import timm
import torch
import torch.nn.functional as nnf
import gradio as gr
import numpy as np
import pandas as pd
import json


model = torch.load("/home/user/app/model_30c4tc4y_scripted.pkl",map_location=torch.device('cpu')) 
model.eval()

with open('/home/user/app/val.json', 'r') as handle:
    parsed = json.load(handle)
    
classes = []
for i in range(len(parsed["categories"])):
  if parsed["categories"][i]['supercategory'] == 'Plants':
    classes.append(parsed["categories"][i]['name'])
    
classes = set(classes)

classes = list(classes)
classes.sort()

labels = classes

def classify_image(inp):
  #print(inp.shape)
  inp = inp.astype(np.uint8).reshape((-1, 3, 300, 300))
  #print(inp.shape)
  inp = torch.from_numpy(inp).float()
  #confidences = model(inp)

  preds = model(inp).data[0]
  # means = preds.mean(dim=0, keepdim=True)
  # stds = preds.std(dim=0, keepdim=True)
  # preds = 10 * (preds - means) / stds
  # #preds = nnf.normalize(model(inp).data[0], dim=0)
  # preds = nnf.softmax(preds, dim=0)
  preds = [pred.cpu() for pred in preds]
  preds = [float(pred.detach()) for pred in preds]
  p_min = min(preds)
  p_max = max(preds)
  preds = [(pred+p_min)/(p_max+p_min) for pred in preds]
  print(pd.Series(preds).describe())

  #confidences_dict = {classes[i]: float(confidences.data[0][i]) for i in range(len(confidences.data[0]))}
  confidences_dict = {classes[i]: float(preds[i]) for i in range(len(preds))}

  return confidences_dict

gr.Interface(fn=classify_image, 
             inputs=gr.Image(shape=(300, 300)),
             outputs=gr.Label(num_top_classes=3)).launch(debug = True)