| import streamlit as st |
| import pandas as pd |
| from sentence_transformers import SentenceTransformer, util |
| from groq import Groq |
|
|
| |
| st.set_page_config( |
| page_title="ZeroAi - Model Recommendation Engine", |
| page_icon="⚡", |
| layout="wide" |
| ) |
|
|
| st.markdown(""" |
| <style> |
| /* Premium Cyber-Dark Space Backdrop */ |
| .stApp { |
| background: radial-gradient(circle at 10% 20%, rgba(16, 185, 129, 0.12) 0%, transparent 45%), |
| radial-gradient(circle at 90% 80%, rgba(59, 130, 246, 0.12) 0%, transparent 45%), |
| #020617; |
| color: #f3f4f6; |
| } |
| |
| /* Central Hub Title Card */ |
| .hero-banner { |
| text-align: center; |
| padding: 30px; |
| background: rgba(15, 23, 42, 0.5); |
| backdrop-filter: blur(16px); |
| border: 1px solid rgba(255, 255, 255, 0.05); |
| border-radius: 24px; |
| margin-bottom: 25px; |
| box-shadow: 0 10px 30px rgba(0, 0, 0, 0.4); |
| } |
| .hero-banner h1 { |
| background: linear-gradient(to right, #ffffff, #10b981, #3b82f6); |
| -webkit-background-clip: text; |
| -webkit-text-fill-color: transparent; |
| font-weight: 800; |
| font-size: 2.8rem; |
| letter-spacing: -1px; |
| } |
| |
| /* Neon glow adjustments for metrics display layout */ |
| div[data-testid="stMetricValue"] { |
| color: #10b981 !important; |
| font-weight: 800 !important; |
| } |
| .stMetric { |
| background: rgba(30, 41, 59, 0.35) !important; |
| backdrop-filter: blur(10px); |
| border: 1px solid rgba(255, 255, 255, 0.05) !important; |
| padding: 15px !important; |
| border-radius: 16px !important; |
| } |
| |
| /* Card design layout templates */ |
| .rec-card { |
| background: rgba(15, 23, 42, 0.6); |
| border-left: 4px solid #10b981; |
| padding: 22px; |
| border-radius: 14px; |
| margin-bottom: 20px; |
| border-top: 1px solid rgba(255, 255, 255, 0.05); |
| border-right: 1px solid rgba(255, 255, 255, 0.05); |
| border-bottom: 1px solid rgba(255, 255, 255, 0.05); |
| } |
| .consultant-card { |
| background: rgba(30, 41, 59, 0.25); |
| border: 1px dashed rgba(59, 130, 246, 0.4); |
| padding: 20px; |
| border-radius: 14px; |
| margin-top: 25px; |
| } |
| |
| /* Performance Badges */ |
| .badge { |
| display: inline-block; |
| padding: 3px 12px; |
| border-radius: 20px; |
| font-size: 0.75rem; |
| font-weight: 600; |
| margin-right: 8px; |
| margin-top: 5px; |
| } |
| .b-speed { background-color: rgba(239, 68, 68, 0.15); color: #f87171; border: 1px solid rgba(239, 68, 68, 0.3); } |
| .b-accuracy { background-color: rgba(59, 130, 246, 0.15); color: #60a5fa; border: 1px solid rgba(59, 130, 246, 0.3); } |
| .b-size { background-color: rgba(16, 185, 129, 0.15); color: #34d399; border: 1px solid rgba(16, 185, 129, 0.3); } |
| </style> |
| """, unsafe_allow_html=True) |
|
|
| |
| @st.cache_data |
| def get_model_universe(): |
| return [ |
| |
| {"name": "distilbert-base-uncased-finetuned-sst-2-english", "category": "Sentiment Analysis / Text Classification", "speed": 95, "accuracy": 91, "size": "268 MB", "tier": "Lightweight", "desc": "Standard production champion for rapid text emotional polarity checks."}, |
| {"name": "cardiffnlp/twitter-roberta-base-sentiment-latest", "category": "Sentiment Analysis / Text Classification", "speed": 72, "accuracy": 95, "size": "499 MB", "tier": "Balanced Accuracy", "desc": "Superb understanding of text semantics, social colloquialism, and emojis."}, |
| {"name": "prajjwal1/bert-tiny", "category": "Sentiment Analysis / Text Classification", "speed": 99, "accuracy": 76, "size": "17.8 MB", "tier": "Edge / Ultra-Lightweight", "desc": "Microscopic structural footprint ideal for low-compute mobile systems."}, |
| |
| |
| {"name": "facebook/bart-large-cnn", "category": "Summarization", "speed": 48, "accuracy": 96, "size": "1.63 GB", "tier": "Heavyweight Elite", "desc": "Generates pristine, highly articulate abstractive overviews of large document batches."}, |
| {"name": "sshleifer/distilbart-cnn-12-6", "category": "Summarization", "speed": 82, "accuracy": 90, "size": "1.20 GB", "tier": "Balanced Performance", "desc": "Distilled sequence-to-sequence structure saving compute cycles while retaining summary context."}, |
| |
| |
| {"name": "dbmdz/bert-large-cased-finetuned-conll03-english", "category": "Named Entity Recognition (NER)", "speed": 60, "accuracy": 97, "size": "1.33 GB", "tier": "High Precision", "desc": "Exceptional accuracy benchmarks locating dates, organizations, and geographic records."}, |
| {"name": "elastic/distilbert-base-cased-finetuned-conll03-english", "category": "Named Entity Recognition (NER)", "speed": 94, "accuracy": 89, "size": "261 MB", "tier": "Production Fast-Track", "desc": "Slashes processing pipelines latency timelines on enterprise logs parsing loops."}, |
| |
| |
| {"name": "Helsinki-NLP/opus-mt-en-de", "category": "Translation", "speed": 85, "accuracy": 92, "size": "298 MB", "tier": "Targeted Local", "desc": "Highly reliable direct sequence alignment framework built cleanly for European regional shifts."}, |
| {"name": "facebook/m2m100_418M", "category": "Translation", "speed": 55, "accuracy": 90, "size": "1.84 GB", "tier": "Universal Mesh", "desc": "Can cross-translate directly between 100 languages without routing through English first."}, |
| |
| |
| {"name": "deepset/roberta-base-squad2", "category": "Question Answering", "speed": 74, "accuracy": 93, "size": "496 MB", "tier": "Extractive standard", "desc": "Excellent for context query retrieval engines scanning structured file manuals."}, |
| {"name": "Intel/dynamic_tinybert_squad2", "category": "Question Answering", "speed": 92, "accuracy": 84, "size": "114 MB", "desc": "Accelerated quantization format ensuring nimble interactions on shared networks."}, |
| |
| |
| {"name": "Qwen/Qwen2.5-Coder-7B-Instruct", "category": "Code Generation & Syntax Design", "speed": 68, "accuracy": 94, "size": "14.0 GB", "tier": "Advanced Local Code", "desc": "State-of-the-art parameters handling polyglot script builds, bug detection, and repo logic."}, |
| {"name": "HuggingFaceTB/SmolLM2-1.3B-Instruct", "category": "General Text Generation & Instructions", "speed": 96, "accuracy": 82, "size": "2.6 GB", "tier": "On-Device Companion", "desc": "Incredible conversational instruction layout designed to squeeze performance on limited rigs."} |
| ] |
|
|
| |
| @st.cache_resource |
| def init_local_embedder(): |
| return SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2") |
|
|
| model_universe = get_model_universe() |
| embedder = init_local_embedder() |
|
|
| |
| categories_list = list(set([m["category"] for m in model_universe])) |
| category_vectors = embedder.encode(categories_list, convert_to_tensor=True) |
|
|
| |
| GROQ_KEY = st.secrets.get("GROQ_API_KEY", "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") |
| groq_client = Groq(api_key=GROQ_KEY) |
|
|
| |
| st.markdown(""" |
| <div class="hero-banner"> |
| <h1>ZeroAi</h1> |
| <p style="color: #94a3b8; font-size: 1.1rem; margin-top: 5px; font-weight: 500;"> |
| 📊 Autonomous Model Recommendation Engine & Architectural Expert |
| </p> |
| </div> |
| """, unsafe_allow_html=True) |
|
|
| |
| panel_left, panel_right = st.columns([1.1, 2.5], gap="large") |
|
|
| with panel_left: |
| st.markdown("### 📈 Core Dimensions") |
| stat_c1, stat_c2 = st.columns(2) |
| stat_c1.metric("Models Indexed", len(model_universe)) |
| stat_c2.metric("Task Arenas", len(categories_list)) |
| |
| st.markdown("---") |
| st.markdown(""" |
| ### 🏆 Best For |
| * 🔥 **Speed:** DistilBERT / TinyBERT |
| * 🎯 **Accuracy:** RoBERTa / BART Large |
| * 📱 **Lightweight:** BERT-tiny variants |
| * 🌐 **Multi-language:** Helsinki-NLP / M2M100 |
| """) |
| st.markdown("---") |
| st.markdown(""" |
| ### 💡 How It Works |
| 1. **Analyzes text query semantic properties** locally. |
| 2. **Maps tasks autonomously** against open-source datasets. |
| 3. **Scores candidate options** matching your performance filters. |
| 4. **Groq Core performs deep consulting logic** to map custom strategies. |
| """) |
|
|
| with panel_right: |
| st.markdown("### 🔍 Enterprise Search & Constraints Core") |
| user_prompt = st.text_input( |
| "Describe your technical requirements, goals, or deployment limits:", |
| placeholder="Example: I need a rapid setup to parse short social media complaints on cheap hardware..." |
| ) |
| |
| |
| st.markdown("##### Priority Weight Controls") |
| w_c1, w_c2 = st.columns(2) |
| speed_factor = w_c1.slider("Speed/Inference Importance", 1, 10, 6) |
| acc_factor = w_c2.slider("Accuracy/Precision Importance", 1, 10, 8) |
| |
| |
| selected_tier = st.selectbox( |
| "Preferred Hardware Tier Filtering (Optional):", |
| ["All Specifications", "Lightweight", "Balanced Accuracy", "High Precision", "Universal Mesh"] |
| ) |
| |
| if user_prompt: |
| |
| prompt_vector = embedder.encode(user_prompt, convert_to_tensor=True) |
| search_match = util.semantic_search(prompt_vector, category_vectors, top_k=1) |
| identified_arena = categories_list[search_match[0][0]['corpus_id']] |
| |
| st.markdown(f"🤖 **ZeroAi Intent Analyzer:** Identified Task Domain Target as 👉 ` {identified_arena} `") |
| st.write("---") |
| |
| |
| candidate_pool = [] |
| for model in model_universe: |
| |
| if model["category"] == identified_arena: |
| |
| composite_rating = ((model["speed"] * speed_factor) + (model["accuracy"] * acc_factor)) / (speed_factor + acc_factor) |
| |
| |
| if selected_tier != "All Specifications" and "tier" in model: |
| if selected_tier.lower() not in model["tier"].lower(): |
| continue |
| |
| candidate_pool.append({**model, "final_rating": round(composite_rating, 1)}) |
| |
| |
| candidate_pool = sorted(candidate_pool, key=lambda x: x["final_rating"], reverse=True) |
| |
| if not candidate_pool: |
| st.warning("No specific models found matching that exact hardware subset. Displaying baseline category models instead.") |
| candidate_pool = [m for m in model_universe if m["category"] == identified_arena] |
| for c in candidate_pool: |
| c["final_rating"] = 50.0 |
| |
| |
| st.markdown("#### Meta-Analysis Matrix: Top Matches") |
| for rank, item in enumerate(candidate_pool[:3]): |
| award_title = "🥇 Optimal Machine Choice" if rank == 0 else f"🥈 Alternative Match #{rank+1}" |
| st.markdown(f""" |
| <div class="rec-card"> |
| <div style="display: flex; justify-content: space-between; align-items: center;"> |
| <span style="font-weight: 700; font-size: 1.15rem; color: #10b981;">{item['name']}</span> |
| <span style="color: #94a3b8; font-size: 0.8rem; font-weight: bold; text-transform: uppercase;">{award_title}</span> |
| </div> |
| <p style="color: #cbd5e1; font-size: 0.9rem; margin-top: 6px;">{item['desc']}</p> |
| <div style="margin-top: 10px;"> |
| <span class="badge b-speed">⚡ Speed: {item['speed']}/100</span> |
| <span class="badge b-accuracy">🎯 Accuracy: {item['accuracy']}/100</span> |
| <span class="badge b-size">📦 Weight: {item['size']}</span> |
| <span style="float: right; font-weight: 700; color: #3b82f6;">Fitness Metric: {item.get('final_rating', 'N/A')}%</span> |
| </div> |
| </div> |
| """, unsafe_allow_html=True) |
| |
| |
| st.write("---") |
| st.markdown("### 🧠 ZeroAi Deep Advisory Report (Powered by Groq Cloud)") |
| |
| with st.spinner("Generating specialized implementation architecture blueprint..."): |
| best_model_choice = candidate_pool[0]["name"] |
| |
| |
| expert_prompt = f""" |
| You are the advanced brain of ZeroAi Engine. The user prompt is: "{user_prompt}" |
| The mapped task category is: "{identified_arena}" |
| The calculated best choice model is: "{best_model_choice}" |
| |
| Provide a professional, concise executive advisory breakdown containing: |
| 1. Why this selection fits their constraint needs perfectly. |
| 2. A tiny 4-5 line clean Python pipeline snippet using `transformers` to load and run this exact model instantly for them. |
| Keep text professional, clean, dark-mode readable and straight to the point. |
| """ |
| |
| try: |
| chat_feedback = groq_client.chat.completions.create( |
| model="llama-3.3-70b-versatile", |
| messages=[ |
| {"role": "system", "content": "You are the advanced ZeroAi core recommendation consultant code manager. Output valid markdown."}, |
| {"role": "user", "content": expert_prompt} |
| ], |
| temperature=0.3 |
| ) |
| |
| report_content = chat_feedback.choices[0].message.content |
| st.markdown(f""" |
| <div class="consultant-card"> |
| <div style="font-weight: bold; font-size: 1.05rem; color: #60a5fa; margin-bottom: 12px; display: flex; align-items: center; gap: 8px;"> |
| <span>🔹 System Deployment Architecture Advisory Report</span> |
| </div> |
| {report_content} |
| </div> |
| """, unsafe_allow_html=True) |
| |
| except Exception as system_err: |
| st.info("Advisory text generation offline. Use the local parameters mapping card layout matrix displayed above.") |
| else: |
| st.info("Input a system task statement above. ZeroAi will handle parsing, filtering, evaluation, and code snippet generation automatically.") |
