Create app.py

app.py

@@ -0,0 +1,35 @@
+import pandas_profiling as pp
+from huggingface_hub.hf_api import create_repo
+from huggingface_hub.repository import Repository
+import gradio as gr
+import pandas as pd
+import tempfile
+
+token = gr.Textbox(label = "Your Hugging Face Token")
+username = gr.Textbox(label = "Your Hugging Face User name")
+dataset_name = gr.Textbox(label = "Dataset Name")
+dataset = gr.File(label = "Dataset")
+output_text = gr.Textbox(label = "Status")
+title = "Dataset Profiler 🪄✨"
+description = "Drag and drop any dataset you want to get a detailed profile on, and this Space will profile and push it to your Hub profile as a new Space. 🌟✨"
+
+def profile_dataset(dataset, username, token, dataset_name):
+
+    df = pd.read_csv(dataset.name)
+    profile = pp.ProfileReport(df, title=f"{dataset_name} Report")
+    
+    url = create_repo(f"{username}/{dataset_name}", repo_type = "space", token = token, space_sdk = "static")
+    repo = Repository(
+        local_dir = f"{username}/{dataset_name}",
+        clone_from=url,
+        use_auth_token=token,
+        repo_type = "space"
+    )
+    repo.git_pull(rebase=True)
+    profile.to_file(f"{username}/{dataset_name}/index.html")
+    repo.git_add()
+    repo.git_commit(commit_message = "Dataset report")
+    repo.git_push()
+    return f"Your dataset report will be ready at {url}"
+
+gr.Interface(profile_dataset, inputs = [dataset, username, token, dataset_name], outputs=[output_text], enable_queue = True).launch(debug=True)