AI Analiz Aracı

from openai import OpenAI
import json

client = OpenAI()

def analyze_dataset(data, context=""):
    """Perform AI-powered analysis on structured data."""
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[
            {"role": "system", "content": """You are a senior data analyst.
Analyze the provided data and return insights in JSON:
{
  "summary": "Brief overview",
  "key_metrics": [{"name": "", "value": "", "trend": ""}],
  "patterns": ["pattern 1", "pattern 2"],
  "anomalies": ["anomaly 1"],
  "recommendations": ["action 1", "action 2"],
  "risk_factors": ["risk 1"]
}"""},
            {"role": "user", "content": f"Context: {context}\n\nData:\n{json.dumps(data, indent=2)}"}
        ],
        response_format={"type": "json_object"}
    )
    return json.loads(response.choices[0].message.content)

# Example usage
sales_data = {
    "q1": {"revenue": 150000, "customers": 45, "churn": 3},
    "q2": {"revenue": 180000, "customers": 52, "churn": 2},
    "q3": {"revenue": 165000, "customers": 48, "churn": 7},
    "q4": {"revenue": 210000, "customers": 61, "churn": 4}
}

insights = analyze_dataset(sales_data, "Annual B2B SaaS performance")
print(json.dumps(insights, indent=2))

def analyze_text(text, analysis_type="comprehensive"):
    """Analyze text for various linguistic properties."""
    prompts = {
        "comprehensive": """Analyze this text comprehensively:
1. Main topics and themes
2. Key entities (people, companies, locations)
3. Sentiment (positive/negative/neutral + confidence)
4. Writing style and tone
5. Summary in 2-3 sentences
Return as structured JSON.""",

        "entities": """Extract all named entities from this text:
- People, Organizations, Locations, Dates, Products
Return as JSON with categories.""",

        "keywords": """Extract the top 10 most important keywords/phrases.
For each, provide relevance score (0-1) and context.
Return as JSON array."""
    }
    
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[
            {"role": "system", "content": prompts.get(analysis_type, prompts["comprehensive"])},
            {"role": "user", "content": text}
        ],
        response_format={"type": "json_object"}
    )
    return json.loads(response.choices[0].message.content)

# Usage
article = """Artificial intelligence is transforming healthcare. 
New diagnostic tools powered by machine learning can detect 
diseases earlier and more accurately than traditional methods. 
Hospitals in Germany and the US are adopting these systems rapidly."""

result = analyze_text(article, "comprehensive")
print(json.dumps(result, indent=2))

def batch_sentiment_analysis(texts):
    """Analyze sentiment for multiple texts at once."""
    formatted = "\n".join([f"{i+1}. {t}" for i, t in enumerate(texts)])
    
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[
            {"role": "system", "content": """Analyze the sentiment 
of each numbered text. Return JSON:
{"results": [
  {"id": 1, "text_preview": "first 30 chars...", 
   "sentiment": "positive/negative/neutral/mixed",
   "confidence": 0.0-1.0,
   "emotions": ["joy", "trust"],
   "key_phrases": ["positive phrase"]}
]}"""},
            {"role": "user", "content": formatted}
        ],
        response_format={"type": "json_object"}
    )
    return json.loads(response.choices[0].message.content)

# Analyze customer reviews
reviews = [
    "Absolutely love this product! Best purchase ever.",
    "Terrible experience. The item arrived broken.",
    "It's okay. Nothing special but does the job.",
    "Great quality but shipping was very slow.",
    "Customer service was outstanding, they fixed everything!"
]

results = batch_sentiment_analysis(reviews)
for r in results["results"]:
    print(f"Review {r['id']}: {r['sentiment']} ({r['confidence']})")

def detect_patterns(data, data_type="time_series"):
    """Detect patterns in data using AI analysis."""
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[
            {"role": "system", "content": f"""You are an expert data scientist.
Analyze this {data_type} data for patterns:

1. Trends (upward, downward, cyclical)
2. Seasonality or periodic patterns
3. Outliers or anomalies
4. Correlations between variables
5. Predicted next values (if time series)

Return JSON with detailed findings and confidence levels."""},
            {"role": "user", "content": json.dumps(data)}
        ],
        response_format={"type": "json_object"}
    )
    return json.loads(response.choices[0].message.content)

# Example: Detect patterns in website traffic
traffic_data = {
    "daily_visitors": [
        {"date": "2025-01-06", "visitors": 1200, "day": "Mon"},
        {"date": "2025-01-07", "visitors": 1350, "day": "Tue"},
        {"date": "2025-01-08", "visitors": 1400, "day": "Wed"},
        {"date": "2025-01-09", "visitors": 1280, "day": "Thu"},
        {"date": "2025-01-10", "visitors": 980, "day": "Fri"},
        {"date": "2025-01-11", "visitors": 650, "day": "Sat"},
        {"date": "2025-01-12", "visitors": 580, "day": "Sun"}
    ]
}

patterns = detect_patterns(traffic_data, "time_series")

def generate_report(analysis_results, report_type="executive"):
    """Generate a human-readable report from analysis results."""
    styles = {
        "executive": "Write a concise executive summary (max 300 words). Focus on key findings, business impact, and recommended actions.",
        "technical": "Write a detailed technical report with methodology, statistical details, and data quality notes.",
        "presentation": "Create presentation-ready bullet points with key metrics highlighted. Include talking points for each slide."
    }
    
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[
            {"role": "system", "content": f"""Generate a {report_type} 
report based on these analysis results.

Style: {styles.get(report_type, styles['executive'])}

Structure the report with clear sections and headers.
Use specific numbers from the data."""},
            {"role": "user", "content": json.dumps(analysis_results, indent=2)}
        ]
    )
    return response.choices[0].message.content

# Generate different report types
report = generate_report(insights, "executive")
print(report)

class AIAnalyzer:
    """Complete AI analysis pipeline."""
    
    def __init__(self):
        self.client = OpenAI()
        self.results = {}
    
    def ingest(self, data, data_type="auto"):
        """Load and prepare data for analysis."""
        self.raw_data = data
        self.data_type = data_type
        return self
    
    def analyze(self, analyses=["summary", "patterns", "sentiment"]):
        """Run multiple analyses on the data."""
        for analysis in analyses:
            if analysis == "summary":
                self.results["summary"] = analyze_dataset(self.raw_data)
            elif analysis == "patterns":
                self.results["patterns"] = detect_patterns(self.raw_data)
            elif analysis == "sentiment":
                if isinstance(self.raw_data, list):
                    self.results["sentiment"] = batch_sentiment_analysis(
                        self.raw_data
                    )
        return self
    
    def report(self, report_type="executive"):
        """Generate a final report."""
        self.results["report"] = generate_report(
            self.results, report_type
        )
        return self.results

# Usage — chain the pipeline
pipeline = AIAnalyzer()
results = (
    pipeline
    .ingest(sales_data)
    .analyze(["summary", "patterns"])
    .report("executive")
)
print(results["report"])