from opperai import Opper
import time

opper = Opper(http_bearer="YOUR_API_KEY")

# First, create a function and call it to generate a trace
unique_name = f"research_assistant_{int(time.time())}"
created_function = opper.functions.create(
    name=unique_name,
    description="Research assistant that provides detailed analysis",
    instructions="You are a research assistant. Provide detailed, well-researched answers with multiple perspectives and cite your reasoning process.",
    model="openai/gpt-4o-mini",
)

print(f"Created function: {created_function.name}")

# Call the function to generate a trace
response = opper.functions.call(
    function_id=created_function.id,
    input={
        "question": "What are the key factors that led to the success of the Tesla Model S?",
        "context": "I'm researching electric vehicle market adoption",
    },
    tags={"research_type": "automotive", "complexity": "detailed"},
)

print(f"Function call completed. Span ID: {response.span_id}")

# Get the trace ID from the response (spans are part of traces)
span = opper.spans.get(span_id=response.span_id)
trace_id = span.trace_id

print(f"Generated trace ID: {trace_id}")


def print_span_hierarchy(spans, parent_id, depth):
    """Helper function to print span hierarchy"""
    children = [s for s in spans if s.parent_id == parent_id]
    for child in children:
        indent = "  " * depth
        print(f"{indent}├─ {child.name} ({child.duration_ms}ms)")
        print_span_hierarchy(spans, child.id, depth + 1)


# Get detailed trace with all spans
trace = opper.traces.get(trace_id=trace_id)

print(f"🔍 Trace Analysis: {trace.name}")
print(f"ID: {trace.id}")
print(f"Duration: {trace.duration_ms}ms")
print(f"Status: {trace.status}")
print(f"Total Tokens: {trace.total_tokens:,}")

if trace.input:
    print(f"Input: {trace.input[:100]}...")
if trace.output:
    print(f"Output: {trace.output[:100]}...")

print(f"\n📊 Span Breakdown ({len(trace.spans)} spans):")

# Group spans by type for analysis
span_types = {}
for span in trace.spans:
    span_type = span.type or "unknown"
    if span_type not in span_types:
        span_types[span_type] = []
    span_types[span_type].append(span)

for span_type, spans in span_types.items():
    print(f"\n{span_type.upper()} spans ({len(spans)}):")

    for span in spans:
        print(f"  • {span.name}")
        print(
            f"    Duration: {span.duration_ms}ms"
            if span.duration_ms
            else "    Duration: N/A"
        )

        if span.data and span.data.model:
            print(f"    Model: {span.data.model}")

        if span.data and span.data.total_tokens:
            print(f"    Tokens: {span.data.total_tokens}")

        if span.score:
            print(f"    Score: {span.score}/10")

        if span.error:
            print(f"    ❌ Error: {span.error}")

        # Show metrics if available
        if span.metrics:
            print(f"    📈 Metrics:")
            for metric in span.metrics:
                print(f"      - {metric.dimension}: {metric.value}")

# Analyze execution flow
print(f"\n🔄 Execution Flow:")
root_spans = [s for s in trace.spans if not s.parent_id]
for root_span in root_spans:
    print(f"🏁 {root_span.name}")
    print_span_hierarchy(trace.spans, root_span.id, 1)

# Performance insights
print(f"\n⚡ Performance Insights:")
if trace.duration_ms:
    total_duration = trace.duration_ms
    generation_spans = [s for s in trace.spans if s.type == "generation"]
    if generation_spans:
        generation_time = sum(s.duration_ms for s in generation_spans if s.duration_ms)
        print(
            f"LLM Generation Time: {generation_time}ms ({generation_time / total_duration * 100:.1f}%)"
        )

    call_spans = [s for s in trace.spans if s.type == "call"]
    if call_spans:
        call_durations = [s.duration_ms for s in call_spans if s.duration_ms]
        if call_durations:
            avg_call_duration = sum(call_durations) / len(call_durations)
            print(f"Average Call Duration: {avg_call_duration:.1f}ms")

{
  "id": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
  "start_time": "2023-11-07T05:31:56Z",
  "end_time": "2023-11-07T05:31:56Z",
  "duration_ms": 123,
  "status": "<string>",
  "name": "<string>",
  "input": "<string>",
  "output": "<string>",
  "total_tokens": 123,
  "spans": [
    {
      "id": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
      "name": "<string>",
      "start_time": "2023-11-07T05:31:56Z",
      "type": "generation",
      "parent_id": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
      "end_time": "2023-11-07T05:31:56Z",
      "duration_ms": 123,
      "error": "<string>",
      "meta": {},
      "data": {
        "input": "<string>",
        "output": "<string>",
        "total_tokens": 123,
        "model": "<string>",
        "instructions": "<string>",
        "function": "<string>",
        "tags": [
          "<string>"
        ],
        "score": 123,
        "generation_id": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
        "observations": "<string>"
      },
      "metrics": [
        {
          "id": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
          "dimension": "<string>",
          "value": 123,
          "created_at": "2023-11-07T05:31:56Z",
          "comment": "Expert feedback"
        }
      ],
      "score": 123
    }
  ]
}