<Response [500]> Dict to pandas dataframe

Question

I'm trying to convert my pydantic model to a pandas dataframe so that it can be used by my tensorflow model but keep receiving a 500 response. The http error log says: ValueError: Input 0 of layer sequential is incompatible with the layer: expected axis -1 of input shape to have value 174 but received input with shape (None, 172). Here is my code:

from fastapi import FastAPI
import pandas as pd
import numpy as np
from pydantic import BaseModel
import uvicorn
import json
from fastapi.encoders import jsonable_encoder

from tensorflow.keras.models import load_model

app = FastAPI()
model = load_model('prediction')


class Userdata(BaseModel):
    RPPA_HSPA1A : float
    RPPA_XIAP : float
    RPPA_CASP7 : float
    RPPA_ERBB3 :float
    RPPA_SMAD1 : float
    RPPA_SYK : float
    RPPA_STAT5A : float
    RPPA_CD20 : float
    RPPA_AKT1_Akt :float
    RPPA_BAD : float
    RPPA_PARP1 : float
    RPPA_MSH2 : float
    RPPA_MSH6 : float
    RPPA_ACACA : float
    RPPA_COL6A1 : float
    RPPA_PTCH1 : float
    RPPA_AKT1 : float
    RPPA_CDKN1B : float
    RPPA_GATA3 : float
    RPPA_MAPT : float
    RPPA_TGM2 : float
    RPPA_CCNE1 : float
    RPPA_INPP4B : float
    RPPA_ACACA_ACC1 : float
    RPPA_RPS6 : float
    RPPA_VASP : float
    RPPA_CDH1 : float
    RPPA_EIF4EBP1 : float
    RPPA_CTNNB1 : float
    RPPA_XBP1 : float
    RPPA_EIF4EBP1_4E : float
    RPPA_PCNA : float
    RPPA_SRC : float
    RPPA_TP53BP1 : float
    RPPA_MAP2K1 : float
    RPPA_RAF1 : float
    RPPA_MET : float
    RPPA_TP53 : float
    RPPA_YAP1 : float
    RPPA_MAPK8 : float
    RPPA_CDKN1B_p27 : float
    RPPA_FRAP1 : float
    RPPA_RAD50 : float
    RPPA_CCNE2 : float
    RPPA_SNAI2 : float
    RPPA_PRKCA_PKC : float
    RPPA_PGR : float
    RPPA_ASNS : float
    RPPA_BID : float
    RPPA_CHEK2 : float
    RPPA_BCL2L1 : float
    RPPA_RPS6 : float
    RPPA_EGFR : float
    RPPA_PIK3CA : float
    RPPA_BCL2L11 : float
    RPPA_GSK3A : float
    RPPA_DVL3 : float
    RPPA_CCND1 : float
    RPPA_RAB11A : float
    RPPA_SRC_Src_pY416 :float
    RPPA_BCL2L111 : float
    RPPA_ATM : float
    RPPA_NOTCH1 : float
    RPPA_C12ORF5 : float
    RPPA_MAPK9 : float
    RPPA_FN1 : float
    RPPA_GSK3A_GSK3B : float
    RPPA_CDKN1B_p27_pT198 : float
    RPPA_MAP2K1_MEK1 : float
    RPPA_CASP8 : float
    RPPA_PAI : float
    RPPA_CHEK1 : float
    RPPA_STK11 : float
    RPPA_AKT1S1 : float
    RPPA_WWTR1 : float
    RPPA_CDKN1A : float
    RPPA_KDR : float
    RPPA_CHEK2_2 : float
    RPPA_EGFR_pY1173 : float
    RPPA_EGFR_pY992 : float
    RPPA_IGF1R : float
    RPPA_YWHAE : float
    RPPA_RPS6KA1 : float
    RPPA_TSC2 : float
    RPPA_CDC2 : float
    RPPA_EEF2 : float
    RPPA_NCOA3 : float
    RPPA_FRAP1 : float
    RPPA_AR : float
    RPPA_GAB2 : float
    RPPA_YBX1 : float
    RPPA_ESR1 : float
    RPPA_RAD51 : float
    RPPA_SMAD4 : float
    RPPA_CDH3 : float
    RPPA_CDH2 : float
    RPPA_FOXO3 : float
    RPPA_ERBB2_HER : float
    RPPA_BECN1 : float
    RPPA_CASP9 : float
    RPPA_SETD2 : float
    RPPA_SRC_Src_mv : float
    RPPA_GSK3A_alpha : float
    RPPA_YAP1_pS127 : float
    RPPA_PRKCA_alpha : float
    RPPA_PRKAA1 : float
    RPPA_RAF1_pS338 : float
    RPPA_MYC : float
    RPPA_PRKAA1_AMPK : float
    RPPA_ERRFI1_MIG : float
    RPPA_EIF4EBP1_2 : float
    RPPA_STAT3 : float
    RPPA_AKT1_AKT2_AKT3 : float
    RPPA_NF2 : float
    RPPA_PECAM1 : float
    RPPA_BAK1 : float
    RPPA_IRS1 : float
    RPPA_PTK2 : float
    RPPA_ERBB3_2 : float
    RPPA_FOXO3_a : float
    RPPA_RB1_Rb : float
    RPPA_MAPK14_p38 : float
    RPPA_NFKB1 : float
    RPPA_CHEK1_Chk1 : float
    RPPA_LCK : float
    RPPA_XRCC5 : float
    RPPA_PARK7 : float
    RPPA_DIABLO : float
    RPPA_CTNNA1 : float
    RPPA_ESR1_ER : float
    RPPA_IGFBP2 : float
    RPPA_STMN1 : float
    RPPA_WWTR1_TAZ : float
    RPPA_CASP3 : float
    RPPA_JUN : float
    RPPA_CCNB1 : float
    RPPA_CLDN7 : float
    RPPA_PXN : float
    RPPA_RPS6KB1_p : float
    RPPA_KIT : float
    RPPA_CAV1 : float
    RPPA_PTEN : float
    RPPA_BAX : float
    RPPA_SMAD3 : float
    RPPA_ERBB2 : float
    RPPA_MET_c : float
    RPPA_ERCC1 : float
    RPPA_MAPK14 : float
    RPPA_BIRC2 : float
    RPPA_PIK3R1 : float
    RPPA_BCL2 : float
    RPPA_PEA : float
    RPPA_EEF2K : float
    RPPA_RPS6KB1_p70 : float
    RPPA_MRE11A : float
    RPPA_KRAS : float
    RPPA_ARID1A : float
    RPPA_YBX1_yb : float
    RPPA_NOTCH3 : float
    RPPA_EIF4EBP1_3 : float
    RPPA_XRCC1 : float
    RPPA_ANXA1 : float
    RPPA_CD49 : float
    RPPA_SHC1 : float
    RPPA_PDK1 : float
    RPPA_EIF4E : float
    RPPA_MAPK1_MAPK3 : float
    RPPA_PTGS2 : float
    RPPA_PRKCA : float
    RPPA_EGFR_egfr : float
    RPPA_RAB25 : float
    RPPA_RB1 : float
    RPPA_MAPK1 : float
    RPPA_TFF1 : float
        
    class config:
        orm_mode = True
            

@app.post("/prediction/")
async def create_item(userdata: Userdata):
    data = userdata.dict()
    df = pd.DataFrame(data, index=[30])
    y = model.predict(df)
    y = [0 if val < 0.5 else 1 for val in y]
    if y == 1:
        survival = 'You will survive.'
    if y == 0:
        survival = 'You will not survive.'
    return {'Prediction': survival}

Answer 1

I think you need to change the way you are creating the DataFrame. But it depends on the shape that the DataFrame needs to have. Here is an example with a simplified BaseModel of only two fields:

import pandas
from pydantic import BaseModel


class Userdata(BaseModel):
    RPPA_HSPA1A: float
    RPPA_XIAP: float


userdata = Userdata(RPPA_HSPA1A=0.1, RPPA_XIAP=0.2)

# One row and many columns (horizontal) with field name as column and custom index
print(pandas.DataFrame(userdata.dict(), index=["value"]))

# Output:
#        RPPA_HSPA1A  RPPA_XIAP
# value          0.1        0.2

# One column and many rows (vertical) with field name as index and custom columns
print(pandas.DataFrame.from_dict(userdata.dict(), orient="index", columns=["value"]))

# Output:
#              value
# RPPA_HSPA1A    0.1
# RPPA_XIAP      0.2