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File size: 2,802 Bytes
a309487 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 | from fastapi import FastAPI, UploadFile, File, HTTPException
import pandas as pd
from backend.core.orchestrator import Orchestrator
app = FastAPI()
orchestrator = Orchestrator()
@app.post("/analyze")
async def analyze_dataset(file: UploadFile = File(...), target_column: str = "target"):
try:
df = pd.read_csv(file.file)
result = orchestrator.run(df, target_column)
# Format response for frontend
dataset_info = result.get("dataset_info", {})
strategy = result.get("strategy", {})
response = {
"columns": list(df.columns),
"dataTypes": dataset_info.get("data_types", {}),
"risks": dataset_info.get("risks", []),
"problemType": result.get("problem_type"),
"confidence": strategy.get("confidence", 0),
"strategy": strategy
}
return response
except Exception as e:
raise HTTPException(status_code=400, detail=str(e))
@app.post("/train")
async def train_model(file: UploadFile = File(...), target_column: str = "target"):
try:
df = pd.read_csv(file.file)
result = orchestrator.run(df, target_column, train=True)
# Ensure strategy is included in the response
strategy = result.get("strategy", {})
response = {
"strategy": strategy,
"metrics": result.get("metrics", {}),
"model_path": result.get("model_path", "/path/to/model.pkl"),
"training_time": result.get("training_time", 0),
"model_id": result.get("model_id", "trained_model_123")
}
return response
except Exception as e:
raise HTTPException(status_code=400, detail=str(e))
@app.post("/explain")
async def explain_model(file: UploadFile = File(...), target_column: str = "target"):
try:
df = pd.read_csv(file.file)
result = orchestrator.run(df, target_column, train=True)
return {
"strategy_explanation": result.get("strategy_explanation"),
"metrics": result.get("metrics", {}),
"feature_importance": result.get("feature_importance", [])
}
except Exception as e:
raise HTTPException(status_code=400, detail=str(e))
@app.post("/predict")
async def predict(data: dict):
try:
# Load the trained model
model = orchestrator.model_io.load("exports/models/trained_model.pkl")
# Prepare data for prediction
df = pd.DataFrame([data])
preds = model.predict(df)
return {"prediction": preds.tolist()}
except Exception as e:
raise HTTPException(status_code=400, detail=str(e))
@app.get("/health")
def health():
return {"status": "ok"}
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