app.py

from fastapi import FastAPI, HTTPException, BackgroundTasks, UploadFile, File, Form
from fastapi.responses import FileResponse
from pydantic import BaseModel
from typing import Optional, Dict, Any, List
import uvicorn
import logging
import os
import pandas as pd
from datetime import datetime
import shutil
from pathlib import Path
import numpy as np
import json
import joblib
from sklearn.metrics import classification_report
from sklearn.multioutput import MultiOutputClassifier
from sklearn.feature_extraction.text import TfidfVectorizer
from models.tfidf_logreg import TfidfLogisticRegression


# Import existing utilities
from dataset_utils import (
    load_and_preprocess_data,
    save_label_encoders,
    load_label_encoders
)
from config import (
    TEXT_COLUMN,
    LABEL_COLUMNS,
    BATCH_SIZE,
    MODEL_SAVE_DIR
)
from models.tfidf_logreg import TfidfLogisticRegression

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

app = FastAPI(title="LOGREG Compliance Predictor API")

UPLOAD_DIR = Path("uploads")
MODEL_SAVE_DIR = Path("saved_models")
UPLOAD_DIR.mkdir(parents=True, exist_ok=True)
MODEL_SAVE_DIR.mkdir(parents=True, exist_ok=True)

# Define paths for vectorizer, model, and encoders
TFIDF_PATH = os.path.join(str(MODEL_SAVE_DIR), "tfidf_vectorizer.pkl")
MODEL_PATH = os.path.join(str(MODEL_SAVE_DIR), "logreg_models.pkl")
ENCODERS_PATH = os.path.join(os.path.dirname(__file__), "label_encoders.pkl")

training_status = {
    "is_training": False,
    "current_epoch": 0,
    "total_epochs": 0,
    "current_loss": 0.0,
    "start_time": None,
    "end_time": None,
    "status": "idle",
    "metrics": None
}

class TrainingConfig(BaseModel):
    batch_size: int = 32
    num_epochs: int = 1  # Not used for LGBM, but kept for API compatibility
    random_state: int = 42

class TrainingResponse(BaseModel):
    message: str
    training_id: str
    status: str
    download_url: Optional[str] = None

class ValidationResponse(BaseModel):
    message: str
    metrics: Dict[str, Any]
    predictions: List[Dict[str, Any]]

class TransactionData(BaseModel):
    Transaction_Id: str
    Hit_Seq: int
    Hit_Id_List: str
    Origin: str
    Designation: str
    Keywords: str
    Name: str
    SWIFT_Tag: str
    Currency: str
    Entity: str
    Message: str
    City: str
    Country: str
    State: str
    Hit_Type: str
    Record_Matching_String: str
    WatchList_Match_String: str
    Payment_Sender_Name: Optional[str] = ""
    Payment_Reciever_Name: Optional[str] = ""
    Swift_Message_Type: str
    Text_Sanction_Data: str
    Matched_Sanctioned_Entity: str
    Is_Match: int
    Red_Flag_Reason: str
    Risk_Level: str
    Risk_Score: float
    Risk_Score_Description: str
    CDD_Level: str
    PEP_Status: str
    Value_Date: str
    Last_Review_Date: str
    Next_Review_Date: str
    Sanction_Description: str
    Checker_Notes: str
    Sanction_Context: str
    Maker_Action: str
    Customer_ID: int
    Customer_Type: str
    Industry: str
    Transaction_Date_Time: str
    Transaction_Type: str
    Transaction_Channel: str
    Originating_Bank: str
    Beneficiary_Bank: str
    Geographic_Origin: str
    Geographic_Destination: str
    Match_Score: float
    Match_Type: str
    Sanctions_List_Version: str
    Screening_Date_Time: str
    Risk_Category: str
    Risk_Drivers: str
    Alert_Status: str
    Investigation_Outcome: str
    Case_Owner_Analyst: str
    Escalation_Level: str
    Escalation_Date: str
    Regulatory_Reporting_Flags: bool
    Audit_Trail_Timestamp: str
    Source_Of_Funds: str
    Purpose_Of_Transaction: str
    Beneficial_Owner: str
    Sanctions_Exposure_History: bool


class PredictionRequest(BaseModel):
    transaction_data: TransactionData
    model_name: str = "logreg_models"  # Default to tfidf_logreg if not specified

class BatchPredictionResponse(BaseModel):
    message: str
    predictions: List[Dict[str, Any]]
    metrics: Optional[Dict[str, Any]] = None

@app.get("/")
async def root():
    return {"message": "LOGREG Compliance Predictor API"}

@app.get("/v1/logreg/health")
async def health_check():
    return {"status": "healthy"}

@app.get("/v1/logreg/training-status")
async def get_training_status():
    return training_status

@app.post("/v1/logreg/train", response_model=TrainingResponse)
async def start_training(
    config: str = Form(...),
    background_tasks: BackgroundTasks = None,
    file: UploadFile = File(...)
):
    if training_status["is_training"]:
        raise HTTPException(status_code=400, detail="Training is already in progress")
    if not file.filename.endswith('.csv'):
        raise HTTPException(status_code=400, detail="Only CSV files are allowed")
    try:
        config_dict = json.loads(config)
        training_config = TrainingConfig(**config_dict)
    except Exception as e:
        raise HTTPException(status_code=400, detail=f"Invalid config parameters: {str(e)}")
    file_path = UPLOAD_DIR / file.filename
    with file_path.open("wb") as buffer:
        shutil.copyfileobj(file.file, buffer)
    training_id = datetime.now().strftime("%Y%m%d_%H%M%S")
    training_status.update({
        "is_training": True,
        "current_epoch": 0,
        "total_epochs": 1,
        "start_time": datetime.now().isoformat(),
        "status": "starting"
    })
    background_tasks.add_task(train_model_task, training_config, str(file_path), training_id)
    download_url = f"/v1/logreg/download-model/{training_id}"
    return TrainingResponse(
        message="Training started successfully",
        training_id=training_id,
        status="started",
        download_url=download_url
    )

@app.post("/v1/logreg/validate")
async def validate_model(
    file: UploadFile = File(...),
    model_name: str = "logreg_models"
):
    if not file.filename.endswith('.csv'):
        raise HTTPException(status_code=400, detail="Only CSV files are allowed")
    try:
        file_path = UPLOAD_DIR / file.filename
        with file_path.open("wb") as buffer:
            shutil.copyfileobj(file.file, buffer)
        data_df, label_encoders = load_and_preprocess_data(str(file_path))
        model_path = MODEL_SAVE_DIR / f"{model_name}.pkl"
        if not model_path.exists():
            raise HTTPException(status_code=404, detail="LOGREG model file not found")
        model = TfidfLOGREG(label_encoders)
        model.load_model(model_name)
        X = data_df[TEXT_COLUMN]
        y = data_df[LABEL_COLUMNS]
        # Type and shape check for X
        if not isinstance(X, pd.Series) or not pd.api.types.is_string_dtype(X):
            raise HTTPException(status_code=400, detail=f"TEXT_COLUMN ('{TEXT_COLUMN}') must be a pandas Series of strings. Got type: {type(X)}, dtype: {getattr(X, 'dtype', None)}")
        reports, y_true_list, y_pred_list = model.evaluate(X, y)
        all_probs = model.predict_proba(X)
        predictions = []
        for i, col in enumerate(LABEL_COLUMNS):
            label_encoder = label_encoders[col]
            true_labels_orig = label_encoder.inverse_transform(y_true_list[i])
            pred_labels_orig = label_encoder.inverse_transform(y_pred_list[i])
            for true, pred, probs in zip(true_labels_orig, pred_labels_orig, all_probs[i]):
                class_probs = {label: float(prob) for label, prob in zip(label_encoder.classes_, probs)}
                predictions.append({
                    "field": col,
                    "true_label": true,
                    "predicted_label": pred,
                    "probabilities": class_probs
                })
        return ValidationResponse(
            message="Validation completed successfully",
            metrics=reports,
            predictions=predictions
        )
    except Exception as e:
        logger.error(f"Validation failed: {str(e)}")
        raise HTTPException(status_code=500, detail=f"Validation failed: {str(e)}")
    finally:
        if os.path.exists(file_path):
            os.remove(file_path)

@app.post("/v1/logreg/predict")
async def predict(
    request: Optional[PredictionRequest] = None,
    file: UploadFile = File(None),
    model_name: str = "logreg_models"
):
    try:
        # Load vectorizer, model, and encoders
        tfidf = joblib.load(TFIDF_PATH)
        model = joblib.load(MODEL_PATH)
        encoders = joblib.load(ENCODERS_PATH)
        # Batch prediction from CSV
        if file and file.filename:
            if not file.filename.endswith('.csv'):
                raise HTTPException(status_code=400, detail="Only CSV files are allowed")
            file_path = UPLOAD_DIR / file.filename
            with file_path.open("wb") as buffer:
                shutil.copyfileobj(file.file, buffer)
            try:
                data_df, _ = load_and_preprocess_data(str(file_path))
                # Concatenate all fields into a single string for each row
                texts = data_df.apply(lambda row: " ".join([str(val) for val in row.values if pd.notna(val)]), axis=1)
                X_vec = tfidf.transform(texts)
                preds = model.predict(X_vec)
                predictions = []
                for i, pred in enumerate(preds):
                    decoded = {
                        col: encoders[col].inverse_transform([label])[0]
                        for col, label in zip(LABEL_COLUMNS, pred)
                    }
                    predictions.append({
                        "transaction_id": data_df.iloc[i].get('Transaction_Id', f"transaction_{i}"),
                        "predictions": decoded
                    })
                return BatchPredictionResponse(
                    message="Batch prediction completed successfully",
                    predictions=predictions
                )
            finally:
                if os.path.exists(file_path):
                    os.remove(file_path)
        # Single prediction
        elif request and request.transaction_data:
            input_data = pd.DataFrame([request.transaction_data.dict()])
            text_input = " ".join([
                str(val) for val in input_data.iloc[0].values if pd.notna(val)
            ])
            X_vec = tfidf.transform([text_input])
            pred = model.predict(X_vec)[0]
            decoded = {
                col: encoders[col].inverse_transform([p])[0]
                for col, p in zip(LABEL_COLUMNS, pred)
            }
            return decoded
        else:
            raise HTTPException(
                status_code=400,
                detail="Either provide a transaction in the request body or upload a CSV file"
            )
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

@app.get("/v1/logreg/download-model/{model_id}")
async def download_model(model_id: str):
    model_path = MODEL_SAVE_DIR / f"{model_id}.pkl"
    if not model_path.exists():
        raise HTTPException(status_code=404, detail="Model not found")
    return FileResponse(
        path=model_path,
        filename=f"logreg_model_{model_id}.pkl",
        media_type="application/octet-stream"
    )

async def train_model_task(config: TrainingConfig, file_path: str, training_id: str):
    try:
        data_df_original, label_encoders = load_and_preprocess_data(file_path)
        save_label_encoders(label_encoders)
        X = data_df_original[TEXT_COLUMN]
        y = data_df_original[LABEL_COLUMNS]
        model = TfidfXGB(label_encoders)
        model.train(X, y)
        model.save_model(training_id)
        training_status.update({
            "is_training": False,
            "end_time": datetime.now().isoformat(),
            "status": "completed"
        })
    except Exception as e:
        logger.error(f"Training failed: {str(e)}")
        training_status.update({
            "is_training": False,
            "end_time": datetime.now().isoformat(),
            "status": "failed",
            "error": str(e)
        })

if __name__ == "__main__":
    port = int(os.environ.get("PORT", 7860))
    uvicorn.run(app, host="0.0.0.0", port=port)