boladeE
859c17aad8
- Update config.py with Pinecone settings and model configurations - Implement VectorStore class with Pinecone backend - Add comprehensive vector operations (add, search, delete) - Set up proper error handling and metadata management - Add .gitignore for Python project
101 lines
3.8 KiB
Python
101 lines
3.8 KiB
Python
import requests
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import json
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import argparse
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def test_direct_model(prompt, max_length=200, num_return_sequences=1, temperature=0.7, top_p=0.9):
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"""Test the direct model inference endpoint."""
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url = "http://localhost:8000/direct-model"
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payload = {
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"prompt": prompt,
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"max_length": max_length,
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"num_return_sequences": num_return_sequences,
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"temperature": temperature,
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"top_p": top_p
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}
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print(f"Sending request to {url}")
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print(f"Payload: {json.dumps(payload, indent=2)}")
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response = requests.post(url, json=payload)
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if response.status_code == 200:
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result = response.json()
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print("\nResponse:")
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for i, text in enumerate(result["generated_texts"]):
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print(f"\n--- Generation {i+1} ---")
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print(text)
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else:
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print(f"Error: {response.status_code}")
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print(response.text)
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def test_generate_copy(prompt, content_type, tone=None, target_audience=None):
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"""Test the generate-copy endpoint."""
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url = "http://localhost:8000/generate-copy"
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payload = {
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"prompt": prompt,
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"content_type": content_type
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}
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if tone:
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payload["tone"] = tone
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if target_audience:
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payload["target_audience"] = target_audience
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print(f"Sending request to {url}")
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print(f"Payload: {json.dumps(payload, indent=2)}")
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response = requests.post(url, json=payload)
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if response.status_code == 200:
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result = response.json()
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print("\nResponse:")
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print(f"Content: {result['content']}")
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print(f"Confidence Score: {result['confidence_score']}")
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print(f"Brand Alignment Score: {result['brand_alignment_score']}")
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else:
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print(f"Error: {response.status_code}")
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print(response.text)
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def main():
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parser = argparse.ArgumentParser(description="Test the backend with the finetuned model")
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parser.add_argument("--endpoint", type=str, choices=["direct-model", "generate-copy"], default="direct-model",
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help="Endpoint to test")
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parser.add_argument("--prompt", type=str, default="Create a welcome message for new clients",
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help="Prompt to generate text for")
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parser.add_argument("--content-type", type=str, default="email",
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help="Content type (for generate-copy endpoint)")
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parser.add_argument("--tone", type=str, default=None,
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help="Tone (for generate-copy endpoint)")
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parser.add_argument("--target-audience", type=str, default=None,
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help="Target audience (for generate-copy endpoint)")
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parser.add_argument("--max-length", type=int, default=200,
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help="Maximum length of the generated text (for direct-model endpoint)")
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parser.add_argument("--num-return-sequences", type=int, default=1,
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help="Number of sequences to generate (for direct-model endpoint)")
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parser.add_argument("--temperature", type=float, default=0.7,
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help="Temperature for sampling (for direct-model endpoint)")
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parser.add_argument("--top-p", type=float, default=0.9,
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help="Top-p for sampling (for direct-model endpoint)")
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args = parser.parse_args()
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if args.endpoint == "direct-model":
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test_direct_model(
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prompt=args.prompt,
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max_length=args.max_length,
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num_return_sequences=args.num_return_sequences,
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temperature=args.temperature,
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top_p=args.top_p
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)
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else:
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test_generate_copy(
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prompt=args.prompt,
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content_type=args.content_type,
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tone=args.tone,
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target_audience=args.target_audience
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)
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if __name__ == "__main__":
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main() |