| import huggingface_hub
|
| if not hasattr(huggingface_hub, 'HfFolder'):
|
| class HfFolder:
|
| @staticmethod
|
| def get_token():
|
| return huggingface_hub.get_token()
|
| huggingface_hub.HfFolder = HfFolder
|
|
|
| import spaces
|
| import gradio as gr
|
| import torch
|
| import faiss
|
| import numpy as np
|
| import json
|
| import re
|
| from datasets import load_dataset
|
| from sentence_transformers import SentenceTransformer
|
| from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModelForSequenceClassification
|
|
|
| print("===============================================")
|
| print("🚀 BOOTING VIORRA TRIO FOR HUGGINGFACE ZEROGPU")
|
| print("===============================================\n")
|
|
|
|
|
| is_loaded = False
|
| dataset = None
|
| corpus_texts = []
|
| corpus_feedback = []
|
| embedder = None
|
| index = None
|
| aes_tokenizer = None
|
| aes_model = None
|
| llm_tokenizer = None
|
| llm = None
|
|
|
| def load_models_lazily():
|
| global is_loaded, dataset, corpus_texts, corpus_feedback, embedder, index
|
| global aes_tokenizer, aes_model, llm_tokenizer, llm
|
|
|
| if is_loaded:
|
| return
|
|
|
| print("--> [1/3] Loading Vector Database from published Viorra Dataset...")
|
| dataset = load_dataset('qsardor/viorra-admissions-essays', split='train')
|
|
|
| corpus_texts = dataset['Essay']
|
| corpus_feedback = dataset['Feedback_cleaned']
|
|
|
| embedder = SentenceTransformer('all-MiniLM-L6-v2', device="cpu")
|
| corpus_embeddings = embedder.encode(corpus_texts, convert_to_numpy=True, show_progress_bar=False)
|
| dimension = corpus_embeddings.shape[1]
|
| index = faiss.IndexFlatL2(dimension)
|
| index.add(corpus_embeddings)
|
|
|
| print("--> [2/3] Lazy Loading AES Scoring Model...")
|
| aes_model_id = "Kevintu/Engessay_grading_ML"
|
| aes_tokenizer = AutoTokenizer.from_pretrained(aes_model_id)
|
| aes_model = AutoModelForSequenceClassification.from_pretrained(aes_model_id)
|
|
|
| print("--> [3/3] Lazy Loading The Brain (google/gemma-4-E2B-it in float16)...")
|
| llm_id = "google/gemma-4-E2B-it"
|
| llm_tokenizer = AutoTokenizer.from_pretrained(llm_id, trust_remote_code=True)
|
| llm = AutoModelForCausalLM.from_pretrained(
|
| llm_id,
|
| torch_dtype=torch.float16,
|
| trust_remote_code=True
|
| ).eval()
|
|
|
| is_loaded = True
|
| print("\n✅ MODELS LOADED LAZILY ON FIRST RUN.")
|
|
|
| @spaces.GPU(duration=120)
|
| def analyze_essay(test_text, progress=gr.Progress()):
|
| if not test_text.strip():
|
| return "Please enter a personal statement to analyze.", gr.update(visible=True)
|
|
|
| progress(0.1, desc="Loading embedding model & vector DB...")
|
| load_models_lazily()
|
|
|
| word_count = len(test_text.split())
|
| if word_count < 150:
|
| return f"Invalid Submission: Your essay is only {word_count} words. The Common App personal statement has a strict minimum of 150 words. Do not submit fragments or test strings.", gr.update(visible=True)
|
| if word_count > 1000:
|
| return f"Invalid Submission: Your essay is {word_count} words, which exceeds the strict 1,000-word maximum limit of the VIORRA. Please edit it down before requesting Ivy League analysis.", gr.update(visible=True)
|
|
|
|
|
| device = torch.device("cuda")
|
|
|
|
|
| embedder.to(device)
|
| aes_model.to(device)
|
| llm.to(device)
|
|
|
|
|
| progress(0.3, desc="Calculating base grammar & syntax scores...")
|
| aes_inputs = aes_tokenizer(test_text, return_tensors="pt", truncation=True, max_length=512).to(device)
|
| with torch.no_grad():
|
| aes_outputs = aes_model(**aes_inputs)
|
| probs = torch.nn.functional.softmax(aes_outputs.logits[0], dim=-1)
|
| classes = torch.arange(len(probs), dtype=torch.float32, device=device)
|
| raw_score = (probs * classes).sum().item()
|
| final_aes_score = round(max(1.0, min(5.0, raw_score)), 2)
|
|
|
|
|
| progress(0.5, desc="Retrieving relevant historical examples...")
|
| query_embedding = embedder.encode([test_text], convert_to_numpy=True)
|
| distances, indices = index.search(query_embedding, 2)
|
| rag_examples = ""
|
| for i, idx in enumerate(indices[0]):
|
| rag_examples += f"\n--- SIMILAR ADMISSIONS ESSAY {i+1} ---\n"
|
| rag_examples += "EXCERPT: " + corpus_texts[idx][:300] + "...\n"
|
| rag_examples += "ADMISSIONS FEEDBACK: " + corpus_feedback[idx] + "\n"
|
|
|
|
|
| aes_inputs = aes_tokenizer(test_text, return_tensors="pt", truncation=True, max_length=512).to(device)
|
| with torch.no_grad():
|
| aes_outputs = aes_model(**aes_inputs)
|
| probs = torch.nn.functional.softmax(aes_outputs.logits[0], dim=-1)
|
| classes = torch.arange(len(probs), dtype=torch.float32, device=device)
|
| raw_score = (probs * classes).sum().item()
|
| final_aes_score = round(max(1.0, min(5.0, raw_score)), 2)
|
|
|
| try:
|
| with open("system_prompt.txt", "r", encoding="utf-8") as f:
|
| sys_prompt = f.read()
|
| except FileNotFoundError:
|
| sys_prompt = "[STUDENT ESSAY]\n\"[[TEST_TEXT]]\"\n[INSTRUCTIONS]\nOutput empty JSON."
|
|
|
| sys_prompt = sys_prompt.replace("[[TEST_TEXT]]", test_text)
|
| sys_prompt = sys_prompt.replace("[[AES_SCORE]]", str(final_aes_score))
|
| sys_prompt = sys_prompt.replace("[[NARRATIVE_HOOK]]", str(int((final_aes_score/5.0)*100)))
|
| sys_prompt = sys_prompt.replace("[[RAG_EXAMPLES]]", rag_examples)
|
|
|
|
|
| messages = [{"role": "user", "content": sys_prompt}]
|
| try:
|
| prompt_text = llm_tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
|
| except Exception:
|
| prompt_text = f"{sys_prompt}\n"
|
|
|
| inputs = llm_tokenizer(prompt_text, return_tensors="pt").to(device)
|
|
|
| progress(0.7, desc="Analyzing Statement (this may take up to 40 seconds)...")
|
| with torch.no_grad():
|
| outputs = llm.generate(
|
| **inputs,
|
| max_new_tokens=2048,
|
| do_sample=True,
|
| temperature=0.7
|
| )
|
|
|
| output_text = llm_tokenizer.decode(outputs[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True)
|
|
|
|
|
| progress(0.9, desc="Formatting evaluation report...")
|
| try:
|
|
|
| json_str = output_text
|
| match = re.search(r'\{[\s\S]*\}', output_text)
|
| if match:
|
| json_str = match.group(0)
|
|
|
| data = json.loads(json_str)
|
|
|
| clarity = data.get("scores", {}).get("clarity_meter", 0)
|
| motivation = data.get("scores", {}).get("motivation_index", 0)
|
| fit = data.get("scores", {}).get("institutional_fit", 0)
|
|
|
| md_report = f"""
|
| ### 📊 Final Evaluation Scores
|
| * **Clarity Meter:** {clarity}/100
|
| * **Motivation Index:** {motivation}/100
|
| * **Institutional Fit:** {fit}/100
|
|
|
| ### 🧠 Expert Diagnostics
|
| """
|
| diagnostics = data.get("diagnostics", [])
|
| for diag in diagnostics:
|
| md_report += f"- **{diag.get('title', 'Issue')}**: \"*{diag.get('quote', '')}*\" \n - **Feedback**: {diag.get('feedback', '')}\n\n"
|
|
|
| return md_report, gr.update(visible=True)
|
|
|
| except Exception as e:
|
| return f"Failed to parse JSON. Raw output: \n{output_text}\n\nError: {str(e)}", gr.update(visible=True)
|
|
|
| ivy_theme = gr.themes.Default(
|
| primary_hue="red",
|
| font=[gr.themes.GoogleFont("Inter"), "ui-sans-serif", "system-ui", "sans-serif"],
|
| )
|
|
|
| css = """
|
| /* ===== VIORRA PREMIUM REDESIGN ===== */
|
|
|
| /* Global: lock to cream-white, override Gradio dark mode completely */
|
| body, .dark, .gradio-container, .dark .gradio-container,
|
| .main, .dark .main, .contain, .dark .contain {
|
| background-color: #faf8f5 !important;
|
| color: #1e293b !important;
|
| }
|
|
|
| /* All block/panel backgrounds */
|
| .block, .dark .block, .panel, .dark .panel,
|
| .form, .dark .form, .wrap, .dark .wrap {
|
| background-color: transparent !important;
|
| border: none !important;
|
| box-shadow: none !important;
|
| }
|
|
|
| /* Main column: min height to prevent footer jump */
|
| .gradio-column, .dark .gradio-column {
|
| min-height: 50vh !important;
|
| }
|
|
|
| /* Textarea — warm cream, not stark white */
|
| textarea, .dark textarea {
|
| background-color: #faf8f5 !important;
|
| color: #1e293b !important;
|
| border: 1.5px solid #d4cdc3 !important;
|
| border-radius: 12px !important;
|
| padding: 1.25rem !important;
|
| font-size: 1rem !important;
|
| line-height: 1.7 !important;
|
| font-family: 'Inter', sans-serif !important;
|
| transition: border-color 0.2s ease, box-shadow 0.2s ease !important;
|
| }
|
| textarea:focus, .dark textarea:focus {
|
| border-color: #8b0000 !important;
|
| box-shadow: 0 0 0 3px rgba(139, 0, 0, 0.08) !important;
|
| outline: none !important;
|
| }
|
| textarea::placeholder {
|
| color: #9ca3af !important;
|
| font-style: italic !important;
|
| }
|
|
|
| /* Primary button */
|
| button.primary, .dark button.primary {
|
| background: linear-gradient(135deg, #8b0000, #a31515) !important;
|
| color: #ffffff !important;
|
| border: none !important;
|
| border-radius: 12px !important;
|
| padding: 0.9rem 2rem !important;
|
| font-size: 1.05rem !important;
|
| font-weight: 600 !important;
|
| letter-spacing: 0.02em !important;
|
| cursor: pointer !important;
|
| transition: all 0.25s ease !important;
|
| box-shadow: 0 2px 8px rgba(139, 0, 0, 0.15) !important;
|
| }
|
| button.primary:hover, .dark button.primary:hover {
|
| background: linear-gradient(135deg, #a31515, #c0392b) !important;
|
| box-shadow: 0 4px 16px rgba(139, 0, 0, 0.25) !important;
|
| transform: translateY(-1px) !important;
|
| }
|
|
|
| /* Secondary button */
|
| button.secondary, .dark button.secondary {
|
| background-color: #faf8f5 !important;
|
| color: #8b0000 !important;
|
| border: 1.5px solid #8b0000 !important;
|
| border-radius: 12px !important;
|
| padding: 0.9rem 2rem !important;
|
| font-size: 1.05rem !important;
|
| font-weight: 600 !important;
|
| cursor: pointer !important;
|
| transition: all 0.25s ease !important;
|
| }
|
| button.secondary:hover, .dark button.secondary:hover {
|
| background-color: #fef2f2 !important;
|
| box-shadow: 0 2px 8px rgba(139, 0, 0, 0.1) !important;
|
| }
|
|
|
| /* Markdown / prose output */
|
| .prose, .dark .prose,
|
| .prose *, .dark .prose *,
|
| .markdown-text, .dark .markdown-text {
|
| color: #1e293b !important;
|
| }
|
| .prose strong, .dark .prose strong {
|
| color: #0f172a !important;
|
| }
|
| .prose h1, .prose h2, .prose h3,
|
| .dark .prose h1, .dark .prose h2, .dark .prose h3 {
|
| color: #0f172a !important;
|
| font-weight: 700 !important;
|
| }
|
| .prose em, .dark .prose em {
|
| color: #1e293b !important;
|
| }
|
| .prose li, .dark .prose li {
|
| color: #1e293b !important;
|
| }
|
|
|
| /* Copy button — styled nicely */
|
| button[title="copy"], .dark button[title="copy"],
|
| .copy-btn, .dark .copy-btn {
|
| background-color: #f1ece4 !important;
|
| color: #8b0000 !important;
|
| border: 1px solid #d4cdc3 !important;
|
| border-radius: 8px !important;
|
| padding: 6px 10px !important;
|
| transition: all 0.2s ease !important;
|
| }
|
| button[title="copy"]:hover, .dark button[title="copy"]:hover,
|
| .copy-btn:hover, .dark .copy-btn:hover {
|
| background-color: #8b0000 !important;
|
| color: #ffffff !important;
|
| border-color: #8b0000 !important;
|
| }
|
|
|
| /* Header */
|
| .viorra-hero {
|
| text-align: center;
|
| padding: 3rem 1rem 2rem;
|
| margin-bottom: 0.5rem;
|
| }
|
| .viorra-hero h1 {
|
| font-size: 3.8rem;
|
| font-weight: 800;
|
| color: #8b0000;
|
| letter-spacing: 6px;
|
| text-transform: uppercase;
|
| margin: 0 0 0.6rem 0;
|
| line-height: 1;
|
| }
|
| .viorra-hero .tagline {
|
| font-size: 1.05rem;
|
| color: #64748b;
|
| font-weight: 400;
|
| letter-spacing: 0.5px;
|
| }
|
| .viorra-hero .divider {
|
| width: 60px;
|
| height: 3px;
|
| background: linear-gradient(90deg, #8b0000, #c0392b);
|
| margin: 1.2rem auto 0;
|
| border-radius: 2px;
|
| }
|
|
|
| /* Footer */
|
| .viorra-footer {
|
| text-align: center;
|
| margin-top: 3rem;
|
| padding: 2rem 1rem;
|
| border-top: 1px solid #e2ddd5;
|
| }
|
| .viorra-footer .team-label {
|
| font-size: 0.95rem;
|
| color: #475569;
|
| margin-bottom: 0.75rem;
|
| font-weight: 500;
|
| }
|
| .viorra-footer .team-label strong {
|
| color: #1e293b;
|
| }
|
| .viorra-footer .members {
|
| font-size: 0.85rem;
|
| color: #64748b;
|
| line-height: 1.8;
|
| }
|
| .viorra-footer .members strong {
|
| color: #334155;
|
| font-weight: 600;
|
| }
|
| .viorra-footer .members .role {
|
| color: #94a3b8;
|
| font-size: 0.8rem;
|
| }
|
|
|
| /* Progress bar */
|
| .progress-bar, .dark .progress-bar {
|
| background-color: #8b0000 !important;
|
| }
|
| .progress-text, .dark .progress-text {
|
| color: #1e293b !important;
|
| }
|
| .eta-bar {
|
| background: linear-gradient(135deg, #8b0000, #c0392b) !important;
|
| }
|
|
|
| /* Warning Box */
|
| .viorra-warning {
|
| background-color: #fef2f2;
|
| border: 1px solid #f87171;
|
| color: #991b1b;
|
| padding: 1rem;
|
| border-radius: 8px;
|
| text-align: center;
|
| margin: 0 auto 2rem auto;
|
| max-width: 800px;
|
| font-size: 0.95rem;
|
| line-height: 1.5;
|
| }
|
| .viorra-warning strong {
|
| color: #7f1d1d;
|
| }
|
|
|
| /* Remove Gradio default borders and shadows on containers */
|
| .gradio-container .contain, .dark .gradio-container .contain {
|
| box-shadow: none !important;
|
| }
|
| """
|
|
|
| with gr.Blocks(theme=ivy_theme, css=css) as demo:
|
| gr.HTML('''
|
| <div class="viorra-hero">
|
| <h1>VIORRA</h1>
|
| <p class="tagline">Smart Personal Statement Checker • Fast, Accurate, and Reliable</p>
|
| <div class="divider"></div>
|
| </div>
|
|
|
| <div class="viorra-warning">
|
| <strong>⚠️ LIMITED DEMO VERSION</strong><br>
|
| This is a lightweight cloud preview. For the complete VIORRA experience—including 100% offline privacy, interactive chat follow-ups, and session history—<a href="https://github.com/qsardor/VIORRA" target="_blank" style="color: #8b0000; text-decoration: underline; font-weight: 600;">install the full application from GitHub</a>.
|
| </div>
|
| ''')
|
|
|
| with gr.Column():
|
| with gr.Column(visible=True) as input_container:
|
| essay_input = gr.Textbox(
|
| lines=12,
|
| placeholder="Paste the student's Personal Statement here.\n\nVIORRA will instantly analyze clarity, score the document, and provide expert-level diagnostics...",
|
| label="Personal Statement",
|
| show_label=False,
|
| container=False
|
| )
|
| submit_btn = gr.Button("Analyze Personal Statement", variant="primary", size="lg")
|
|
|
| with gr.Column(visible=False) as output_container:
|
| report_output = gr.Markdown(
|
| label="Evaluation Report",
|
| value="*Analyzing... Please wait.*",
|
| show_copy_button=True
|
| )
|
| reset_btn = gr.Button("Analyze Another Statement", variant="secondary", size="lg", visible=False)
|
|
|
| def show_loading():
|
| return (
|
| gr.update(visible=False),
|
| gr.update(visible=False),
|
| gr.update(visible=True),
|
| gr.update(value="*Analyzing your statement... This may take up to 40 seconds.*"),
|
| gr.update(visible=False)
|
| )
|
|
|
| def reset_ui():
|
| return gr.update(visible=True), gr.update(visible=True), gr.update(visible=False), gr.update(value="")
|
|
|
| submit_btn.click(
|
| fn=show_loading,
|
| inputs=[],
|
| outputs=[essay_input, submit_btn, output_container, report_output, reset_btn],
|
| queue=False
|
| ).then(
|
| fn=analyze_essay,
|
| inputs=essay_input,
|
| outputs=[report_output, reset_btn]
|
| )
|
|
|
| reset_btn.click(
|
| fn=reset_ui,
|
| inputs=[],
|
| outputs=[essay_input, submit_btn, output_container, essay_input],
|
| queue=False
|
| )
|
|
|
| gr.HTML("""
|
| <div class="viorra-footer">
|
| <p class="team-label">Created with ❤️ by <strong>Team Violets</strong></p>
|
| <p class="members">
|
| <strong>Azizakhan Rustamova</strong> <span class="role">Founder & Marketing</span> •
|
| <strong>Sardor Qurbonov</strong> <span class="role">Main Developer of Software</span><br>
|
| <strong>Ruhshona Farhodova</strong> <span class="role">Business Developer</span> •
|
| <strong>Damirbek Xolnazarov</strong> <span class="role">Full Stack Developer</span>
|
| </p>
|
| </div>
|
| """)
|
|
|
| if __name__ == "__main__":
|
| demo.launch()
|
|
|
