0fffbcfba4
* Added backend infrastructure (FastAPI, Dockerfile, requirements) * Set up frontend (Streamlit, Dockerfile, requirements) * Configured docker-compose with Milvus and Flowise services * Created project structure with .env.template and .dockerignore * Added initial gitignore and project metadata
67 lines
2.1 KiB
Python
67 lines
2.1 KiB
Python
from fastapi import FastAPI, APIRouter, HTTPException
|
|
import uuid
|
|
|
|
app = FastAPI(title="Retro API", description="Retro content management system")
|
|
|
|
# Routers
|
|
import_router = APIRouter(prefix="/import", tags=["Import"])
|
|
analyse_router = APIRouter(prefix="/analyse", tags=["Analyse"])
|
|
export_router = APIRouter(prefix="/export", tags=["Export"])
|
|
generate_router = APIRouter(prefix="/generate", tags=["Generate"])
|
|
|
|
# Sample data storage (replace with Milvus/DB integration)
|
|
data_store = {}
|
|
|
|
# Import Router
|
|
@import_router.post("/data")
|
|
def import_data(data: dict):
|
|
"""
|
|
Import data (e.g., text, files, or structured data).
|
|
"""
|
|
data_id = str(uuid.uuid4())
|
|
data_store[data_id] = data
|
|
return {"message": "Data imported successfully", "data_id": data_id}
|
|
|
|
# Analyse Router
|
|
@analyse_router.get("/{data_id}")
|
|
def analyse_data(data_id: str):
|
|
"""
|
|
Analyse imported data (e.g., sentiment, keywords, or patterns).
|
|
"""
|
|
if data_id not in data_store:
|
|
raise HTTPException(status_code=404, detail="Data not found")
|
|
# Placeholder for analysis logic
|
|
return {
|
|
"data_id": data_id,
|
|
"analysis": {"word_count": len(data_store[data_id].get("text", ""))},
|
|
}
|
|
|
|
# Export Router
|
|
@export_router.get("/{data_id}")
|
|
def export_data(data_id: str):
|
|
"""
|
|
Export analysed data (e.g., as JSON, CSV, or PDF).
|
|
"""
|
|
if data_id not in data_store:
|
|
raise HTTPException(status_code=404, detail="Data not found")
|
|
return {
|
|
"data_id": data_id,
|
|
"content": data_store[data_id],
|
|
"status": "exported",
|
|
}
|
|
|
|
# Generate Router
|
|
@generate_router.post("/content")
|
|
def generate_content(prompt: str):
|
|
"""
|
|
Generate new content (e.g., text, images, or summaries).
|
|
"""
|
|
# Placeholder for generation logic (e.g., LLM, AI model)
|
|
generated = {"response": f"Generated content based on: {prompt}"}
|
|
return {"message": "Content generated", "data": generated}
|
|
|
|
# Include routers in the main app
|
|
app.include_router(import_router)
|
|
app.include_router(analyse_router)
|
|
app.include_router(export_router)
|
|
app.include_router(generate_router)
|