app.py

import streamlit as st
import pandas as pd
from sentence_transformers import SentenceTransformer, util
from groq import Groq

# 1. Advanced Custom Styling & Glassmorphic CSS Theme Injection
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)

# 2. Hardcoded Comprehensive Architecture Mapping Matrix Dataset
@st.cache_data
def get_model_universe():
    return [
        # --- Sentiment / Classification ---
        {"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."},
        
        # --- Summarization ---
        {"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."},
        
        # --- Token Classification / NER ---
        {"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."},
        
        # --- Translation ---
        {"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."},
        
        # --- Question Answering ---
        {"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."},
        
        # --- Code / Text Generation ---
        {"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."}
    ]

# 3. Initialize Engines (Local Embedder + Groq Matrix API)
@st.cache_resource
def init_local_embedder():
    return SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")

model_universe = get_model_universe()
embedder = init_local_embedder()

# Setup Categories index
categories_list = list(set([m["category"] for m in model_universe]))
category_vectors = embedder.encode(categories_list, convert_to_tensor=True)

# Secure Groq Cloud Fallback Connection
GROQ_KEY = st.secrets.get("GROQ_API_KEY", "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")
groq_client = Groq(api_key=GROQ_KEY)

# 4. Display Page Framework Renderers
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)

# Main Multi-Column Split Setup
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..."
    )
    
    # Priority Customization Section
    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)
    
    # Filter Controls
    selected_tier = st.selectbox(
        "Preferred Hardware Tier Filtering (Optional):", 
        ["All Specifications", "Lightweight", "Balanced Accuracy", "High Precision", "Universal Mesh"]
    )
    
    if user_prompt:
        # Step A: Perform vector intent analysis locally
        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("---")
        
        # Step B: Mathematical filtering and sorting loop
        candidate_pool = []
        for model in model_universe:
            # Check domain matching
            if model["category"] == identified_arena:
                # Calculate ranking values
                composite_rating = ((model["speed"] * speed_factor) + (model["accuracy"] * acc_factor)) / (speed_factor + acc_factor)
                
                # Check Hardware Filters if requested
                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)})
                
        # Sort best options to top
        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
        
        # Step C: Render Structured Local Recommendations
        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)
            
        # Step D: Call Groq Core as an Expert AI Advisor to generate integration code
        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"]
            
            # Construct instructions for Groq
            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.")