame([content]) | |
| dataset_type = "json" | |
| else: | |
| st.error(f"Unsupported file format: {file_extension}") | |
| return | |
| # Store dataset and its info in session state | |
| st.session_state.dataset = df | |
| st.session_state.dataset_name = uploaded_file.name | |
| st.session_state.dataset_type = dataset_type | |
| st.session_state.dataset_info = get_dataset_info(df) | |
| except Exception as e: | |
| st.error(f"Error loading dataset: {str(e)}") | |
| # Process sample dataset | |
| elif sample_dataset != "None": | |
| try: | |
| if sample_dataset == "Iris Dataset": | |
| # Load Iris dataset | |
| from sklearn.datasets import load_iris | |
| iris = load_iris() | |
| df = pd.DataFrame(data=iris.data, columns=iris.feature_names) | |
| df['target'] = iris.target | |
| dataset_type = "csv" | |
| elif sample_dataset == "Titanic Dataset": | |
| # URL for Titanic dataset | |
| url = "https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv" | |
| df = pd.read_csv(url) | |
| dataset_type = "csv" | |
| elif sample_dataset == "Boston Housing Dataset": | |
| # Load Boston Housing dataset | |
| from sklearn.datasets import fetch_california_housing | |
| housing = fetch_california_housing() | |
| df = pd.DataFrame(data=housing.data, columns=housing.feature_names) | |
| df['target'] = housing.target | |
| dataset_type = "csv" | |
| # Store dataset and its info in session state | |
| st.session_state.dataset = df | |
| st.session_state.dataset_name = sample_dataset | |
| st.session_state.dataset_type = dataset_type | |
| st.session_state.dataset_info = get_dataset_info(df) | |
| except Exception as e: | |
| st.error(f"Error loading sample dataset: {str(e)}") | |