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e8bee42d76
feat: Initialize LocalAnalyticsCollector in main.py feat: Display session data in local analytics dashboard fix: Use cumulative data from last interaction for dashboard stats fix: Extract initial query from diffs in local analytics collector.
63 lines
2.6 KiB
Python
63 lines
2.6 KiB
Python
import json
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from pathlib import Path
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from collections import defaultdict
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def calculate_cost_by_model(filepath):
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"""
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Reads session data from a JSONL file and calculates the total estimated cost per model.
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"""
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cost_by_model = defaultdict(float)
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if not filepath.exists():
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print(f"Error: Session data file not found at {filepath}")
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return dict(cost_by_model) # Return empty dict if file not found
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with open(filepath, "r", encoding="utf-8") as f:
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for line in f:
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try:
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data = json.loads(line)
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# Iterate through the models used summary for this interaction
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models_summary = data.get("models_used_summary", [])
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if not isinstance(models_summary, list):
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print(f"Warning: 'models_used_summary' is not a list in line: {line.strip()}")
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continue
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for model_info in models_summary:
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if not isinstance(model_info, dict):
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print(f"Warning: Item in 'models_used_summary' is not a dict in line: {line.strip()}")
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continue
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model_name = model_info.get("name", "Unknown Model")
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cost = model_info.get("cost", 0.0)
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# Ensure cost is a number before adding
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if isinstance(cost, (int, float)):
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cost_by_model[model_name] += cost
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else:
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print(f"Warning: Found non-numeric cost value for model '{model_name}': {cost} in line: {line.strip()}")
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except json.JSONDecodeError as e:
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print(f"Error decoding JSON from line: {line.strip()} - {e}")
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except Exception as e:
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print(f"An unexpected error occurred processing line: {line.strip()} - {e}")
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return dict(cost_by_model) # Convert defaultdict to dict for final return
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if __name__ == "__main__":
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# Define the path to the session data file
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BASE_DIR = Path(__file__).resolve().parent
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SESSION_DATA_FILE = BASE_DIR / "session.jsonl"
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cost_by_model = calculate_cost_by_model(SESSION_DATA_FILE)
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print("Total Estimated Cost by Model:")
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if cost_by_model:
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# Sort models by cost descending
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sorted_models = sorted(cost_by_model.items(), key=lambda item: item[1], reverse=True)
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for model, cost in sorted_models:
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print(f" {model}: ${cost:.4f}")
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total_overall_cost = sum(cost_by_model.values())
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print("-" * 30)
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print(f"Total Estimated Cost (Overall): ${total_overall_cost:.4f}")
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else:
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print(" No cost data found.")
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