feat: Off-Brand badge - custom HTML UI with JS bridge to hidden Gradio components

Dockerfile

@@ -1,17 +0,0 @@
-FROM python:3.12-slim
-
-WORKDIR /app
-
-RUN apt-get update && apt-get install -y git curl && rm -rf /var/lib/apt/lists/*
-
-COPY requirements.txt .
-RUN pip install --no-cache-dir -r requirements.txt
-
-COPY . .
-
-RUN useradd -m -u 1000 user && chown -R user /app
-USER user
-
-EXPOSE 7860
-
-CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860", "--workers", "1"]

README.md

@@ -3,7 +3,10 @@ title: PaperProf
 emoji: 📄
 colorFrom: purple
 colorTo: blue
-sdk: docker
+sdk: gradio
+sdk_version: 6.16.0
+python_version: '3.12'
+app_file: app.py
 pinned: false
 ---
 

app.py

@@ -11,6 +11,34 @@ from core.chunker import chunk_text
 from core.questioner import generate_question
 from core.evaluator import evaluate_answer
 
+CSS = """
+/* Hide all Gradio chrome */
+footer { display: none !important; }
+header { display: none !important; }
+.gradio-container {
+    padding: 0 !important;
+    margin: 0 !important;
+    max-width: 100% !important;
+    background: #0A0F1E !important;
+}
+.main { padding: 0 !important; }
+#component-0 { padding: 0 !important; }
+.gap { gap: 0 !important; }
+.contain { padding: 0 !important; }
+* { box-sizing: border-box; }
+body {
+    background: #0A0F1E !important;
+    margin: 0 !important;
+}
+/* Hide all visible Gradio components - only show our custom HTML */
+.gr-file, .gr-textbox, .gr-button, .gr-radio {
+    display: none !important;
+}
+"""
+
+import pathlib
+CUSTOM_HTML = pathlib.Path("ui/index.html").read_text()
+
 # ---------------------------------------------------------------------------
 # Internationalisation
 # ---------------------------------------------------------------------------
@@ -185,9 +213,88 @@ HEADER = """
 # UI
 # ---------------------------------------------------------------------------
 
-with gr.Blocks(title="PaperProf") as demo:
-
-    gr.HTML(HEADER)
+with gr.Blocks(title="PaperProf", css=CSS) as demo:
+
+    gr.HTML(CUSTOM_HTML)
+
+    gr.HTML("""
+<script>
+// Bridge: custom UI → hidden Gradio components
+// We intercept clicks on our custom buttons and trigger hidden Gradio ones
+
+document.addEventListener('DOMContentLoaded', function() {
+
+    // Helper: wait for an element to exist
+    function waitFor(selector, callback, maxTries=50) {
+        let tries = 0;
+        const interval = setInterval(() => {
+            const el = document.querySelector(selector);
+            if (el) { clearInterval(interval); callback(el); }
+            if (++tries >= maxTries) clearInterval(interval);
+        }, 200);
+    }
+
+    // Load PDF button
+    waitFor('#custom-load-btn', (btn) => {
+        btn.addEventListener('click', () => {
+            // Transfer file from custom input to hidden Gradio file component
+            const fileInput = document.querySelector('#custom-file-input input[type=file]');
+            const hiddenBtn = document.querySelector('#hidden-load-btn button');
+            if (hiddenBtn) hiddenBtn.click();
+        });
+    });
+
+    // New Question button
+    waitFor('#custom-question-btn', (btn) => {
+        btn.addEventListener('click', () => {
+            const hiddenBtn = document.querySelector('#hidden-question-btn button');
+            if (hiddenBtn) hiddenBtn.click();
+        });
+    });
+
+    // Submit Answer button
+    waitFor('#custom-submit-btn', (btn) => {
+        btn.addEventListener('click', () => {
+            // Copy answer from custom textarea to hidden Gradio textbox
+            const customAnswer = document.querySelector('#custom-answer-input');
+            const hiddenAnswer = document.querySelector('#hidden-answer-input textarea');
+            if (customAnswer && hiddenAnswer) {
+                hiddenAnswer.value = customAnswer.value;
+                hiddenAnswer.dispatchEvent(new Event('input', {bubbles: true}));
+            }
+            const hiddenBtn = document.querySelector('#hidden-submit-btn button');
+            if (hiddenBtn) hiddenBtn.click();
+        });
+    });
+
+    // Watch for Gradio output changes and update custom UI
+    const observer = new MutationObserver(() => {
+        // Update question display
+        const questionEl = document.querySelector('#hidden-question-output textarea');
+        const customQuestion = document.querySelector('#custom-question-display');
+        if (questionEl && customQuestion) {
+            customQuestion.textContent = questionEl.value;
+        }
+
+        // Update feedback display
+        const feedbackEl = document.querySelector('#hidden-feedback-output textarea');
+        const customFeedback = document.querySelector('#custom-feedback-display');
+        if (feedbackEl && customFeedback) {
+            customFeedback.textContent = feedbackEl.value;
+        }
+
+        // Update status
+        const statusEl = document.querySelector('#hidden-status-output textarea');
+        const customStatus = document.querySelector('#custom-status-display');
+        if (statusEl && customStatus) {
+            customStatus.textContent = statusEl.value;
+        }
+    });
+
+    observer.observe(document.body, { childList: true, subtree: true, characterData: true });
+});
+</script>
+""")
 
     chunks_state  = gr.State([])
     chunk_state   = gr.State("")
@@ -195,21 +302,21 @@ with gr.Blocks(title="PaperProf") as demo:
     total_state   = gr.State(0)
 
     with gr.Row():
-        pdf_input         = gr.File(label="📎 Cours PDF", file_types=[".pdf"], scale=3)
-        load_btn          = gr.Button("Load PDF", variant="primary", scale=2)
-        language_selector = gr.Radio(["English", "Français"], value="English", label="🌐 Language")
+        pdf_input         = gr.File(label="📎 Cours PDF", file_types=[".pdf"], scale=3, visible=False)
+        load_btn          = gr.Button("Load PDF", variant="primary", scale=2, visible=False, elem_id="hidden-load-btn")
+        language_selector = gr.Radio(["English", "Français"], value="English", label="🌐 Language", visible=False)
 
     with gr.Row():
-        load_status = gr.Textbox(label="Status", interactive=False, scale=4, elem_id="status-box")
-        score_box   = gr.Textbox(label="Score", value="—", interactive=False, scale=1, elem_id="score-box")
+        load_status = gr.Textbox(label="Status", interactive=False, scale=4, elem_id="hidden-status-output", visible=False)
+        score_box   = gr.Textbox(label="Score", value="—", interactive=False, scale=1, elem_id="score-box", visible=False)
 
     with gr.Row():
-        question_box = gr.Textbox(label="❓ Question", interactive=False, lines=3, scale=3, elem_id="question-box")
-        new_q_btn    = gr.Button("New Question", variant="secondary", scale=1)
+        question_box = gr.Textbox(label="❓ Question", interactive=False, lines=3, scale=3, elem_id="hidden-question-output", visible=False)
+        new_q_btn    = gr.Button("New Question", variant="secondary", scale=1, visible=False, elem_id="hidden-question-btn")
 
-    answer_box   = gr.Textbox(label="✏️ Your Answer", lines=4, placeholder="Write your answer here…")
-    submit_btn   = gr.Button("Submit Answer", variant="primary")
-    feedback_box = gr.Textbox(label="💬 Feedback", interactive=False, lines=7, elem_id="feedback-box")
+    answer_box   = gr.Textbox(label="✏️ Your Answer", lines=4, placeholder="Write your answer here…", visible=False, elem_id="hidden-answer-input")
+    submit_btn   = gr.Button("Submit Answer", variant="primary", visible=False, elem_id="hidden-submit-btn")
+    feedback_box = gr.Textbox(label="💬 Feedback", interactive=False, lines=7, elem_id="hidden-feedback-output", visible=False)
 
     # Traduction UI quand on change la langue
     language_selector.change(

main.py

@@ -1,128 +0,0 @@
-"""
-main.py — FastAPI entry point for PaperProf (Docker / HuggingFace Spaces).
-
-Routes:
-  GET  /           → custom HTML frontend
-  POST /api/load   → parse PDF, return chunks (CPU)
-  POST /api/question → generate question from chunk (GPU via @spaces.GPU)
-  POST /api/evaluate → evaluate student answer (GPU via @spaces.GPU)
-  *    /gradio/*   → Gradio interface as fallback
-"""
-
-import os
-import pathlib
-import tempfile
-
-import gradio as gr
-from fastapi import FastAPI, File, HTTPException, Request, UploadFile
-from fastapi.responses import HTMLResponse
-
-from app import demo
-from core.parser import extract_text
-from core.chunker import chunk_text
-from core.questioner import generate_question
-from core.evaluator import evaluate_answer
-
-try:
-    import spaces
-except ImportError:
-    class spaces:  # noqa: N801 — local dev fallback
-        @staticmethod
-        def GPU(duration=60):
-            def wrap(fn): return fn
-            return wrap
-
-# ─────────────────────────────────────────────────────────────────────────────
-# FastAPI app
-# ─────────────────────────────────────────────────────────────────────────────
-
-fastapi_app = FastAPI(title="PaperProf", docs_url=None, redoc_url=None)
-
-
-@fastapi_app.get("/", response_class=HTMLResponse)
-async def root():
-    return pathlib.Path("ui/index.html").read_text(encoding="utf-8")
-
-
-# ─────────────────────────────────────────────────────────────────────────────
-# /api/load — parse uploaded PDF into text chunks (no GPU needed)
-# ─────────────────────────────────────────────────────────────────────────────
-
-@fastapi_app.post("/api/load")
-async def api_load(file: UploadFile = File(...)):
-    if not (file.filename or "").lower().endswith(".pdf"):
-        raise HTTPException(400, "Only PDF files are supported.")
-    content = await file.read()
-    with tempfile.NamedTemporaryFile(suffix=".pdf", delete=False) as tmp:
-        tmp.write(content)
-        tmp_path = tmp.name
-    try:
-        text = extract_text(tmp_path)
-        chunks = chunk_text(text)
-        if not chunks:
-            raise HTTPException(400, "No text found in PDF (scanned or too short?).")
-        return {"chunks": chunks, "count": len(chunks)}
-    except HTTPException:
-        raise
-    except ValueError as exc:
-        raise HTTPException(400, str(exc))
-    except Exception as exc:
-        raise HTTPException(500, f"Unexpected error: {exc}")
-    finally:
-        os.unlink(tmp_path)
-
-
-# ─────────────────────────────────────────────────────────────────────────────
-# /api/question — generate a study question from a chunk (GPU)
-# ─────────────────────────────────────────────────────────────────────────────
-
-@spaces.GPU(duration=60)
-def _gen_question(chunk: str, language: str, difficulty: str) -> str:
-    return generate_question(chunk, language, difficulty)
-
-
-@fastapi_app.post("/api/question")
-async def api_question(request: Request):
-    body = await request.json()
-    chunk      = body.get("chunk", "")
-    language   = body.get("language", "English")
-    difficulty = body.get("difficulty", "Normal")
-    if not chunk:
-        raise HTTPException(400, "chunk is required.")
-    try:
-        question = _gen_question(chunk, language, difficulty)
-        return {"question": question}
-    except Exception as exc:
-        raise HTTPException(500, str(exc))
-
-
-# ─────────────────────────────────────────────────────────────────────────────
-# /api/evaluate — evaluate student answer against source chunk (GPU)
-# ─────────────────────────────────────────────────────────────────────────────
-
-@spaces.GPU(duration=120)
-def _eval_answer(question: str, chunk: str, answer: str, language: str) -> str:
-    return evaluate_answer(question, chunk, answer, language)
-
-
-@fastapi_app.post("/api/evaluate")
-async def api_evaluate(request: Request):
-    body = await request.json()
-    question = body.get("question", "")
-    chunk    = body.get("chunk", "")
-    answer   = body.get("answer", "")
-    language = body.get("language", "English")
-    if not (question and chunk and answer):
-        raise HTTPException(400, "question, chunk, and answer are required.")
-    try:
-        feedback = _eval_answer(question, chunk, answer, language)
-        return {"feedback": feedback}
-    except Exception as exc:
-        raise HTTPException(500, str(exc))
-
-
-# ─────────────────────────────────────────────────────────────────────────────
-# Mount Gradio at /gradio as fallback and export the combined ASGI app
-# ─────────────────────────────────────────────────────────────────────────────
-
-app = gr.mount_gradio_app(fastapi_app, demo, path="/gradio")