# Clone repository
git clone https://github.com/Yosef-Ali/amharic-dataset-mcp.git
cd amharic-dataset-mcp

# Install package
pip install -e .

# Install with development tools
pip install -e ".[dev]"

# Install with GPU support
pip install -e ".[gpu]"

# For Gemini integration
pip install google-generativeai

# For Qwen models
pip install transformers torch

# Complete installation with all AI models
pip install -e ".[dev,gpu]" google-generativeai transformers torch

# Start the Amharic dataset MCP server
amharic-dataset-server --port 3001

{
  "mcpServers": {
    "amharic-dataset": {
      "command": "amharic-dataset-server",
      "args": ["--port", "3001"]
    }
  }
}

import google.generativeai as genai
from amharic_dataset_mcp import AmharicDatasetPipeline

# Configure Gemini
genai.configure(api_key="your-gemini-api-key")
model = genai.GenerativeModel('gemini-pro')

# Use with Amharic MCP tools
pipeline = AmharicDatasetPipeline()
amharic_data = pipeline.collect_authentic_data(sources=["bbc_amharic"], max_items=100)

# Enhance with Gemini for translation/analysis
for item in amharic_data:
    prompt = f"Analyze this Amharic text quality: {item['text']}"
    response = model.generate_content(prompt)
    item['gemini_analysis'] = response.text

from transformers import AutoTokenizer, AutoModelForCausalLM
from amharic_dataset_mcp import AmharicQualityScorer

# Load Qwen model
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-7B-Instruct")
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B-Instruct")

# Quality scoring with Qwen
scorer = AmharicQualityScorer()
amharic_text = "እንደምን አደርክ? ደህና ነኝ፣ እግዚአብሔር ይመስገን።"

# Get quality score from MCP
quality_result = scorer.calculate_overall_quality_score(amharic_text)

# Use Qwen for additional analysis
prompt = f"Rate the naturalness of this Amharic conversation: {amharic_text}"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=150)
qwen_analysis = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(f"MCP Score: {quality_result['overall_score']:.3f}")
print(f"Qwen Analysis: {qwen_analysis}")

from amharic_dataset_mcp import AmharicDatasetPipeline
import google.generativeai as genai
from transformers import pipeline

# Initialize models
genai.configure(api_key="your-key")
gemini = genai.GenerativeModel('gemini-pro')
qwen_pipe = pipeline("text-generation", model="Qwen/Qwen2.5-3B-Instruct")

# Amharic pipeline
amharic_pipeline = AmharicDatasetPipeline()

async def multi_model_quality_check(text):
    """Use multiple models for comprehensive Amharic quality assessment"""
    
    # 1. MCP Quality Scoring
    mcp_score = amharic_pipeline.quality_scorer.calculate_overall_quality_score(text)
    
    # 2. Gemini Analysis
    gemini_prompt = f"Rate this Amharic text authenticity (1-10): {text}"
    gemini_response = gemini.generate_content(gemini_prompt)
    
    # 3. Qwen Analysis
    qwen_prompt = f"Analyze Amharic grammar: {text}"
    qwen_response = qwen_pipe(qwen_prompt, max_new_tokens=100)
    
    return {
        "text": text,
        "mcp_score": mcp_score['overall_score'],
        "mcp_category": mcp_score['quality_category'],
        "gemini_analysis": gemini_response.text,
        "qwen_analysis": qwen_response[0]['generated_text'],
        "ensemble_recommendation": "high_quality" if mcp_score['overall_score'] > 0.8 else "needs_review"
    }

# Example usage
result = await multi_model_quality_check("የኢትዮጵያ መንግስት አዲስ ፖሊሲ አወጣ።")

# Collect authentic Amharic data
await mcp_client.call_tool("collect_amharic_data", {
    "sources": ["bbc_amharic", "voa_amharic"],
    "max_items": 1000,
    "quality_threshold": 0.7
})

# Enhance data quality with RAG
await mcp_client.call_tool("enhance_amharic_quality", {
    "texts": ["የኢትዮጵያ መንግስት አዲስ ፖሊሲ አወጣ"],
    "context_category": "news"
})

# Score quality automatically  
await mcp_client.call_tool("score_amharic_quality", {
    "text": "እንደምን አደርክ? ደህና ነኝ፣ እግዚአብሔር ይመስገን።",
    "detailed_analysis": true
})

# Store in database
await mcp_client.call_tool("store_amharic_data", {
    "data": [...],
    "database_url": "sqlite:///amharic_dataset.db"
})

# Run all tests
pytest

# Run with coverage
pytest --cov=src/amharic_dataset_mcp

# Run specific test category
pytest tests/test_quality_scoring.py
pytest tests/test_rag_enhancement.py
pytest tests/test_data_collection.py

# Add custom grammar patterns
await mcp_client.call_tool("add_quality_pattern", {
    "category": "cooking_verbs",
    "good_patterns": ["ማብሰል", "ማጥበስ"],
    "bad_patterns": ["ማቁሰል", "ማቁላት"],
    "weight": 0.3
})

# Extend knowledge base with domain-specific examples
await mcp_client.call_tool("extend_knowledge_base", {
    "category": "medical",
    "examples": [
        {
            "text": "ሐኪም ወዴት ሄደህ? ወደ ሆስፒታል ሄዳለሁ።",
            "quality_score": 1.0,
            "explanation": "Uses ሐኪም (Amharic) instead of ዶክተር (borrowed)"
        }
    ]
})

# Process large datasets efficiently
await mcp_client.call_tool("batch_process_dataset", {
    "input_file": "raw_amharic_data.jsonl",
    "output_file": "processed_amharic_data.jsonl", 
    "batch_size": 100,
    "quality_threshold": 0.6
})