mirror of
https://github.com/run-llama/invoice-extraction-vibe-coding.git
synced 2026-07-01 21:14:02 -04:00
Tuana Çelik
c19267d4a5
* cr * cr * cr * cr --------- Co-authored-by: Jerry Liu <jerryjliu98@gmail.com>
84 lines
3.0 KiB
Python
84 lines
3.0 KiB
Python
"""
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Simple invoice extraction script using LlamaCloud.
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This is the starting point for vibe coding a full Streamlit application.
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Configure your LlamaCloud credentials and run this script to test extraction
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before building the full web application.
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"""
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import os
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import json
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from dotenv import load_dotenv
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from llama_cloud_services import LlamaExtract
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from llama_cloud.core.api_error import ApiError
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# Load environment variables
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load_dotenv()
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# Configuration - Update these with your LlamaCloud details
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PROJECT_ID = os.getenv("LLAMA_CLOUD_PROJECT_ID", "your-project-id-here")
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ORGANIZATION_ID = os.getenv("LLAMA_CLOUD_ORGANIZATION_ID", "your-organization-id-here")
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AGENT_NAME = os.getenv("LLAMA_CLOUD_AGENT_NAME", "your-agent-name-here")
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# Sample image path
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SAMPLE_IMAGE = "sample_data/batch1-0274.jpg"
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def main():
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"""Run invoice extraction on sample image."""
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# Validate configuration
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if PROJECT_ID == "your-project-id-here" or ORGANIZATION_ID == "your-organization-id-here":
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print("❌ Please configure your LlamaCloud credentials!")
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print("Update your .env file with:")
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print("LLAMA_CLOUD_PROJECT_ID=your-actual-project-id")
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print("LLAMA_CLOUD_ORGANIZATION_ID=your-actual-organization-id")
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print("LLAMA_CLOUD_AGENT_NAME=your-actual-agent-name")
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return
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try:
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print("🚀 Initializing LlamaCloud extraction agent...")
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# Initialize the extraction service
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extract = LlamaExtract(
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show_progress=True, # Show progress for better UX
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check_interval=5,
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project_id=PROJECT_ID,
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organization_id=ORGANIZATION_ID
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)
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# Get the configured agent
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agent = extract.get_agent(name=AGENT_NAME)
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print(f"✅ Successfully connected to agent: {AGENT_NAME}")
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# Run extraction on sample image
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print(f"📄 Processing sample invoice: {SAMPLE_IMAGE}")
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result = agent.extract(SAMPLE_IMAGE)
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# Display results
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print("✅ Extraction completed successfully!")
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print("\n📊 Extracted Data:")
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print("=" * 50)
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print(json.dumps(result.data, indent=2))
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# Save results for reference
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output_file = "sample_output.json"
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with open(output_file, "w") as f:
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json.dump(result.data, f, indent=2)
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print(f"\n💾 Results saved to: {output_file}")
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print("\n🎉 Success! You're ready to start vibe coding the Streamlit app!")
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print("Use cursor_prompt.md to transform this script into a full application.")
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except ApiError as e:
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print(f"❌ API Error: {e}")
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print("Check your API key and agent configuration.")
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except FileNotFoundError:
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print(f"❌ Sample image not found: {SAMPLE_IMAGE}")
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print("Make sure the sample_data directory exists with the sample image.")
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except Exception as e:
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print(f"❌ Unexpected error: {e}")
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if __name__ == "__main__":
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main()
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