Implement Hugging Face OAuth login requirement; update README for usage instructions

README.md

@@ -7,6 +7,7 @@ sdk: gradio
 sdk_version: 5.44.1
 app_file: app.py
 pinned: false
+hf_oauth: true
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
@@ -27,3 +28,4 @@ This app uses LangChain with the Hugging Face Inference API to generate QA datas
 
 - Uses HF Inference API via LangChain; no local `transformers` needed.
 - Output files are saved to `outputs/` as JSON and JSONL.
+ - Login: the app requires Hugging Face login by default on Spaces. Set env `REQUIRE_LOGIN=0` to disable (useful for local runs). A Login button is shown at the top; Generate is enabled after login.

app.py

@@ -7,8 +7,12 @@ from typing import List, Dict, Any, Tuple
 
 import gradio as gr
 
-# Require login to use the app (default true). Set REQUIRE_LOGIN=0 to disable.
-REQUIRE_LOGIN = os.getenv("REQUIRE_LOGIN", "1").strip().lower() in ("1", "true", "yes", "y")
+# Detect if OAuth is available (enabled on Spaces when hf_oauth: true)
+OAUTH_AVAILABLE = bool(os.getenv("OAUTH_CLIENT_ID"))
+
+# Require login to use the app. Defaults to on only when OAuth is available.
+_default_require = "1" if OAUTH_AVAILABLE else "0"
+REQUIRE_LOGIN = os.getenv("REQUIRE_LOGIN", _default_require).strip().lower() in ("1", "true", "yes", "y")
 
 try:
     from pypdf import PdfReader
@@ -238,15 +242,23 @@ with gr.Blocks(title="AutoGDataset - PDF to QA Dataset (LangChain)") as demo:
     Choose one of the preset models or provide a custom repo id. Provide a valid `HF_TOKEN` if required by the model.
     """)
 
-    # Login requirement (Hugging Face OAuth via Gradio LoginButton)
+    # Login requirement (Hugging Face OAuth via Gradio LoginButton when available)
     user_state = gr.State(value=None)
+    effective_require_login = bool(REQUIRE_LOGIN and OAUTH_AVAILABLE)
     with gr.Row():
         login_info = gr.Markdown(
-            value=("Please login with your Hugging Face account to use the app." if REQUIRE_LOGIN else "Login is optional; proceed or login for personalization."),
+            value=(
+                "Please login with your Hugging Face account to use the app."
+                if effective_require_login
+                else (
+                    "Login is optional." if OAUTH_AVAILABLE else "OAuth login not configured on this deployment."
+                )
+            ),
             elem_id="login-info",
         )
-    with gr.Row():
-        login_btn = gr.LoginButton(value="Login with Hugging Face")
+    if OAUTH_AVAILABLE:
+        with gr.Row():
+            login_btn = gr.LoginButton(value="Login with Hugging Face")
 
     with gr.Row():
         pdf_files = gr.File(label="Upload PDF(s)", file_count="multiple", file_types=[".pdf"])
@@ -271,7 +283,7 @@ with gr.Blocks(title="AutoGDataset - PDF to QA Dataset (LangChain)") as demo:
             max_pairs = gr.Slider(1, 12, value=6, step=1, label="Max Pairs/Chunk")
         custom_instruction = gr.Textbox(label="Custom Instruction (optional)", lines=3, placeholder="Override default instruction. Must ask for a pure JSON array of {question, answer}.")
 
-    generate_btn = gr.Button("Generate Dataset", variant="primary", interactive=(not REQUIRE_LOGIN))
+    generate_btn = gr.Button("Generate Dataset", variant="primary", interactive=(not effective_require_login))
 
     with gr.Row():
         status = gr.Markdown()
@@ -301,23 +313,25 @@ with gr.Blocks(title="AutoGDataset - PDF to QA Dataset (LangChain)") as demo:
         api_name="generate",
     )
 
-    def _on_login(user):
-        try:
-            username = None
-            if isinstance(user, dict):
-                username = user.get("username") or user.get("name")
-            if not username and hasattr(user, "username"):
-                username = getattr(user, "username")
-            msg = f"Logged in as @{username}" if username else "Logged in"
-        except Exception:
-            msg = "Logged in"
-        return user, gr.update(value=msg), gr.update(interactive=True)
-
-    # Enable Generate button after login and store user profile
-    if hasattr(login_btn, "login"):
-        login_btn.login(_on_login, inputs=None, outputs=[user_state, login_info, generate_btn])
-    else:
-        login_btn.click(_on_login, inputs=None, outputs=[user_state, login_info, generate_btn])
+    if OAUTH_AVAILABLE:
+        def _on_login(user):
+            try:
+                username = None
+                if isinstance(user, dict):
+                    username = user.get("username") or user.get("name")
+                if not username and hasattr(user, "username"):
+                    username = getattr(user, "username")
+                msg = f"Logged in as @{username}" if username else "Logged in"
+            except Exception:
+                msg = "Logged in"
+            return user, gr.update(value=msg), gr.update(interactive=True)
+
+        # Enable Generate button after login and store user profile
+        if hasattr(login_btn, "login"):
+            login_btn.login(_on_login, inputs=None, outputs=[user_state, login_info, generate_btn])
+        else:
+            # In local/dev without OAuth routing, clicking will mock-login
+            login_btn.click(lambda: ("local_user", gr.update(value="Logged in (local)"), gr.update(interactive=True)), inputs=None, outputs=[user_state, login_info, generate_btn])
 
 if __name__ == "__main__":
     # For local runs