diff --git a/src/model_fit_api/app.py b/src/model_fit_api/app.py
index f6e3b3b..731815d 100644
--- a/src/model_fit_api/app.py
+++ b/src/model_fit_api/app.py
@@ -1,5 +1,5 @@
 import json
-from typing import Literal, List, Dict
+from typing import Literal, List, Dict, Optional
 
 import numpy as np
 import pandas as pd
@@ -8,58 +8,54 @@
 from fastapi import FastAPI
 from pydantic import BaseModel
 
-models = [
-    "nugent-sn1a",
-    "nugent-sn91t",
-    "nugent-sn91bg",
-    "nugent-sn1bc",
-    "nugent-hyper",
-    "nugent-sn2n",
-    "nugent-sn2p",
-    "nugent-sn2l",
-    "salt2",
-    "salt3-nir",
-    "salt3",
-    "snf-2011fe",
-    "v19-1993j",
-    "v19-1998bw",
-    "v19-1999em",
-    "v19-2009ip",
-]
+
 app = FastAPI()
 
 
 class Observation(BaseModel):
-    mjd: float
-    flux: float
-    fluxerr: float
-    zp: float = 8.9
+    mjd: Optional[float]
+    band: Optional[str]
+    flux: Optional[float]
+    fluxerr: Optional[float]
+    zp: Optional[float] = 8.9
     zpsys: Literal["ab", "vega"] = "ab"
-    band: str
 
 
 class Target(BaseModel):
     light_curve: List[Observation]
-    ebv: float
-    t_min: float
-    t_max: float
-    count: int
-    name_model: str
+    ebv: Optional[float]
+    t_min: Optional[float]
+    t_max: Optional[float]
+    count: Optional[int]
+    name_model: Optional[str]
     redshift: List[float]
 
 
+class Model_data(BaseModel):
+    parameters: Dict[str, float] 
+    name_model: str 
+    zp: float 
+    zpsys: str 
+    band_list: List[str] 
+    t_min: float 
+    t_max: float 
+    count: int
+
 class Point(BaseModel):
-    time: float
-    flux: float
-    band: str
+    time: Optional[float]
+    difflux: Optional[float]
+    band: Optional[str]
 
 
-class Result(BaseModel):
-    flux_jansky: List[Point]
+class Parameters(BaseModel):
     degrees_of_freedom: int
     covariance: List[List[float]]
     chi2: float
     parameters: Dict[str, float]
+    
+    
+class Flux(BaseModel):
+    flux_jansky: List[Point]
 
 
 def fit(data, name_model, ebv, redshift):
@@ -72,36 +68,66 @@ def fit(data, name_model, ebv, redshift):
     return summary, fitted_model
 
 
-def get_flux_and_params(summary, data, fitted_model, t_min, t_max, count):
-    segment = np.linspace(t_min, t_max, count)
-    df = data.to_pandas()
+def get_flux(data: Model_data):
+    dust = sncosmo.CCM89Dust()
+    fitted_model = sncosmo.Model(source=data.name_model, effects=[dust], effect_names=["mw"], effect_frames=["obs"])
+    fitted_model.set(**data.parameters)
+    segment = np.linspace(data.t_min, data.t_max, data.count)
     points = []
-    for band in df["band"].unique():
-        predicts = fitted_model.bandflux(band, segment, df["zp"][0], df["zpsys"][0])
+    for band in data.band_list:
+        predicts = fitted_model.bandflux(band, segment, data.zp, data.zpsys)
         points += [Point(time=time, flux=flux, band=band) for time, flux in zip(segment, predicts)]
-    return Result(
-        flux_jansky=points,
+    return Flux(
+        flux_jansky=points
+    )
+    
+    
+def get_params(data: Target):
+    df = pd.DataFrame([dict(obs) for obs in data.light_curve])
+    table = Table.from_pandas(df)
+    summary, fitted_model = fit(table, data.name_model, data.ebv, data.redshift)
+    try: cov=summary.covariance.tolist() 
+    except: 
+        cov=[[]] 
+        print('covariance is none')
+    return Parameters(
         parameters=dict(zip(summary.param_names, summary.parameters)),
         degrees_of_freedom=summary.ndof,
-        covariance=summary.covariance.tolist(),
+        covariance=cov,
         chi2=summary.chisq,
     )
 
 
-def approximate(data: Target):
-    df = pd.DataFrame([obs.model_dump() for obs in data.light_curve])
-    table = Table.from_pandas(df)
-    summary, fitted_model = fit(table, data.name_model, data.ebv, data.redshift)
-    result = get_flux_and_params(summary, table, fitted_model, data.t_min, data.t_max, data.count)
-    return result
-
-
-@app.post("/api/v1/sncosmo")
+@app.post("/api/v1/sncosmo/fit")
 async def sn_cosmo(data: Target):
     """Fit light curve with sncosmo."""
-    return approximate(data)
+    return get_params(data)
+    
+
+@app.post("/api/v1/sncosmo/get_bright")
+async def sn_cosmo(data: Model_data):
+    """Fit light curve with sncosmo."""
+    return get_flux(data)
 
 
 @app.get("/api/v1/models")
-async def models(data: Target):
+async def models():
+    models = [
+    "nugent-sn1a",
+    "nugent-sn91t",
+    "nugent-sn91bg",
+    "nugent-sn1bc",
+    "nugent-hyper",
+    "nugent-sn2n",
+    "nugent-sn2p",
+    "nugent-sn2l",
+    "salt2",
+    "salt3-nir",
+    "salt3",
+    "snf-2011fe",
+    "v19-1993j",
+    "v19-1998bw",
+    "v19-1999em",
+    "v19-2009ip",
+    ]
     return {"models": models}