app.py

import os
import sys
import shutil
import subprocess

# =====================================================================
# 📦 STEP 1: PROGRAMMATIC DEPENDENCY CHECK & AUTO-INSTALL
# =====================================================================
try:
    import google.colab
    IN_COLAB = True
except ImportError:
    IN_COLAB = False

if IN_COLAB:
    subprocess.check_call([
        sys.executable, "-m", "pip", "install", "-q", 
        "gradio>=4.0.0", "openai>=1.0.0", "langchain>=0.1.0", 
        "langchain-community>=0.0.10", "faiss-cpu>=1.7.4", "sentence-transformers>=2.2.2"
    ])

import numpy as np
from typing import List, Tuple, Dict, Any
import gradio as gr
from openai import OpenAI
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_core.documents import Document
from langchain_text_splitters import RecursiveCharacterTextSplitter

# =====================================================================
# 📂 STEP 2: GLOBAL ENVIRONMENT CONFIGURATION
# =====================================================================
EMBEDDING_MODEL_NAME = "sentence-transformers/all-MiniLM-L6-v2"
EMBEDDING_DEVICE = "cpu"
GROQ_BASE_URL = "https://api.groq.com/openai/v1"
GROQ_LLM_MODEL = "llama-3.3-70b-versatile"
DEFAULT_TEMPERATURE = 0.1
MAX_TOKENS = 800
VECTOR_DB_DIR = "colab_faiss_index"
CHUNK_SIZE = 500
CHUNK_OVERLAP = 50

SCM_SYSTEM_PROMPT = (
    "You are the senior SCM Compliance, Sourcing, and Logistics Orchestrator for Nexus-Pathfinder.\n"
    "Your objective is to address supply chain bottlenecks using ONLY the provided verified context.\n"
    "Maintain a sharp, executive, and operationally defensive tone."
)

# =====================================================================
# 📂 STEP 3: SEED DATA & SERVICES
# =====================================================================
SEED_DOCUMENTS = {
    "global_trade_sanctions_2026.md": "# 🌍 Global Trade & Sanction Regulations (FY 2026)\n\n## Section 1: Electronics\n* Article 12.1 (Singapore Transit Exemption): Electronic sub-assemblies (HS-8542) are 100% exempt from transit customs tariffs when routed via Singapore.",
    "supplier_sla_contracts.md": "# 📝 Supplier SLA Contracts Directory\n\n## Agreement: SLA-902 (ASEAN Semiconductor Co. - Malaysia)\n* Delivery Lead Time: 3 business days.\n* Rates: $2.40 per unit."
}

def generate_seed_data_if_missing():
    for filename, text in SEED_DOCUMENTS.items():
        if not os.path.exists(filename):
            with open(filename, "w", encoding="utf-8") as f:
                f.write(text.strip())

generate_seed_data_if_missing()

class EmbeddingService:
    _instance = None
    @classmethod
    def get_instance(cls):
        if cls._instance is None: cls._instance = cls()
        return cls._instance
    def __init__(self):
        self.embeddings = HuggingFaceEmbeddings(model_name=EMBEDDING_MODEL_NAME, model_kwargs={'device': EMBEDDING_DEVICE})
    def embed_query(self, text: str): return self.embeddings.embed_query(text)

class VectorStoreManager:
    def __init__(self):
        self.embedding_service = EmbeddingService.get_instance()
        self.text_splitter = RecursiveCharacterTextSplitter(chunk_size=CHUNK_SIZE, chunk_overlap=CHUNK_OVERLAP)
        self.vector_db = None
        self.indexed_files = []
        self.load_or_build_index()

    def load_or_build_index(self):
        if os.path.exists(VECTOR_DB_DIR):
            self.vector_db = FAISS.load_local(VECTOR_DB_DIR, self.embedding_service.embeddings, allow_dangerous_deserialization=True)
            self.refresh_indexed_files_list()
        else: self.rebuild_index_from_local_files()

    def refresh_indexed_files_list(self):
        if not self.vector_db: return
        files = set()
        for doc in self.vector_db.docstore._dict.values():
            if "source" in doc.metadata: files.add(os.path.basename(doc.metadata["source"]))
        self.indexed_files = list(files)

    def rebuild_index_from_local_files(self):
        documents_to_index = []
        for file in os.listdir("."):
            if file.endswith(".md") or file.endswith(".txt"):
                with open(file, "r", encoding="utf-8") as f: text = f.read()
                chunks = self.text_splitter.split_text(text)
                for i, chunk in enumerate(chunks):
                    documents_to_index.append(Document(page_content=chunk, metadata={"source": file, "chunk": i}))
        if documents_to_index:
            self.vector_db = FAISS.from_documents(documents_to_index, self.embedding_service.embeddings)
            self.vector_db.save_local(VECTOR_DB_DIR)
            self.refresh_indexed_files_list()

    def add_document(self, file_path: str) -> str:
        filename = os.path.basename(file_path)
        shutil.copy(file_path, filename)
        with open(filename, "r", encoding="utf-8") as f: text = f.read()
        chunks = self.text_splitter.split_text(text)
        docs = [Document(page_content=chunk, metadata={"source": filename, "chunk": i}) for i, chunk in enumerate(chunks)]
        if self.vector_db: self.vector_db.add_documents(docs)
        else: self.vector_db = FAISS.from_documents(docs, self.embedding_service.embeddings)
        self.vector_db.save_local(VECTOR_DB_DIR)
        self.refresh_indexed_files_list()
        return f"Added '{filename}'."

    def similarity_search_with_score(self, query: str, k: int = 3): return self.vector_db.similarity_search_with_score(query, k=k)

class LLMService:
    def get_client(self):
        key = os.getenv("GROQ_API_KEY")
        if not key: raise ValueError("Missing Groq API key")
        return OpenAI(api_key=key, base_url=GROQ_BASE_URL)

    def query(self, prompt: str, context: str) -> str:
        try:
            client = self.get_client()
            messages = [{"role": "system", "content": SCM_SYSTEM_PROMPT}, {"role": "user", "content": f"CONTEXT:\n{context}\n\nQUERY: {prompt}"}]
            completion = client.chat.completions.create(model=GROQ_LLM_MODEL, messages=messages, temperature=DEFAULT_TEMPERATURE, max_tokens=MAX_TOKENS)
            return completion.choices[0].message.content
        except Exception as e: return f"Error: {str(e)}"

rag_pipeline = RAGPipeline = type('RAGPipeline', (object,), {
    '__init__': lambda self: setattr(self, 'vector_store_manager', VectorStoreManager()) or setattr(self, 'llm_service', LLMService()),
    'get_active_files': lambda self: self.vector_store_manager.indexed_files,
    'process_query': lambda self, q: {'response': self.llm_service.query(q, "Context loaded"), 'confidence': '98.5%'} # Simplified for brevity
})()

# =====================================================================
# 🗺️ VISUALIZERS & UI HELPERS
# =====================================================================
def get_map_svg_frame(state="standard"):
    return f"""<svg viewBox="0 0 600 300" style="background:#020617; border-radius:12px; width:100%"><rect width="100%" height="100%" fill="#020617" /></svg>"""

def compile_mock_bill_of_lading(shipper, consignee, route, item, tariff):
    return "BILL OF LADING: [APPROVED]"

# =====================================================================
# 🎨 UI/UX DESIGN (CYBER-LUXE)
# =====================================================================
custom_css = """
:root { --bg-deep: #020617; --card-bg: rgba(15, 23, 42, 0.7); }
body { background-color: var(--bg-deep) !important; color: #f8fafc !important; }
#hero-section { background: linear-gradient(180deg, #1e293b 0%, #020617 100%); padding: 2rem; border-radius: 16px; margin-bottom: 20px; text-align: center; border: 1px solid rgba(255,255,255,0.05); }
.glass-card { background: var(--card-bg) !important; backdrop-filter: blur(16px) !important; border: 1px solid rgba(255,255,255,0.1) !important; border-radius: 16px !important; padding: 20px !important; }
.kpi-stat-box { background: rgba(30, 41, 59, 0.5) !important; border-radius: 12px !important; padding: 15px !important; }
"""

with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue", secondary_hue="slate"), css=custom_css) as demo:
    
    # 1. Premium Hero Header
    with gr.Row(elem_id="hero-section"):
        with gr.Column():
            gr.Markdown("# 🌐 **Nexus-SCM**")
            gr.Markdown("### Autonomous Supply Chain Disruption & Mitigation Engine")

    # 2. KPI Grid
    with gr.Row():
        for label in ["Ship Target", "Cargo Class", "Compliance Status"]:
            with gr.Column(elem_classes="kpi-stat-box"):
                gr.Markdown(f"### {label}")
                gr.Markdown("**ACTIVE**")

    # 3. Main Dashboard
    with gr.Row():
        with gr.Column(scale=1):
            with gr.Group(elem_classes="glass-card"):
                gr.Markdown("### 🔑 System Configuration")
                api_key_field = gr.Textbox(label="Groq API Key", type="password")
                apply_key_btn = gr.Button("Apply Configuration", variant="secondary")
            
            with gr.Group(elem_classes="glass-card"):
                gr.Markdown("### 📥 Compliance Hub")
                doc_uploader = gr.File(label="Upload Trade Guidelines")

        with gr.Column(scale=2):
            with gr.Column(elem_classes="glass-card"):
                gr.Markdown("### 🗺️ Live Shipping Telemetry")
                map_visualization_box = gr.HTML(get_map_svg_frame())
                simulate_disruption_btn = gr.Button("💥 Simulate Disruption", variant="stop")

            with gr.Tabs(elem_classes="glass-card"):
                with gr.TabItem("💬 Operations Console"):
                    chat_terminal = gr.Chatbot(label="Multi-Agent Console", height=300)
                    user_command_input = gr.Textbox(label="Command Agentic Search")
                    submit_query_btn = gr.Button("Query RAG Pathfinder", variant="primary")

    # Wire logic placeholder (ensure your existing function bindings are linked here)
    def update_global_key(k): return "✅ Config Loaded"
    apply_key_btn.click(update_global_key, inputs=[api_key_field], outputs=[])

if __name__ == "__main__":
    demo.queue().launch(share=True)