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
93 lines
3.0 KiB
Python
93 lines
3.0 KiB
Python
from fastapi import FastAPI, HTTPException
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from pydantic import BaseModel
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from typing import Optional, List
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import uvicorn
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from copywriter import generate_marketing_copy
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from vector_store import VectorStore
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from embeddings import CohereEmbeddings
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from brand_style import BrandStyleChecker
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from config import Settings
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from finetuned_model import finetuned_model
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app = FastAPI(title="Marketing Assistant AI")
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settings = Settings()
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vector_store = VectorStore()
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embeddings = CohereEmbeddings()
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brand_checker = BrandStyleChecker()
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class CopyRequest(BaseModel):
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prompt: str
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content_type: str
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tone: Optional[str] = None
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target_audience: Optional[str] = None
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class CopyResponse(BaseModel):
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content: str
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confidence_score: float
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brand_alignment_score: float
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class DirectModelRequest(BaseModel):
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prompt: str
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max_length: Optional[int] = 200
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num_return_sequences: Optional[int] = 1
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temperature: Optional[float] = 0.7
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top_p: Optional[float] = 0.9
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class DirectModelResponse(BaseModel):
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generated_texts: List[str]
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@app.post("/generate-copy", response_model=CopyResponse)
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async def create_marketing_copy(request: CopyRequest):
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try:
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# Generate embeddings for the prompt
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prompt_embedding = embeddings.generate(request.prompt)
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# Retrieve similar content from vector store
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similar_content = vector_store.search(prompt_embedding)
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# Generate marketing copy
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content = generate_marketing_copy(
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prompt=request.prompt,
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content_type=request.content_type,
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similar_content=similar_content,
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tone=request.tone,
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target_audience=request.target_audience
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)
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# Check brand alignment
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brand_alignment = brand_checker.check_alignment(content)
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return CopyResponse(
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content=content,
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confidence_score=0.85, # This should be calculated based on model confidence
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brand_alignment_score=brand_alignment
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)
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except Exception as e:
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raise HTTPException(status_code=500, detail=str(e))
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@app.post("/direct-model", response_model=DirectModelResponse)
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async def direct_model_inference(request: DirectModelRequest):
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"""
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Direct inference using the finetuned model without using the vector store or other components.
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This endpoint is useful for testing the model directly.
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"""
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try:
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# Generate text using the finetuned model
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generated_texts = finetuned_model.generate(
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prompt=request.prompt,
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max_length=request.max_length,
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num_return_sequences=request.num_return_sequences,
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temperature=request.temperature,
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top_p=request.top_p
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)
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return DirectModelResponse(generated_texts=generated_texts)
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except Exception as e:
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raise HTTPException(status_code=500, detail=str(e))
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@app.get("/health")
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async def health_check():
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return {"status": "healthy"}
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if __name__ == "__main__":
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uvicorn.run("main:app", host="localhost", port=8000, reload=True) |