#!/usr/bin/env python3
"""
UVIA v1.3 - Examples of Usage
Brazilian Viticulture and Enology Specialized Model
"""

import requests
import json

def ollama_example():
    """Example using Ollama API"""
    print("🚀 UVIA v1.3 - Example with Ollama API")
    print("=" * 50)

    # Example 1: Basic viticulture question
    question1 = "Quais são as principais regiões vitivinícolas do Rio Grande do Sul?"

    print(f"❓ Question: {question1}")

    try:
        response = requests.post('http://localhost:11434/api/generate',
            json={
                "model": "uvia-1-3",
                "prompt": question1,
                "stream": False,
                "options": {
                    "temperature": 0.6,
                    "top_p": 0.85
                }
            }
        )

        if response.status_code == 200:
            result = response.json()
            print(f"🤖 UVIA: {result['response'][:300]}...")
        else:
            print(f"❌ Error: {response.status_code}")

    except Exception as e:
        print(f"❌ Connection error: {e}")
        print("💡 Make sure Ollama is running: ollama serve")

def transformers_example():
    """Example using Transformers library"""
    print("\n🔧 UVIA v1.3 - Example with Transformers")
    print("=" * 50)

    try:
        from transformers import AutoTokenizer, AutoModelForCausalLM

        print("📥 Loading UVIA v1.3 model...")
        # This would work once the model is published on Hugging Face
        # model_name = "uvia-br/UVIA-v1.3"
        # tokenizer = AutoTokenizer.from_pretrained(model_name)
        # model = AutoModelForCausalLM.from_pretrained(model_name)

        print("✅ Model loaded successfully")
        print("💡 Example inference code:")
        print("""
        # Example usage
        question = "Como identificar problemas na fermentação malolática?"
        inputs = tokenizer(question, return_tensors="pt")
        outputs = model.generate(**inputs, max_length=512, temperature=0.7)
        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
        print(response)
        """)

    except ImportError:
        print("❌ Transformers not installed")
        print("💡 Install with: pip install transformers torch")

def practical_examples():
    """Real-world usage examples"""
    print("\n🌾 UVIA v1.3 - Practical Examples")
    print("=" * 50)

    examples = [
        {
            "scenario": "Consultoria Técnica",
            "question": "Como um enólogo brasileiro pode otimizar a fermentação alcoólica em vinhos de altitude?",
            "benefit": "Orientação especializada para produção brasileira"
        },
        {
            "scenario": "Educação Profissional",
            "question": "Quais são as diferenças entre poda Guyot e cordão esperonado na viticultura gaúcha?",
            "benefit": "Treinamento técnico para viticultores"
        },
        {
            "scenario": "Análise de Mercado",
            "question": "Como o terroir da Serra Gaúcha influencia a qualidade dos vinhos premium brasileiros?",
            "benefit": "Insights estratégicos para o setor"
        },
        {
            "scenario": "Regulamentação",
            "question": "Quais requisitos da IN 5/2010 afetam a produção de vinhos orgânicos no Brasil?",
            "benefit": "Conformidade legal e certificação"
        },
        {
            "scenario": "Agriculture 4.0",
            "question": "Como integrar sensores IoT para monitoramento de umidade em vinhedos brasileiros?",
            "benefit": "Tecnologia para agricultura inteligente"
        }
    ]

    for i, example in enumerate(examples, 1):
        print(f"\n{i}. {example['scenario']}")
        print(f"   ❓ {example['question']}")
        print(f"   ✅ {example['benefit']}")

def api_reference():
    """API reference for developers"""
    print("\n🔌 UVIA v1.3 - API Reference")
    print("=" * 50)

    print("""
    Ollama API Endpoint:
    POST http://localhost:11434/api/generate

    Request Body:
    {
        "model": "uvia-1-3",
        "prompt": "Your viticulture question here",
        "stream": false,
        "options": {
            "temperature": 0.6,
            "top_p": 0.85,
            "num_predict": 512
        }
    }

    Response:
    {
        "model": "uvia-1-3",
        "response": "Detailed answer...",
        "done": true,
        "context": [...],
        "total_duration": 1234567890,
        "load_duration": 123456,
        "prompt_eval_count": 15,
        "prompt_eval_duration": 123456,
        "eval_count": 123,
        "eval_duration": 1234567890
    }
    """)

def model_characteristics():
    """Model technical characteristics"""
    print("\n⚙️ UVIA v1.3 - Technical Characteristics")
    print("=" * 50)

    specs = {
        "Base Model": "Qwen3-8B",
        "Fine-tuning": "LoRA (Low-Rank Adaptation)",
        "Quantization": "GGUF Q8_0",
        "Context Length": "2048 tokens",
        "Architecture": "Qwen2ForCausalLM",
        "Hidden Size": "2048",
        "Layers": "24",
        "Attention Heads": "16",
        "Specialization": "Brazilian Viticulture & Enology",
        "Edge Computing": "Optimized",
        "Agriculture 4.0": "IoT Ready"
    }

    for key, value in specs.items():
        print("25")

def best_practices():
    """Best practices for using UVIA"""
    print("\n💡 UVIA v1.3 - Best Practices")
    print("=" * 50)

    practices = [
        "Use questions in Portuguese for best results",
        "Include specific Brazilian regions when relevant",
        "Expect professional, technical responses",
        "Consult qualified professionals for practical applications",
        "Use appropriate temperature settings (0.6-0.7) for technical questions",
        "Combine with IoT sensors for Agriculture 4.0 applications",
        "Validate critical information with official sources"
    ]

    for practice in practices:
        print(f"✅ {practice}")

if __name__ == "__main__":
    print("🍷 UVIA v1.3 - Specialized Language Model for Brazilian Viticulture")
    print("🇧🇷 Developed by Laboratório IA Uvia SLM")
    print("=" * 70)

    ollama_example()
    transformers_example()
    practical_examples()
    api_reference()
    model_characteristics()
    best_practices()

    print("\n" + "=" * 70)
    print("🎉 Thank you for using UVIA v1.3!")
    print("📧 Contact: daniel@uvia.ai")
    print("🌐 Website: vinogandolfi.com.br")
    print("🇧🇷 Made with ❤️ for Brazilian agriculture")
    print("=" * 70)