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import gradio as gr |
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import os |
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import pandas as pd |
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from datasets import load_dataset |
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from kaggle.api.kaggle_api_extended import KaggleApi |
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api = KaggleApi() |
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try: |
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api.authenticate() |
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print("Kaggle authentication successful.") |
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except Exception as e: |
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print(f"Kaggle authentication failed: {e}") |
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exit(1) |
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kaggle_dataset_path = "<dataset-path>" |
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try: |
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os.system(f"kaggle datasets download -d {kaggle_dataset_path}") |
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print(f"Dataset {kaggle_dataset_path} downloaded successfully.") |
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except Exception as e: |
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print(f"Failed to download Kaggle dataset: {e}") |
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exit(1) |
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dataset_name = "<dataset-name>" |
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try: |
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os.system(f"unzip ./{dataset_name}.zip -d ./data/") |
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print(f"Dataset {dataset_name} extracted successfully.") |
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except Exception as e: |
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print(f"Failed to extract dataset: {e}") |
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exit(1) |
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hf_dataset_name = 'dataset_name' |
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try: |
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hf_dataset = load_dataset(hf_dataset_name) |
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hf_df = pd.DataFrame(hf_dataset['train']) |
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print(f"Hugging Face dataset {hf_dataset_name} loaded successfully.") |
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except Exception as e: |
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print(f"Failed to load Hugging Face dataset: {e}") |
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exit(1) |
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kaggle_df_path = './data/kaggle_dataset.csv' |
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try: |
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kaggle_df = pd.read_csv(kaggle_df_path) |
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print(f"Kaggle dataset loaded from {kaggle_df_path}.") |
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except Exception as e: |
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print(f"Failed to load Kaggle dataset: {e}") |
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exit(1) |
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merged_df = pd.concat([hf_df, kaggle_df], ignore_index=True) |
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def display_data(): |
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return merged_df.head() |
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iface = gr.Interface(fn=display_data, inputs=[], outputs="dataframe") |
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iface.launch() |
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