Updating to get the model imports to work

app.py

@@ -1,18 +1,19 @@
 import numpy as np
 from scipy import signal
-import huggingface_hub  # for loading model
+import math
+# import huggingface_hub  # for loading model
 import streamlit as st
 # from transformers import AutoModel
-from transformers import TFAutoModel
+# from transformers import TFAutoModel
 # Needed for importing torch to use in the transformers model
-import torch
-import tensorflow
+# import torch
+# import tensorflow
 import matplotlib.pyplot as plt
 # HELLO HUGGING FACE
 
 
 def basic_box_array(image_size):
-    A = numpy.zeros((int(image_size), int(image_size)))  # Initializes A matrix with 0 values
+    A = np.zeros((int(image_size), int(image_size)))  # Initializes A matrix with 0 values
     # Creates the outside edges of the box
     for i in range(image_size):
         for j in range(image_size):
@@ -22,7 +23,7 @@ def basic_box_array(image_size):
 
 
 def back_slash_array(image_size):
-    A = numpy.zeros((int(image_size), int(image_size)))  # Initializes A matrix with 0 values
+    A = np.zeros((int(image_size), int(image_size)))  # Initializes A matrix with 0 values
     for i in range(image_size):
         for j in range(image_size):
             if i == j:
@@ -31,7 +32,7 @@ def back_slash_array(image_size):
 
 
 def forward_slash_array(image_size):
-    A = numpy.zeros((int(image_size), int(image_size)))  # Initializes A matrix with 0 values
+    A = np.zeros((int(image_size), int(image_size)))  # Initializes A matrix with 0 values
     for i in range(image_size):
         for j in range(image_size):
             if i == (image_size - 1) - j:
@@ -41,7 +42,7 @@ def forward_slash_array(image_size):
 
 def hot_dog_array(image_size):
     # Places pixels down the vertical axis to split the box
-    A = numpy.zeros((int(image_size), int(image_size)))  # Initializes A matrix with 0 values
+    A = np.zeros((int(image_size), int(image_size)))  # Initializes A matrix with 0 values
     for i in range(image_size):
         for j in range(image_size):
             if j == math.floor((image_size - 1) / 2) or j == math.ceil((image_size - 1) / 2):
@@ -51,7 +52,7 @@ def hot_dog_array(image_size):
 
 def hamburger_array(image_size):
     # Places pixels across the horizontal axis to split the box
-    A = numpy.zeros((int(image_size), int(image_size)))  # Initializes A matrix with 0 values
+    A = np.zeros((int(image_size), int(image_size)))  # Initializes A matrix with 0 values
     for i in range(image_size):
         for j in range(image_size):
             if i == math.floor((image_size - 1) / 2) or i == math.ceil((image_size - 1) / 2):
@@ -60,7 +61,7 @@ def hamburger_array(image_size):
 
 
 def center_array(image_size):
-    A = numpy.zeros((int(image_size), int(image_size)))  # Initializes A matrix with 0 values
+    A = np.zeros((int(image_size), int(image_size)))  # Initializes A matrix with 0 values
     for i in range(image_size):
         for j in range(image_size):
             if i == math.floor((image_size - 1) / 2) and j == math.ceil((image_size - 1) / 2):
@@ -86,7 +87,7 @@ def update_array(array_original, array_new, image_size):
 def add_pixels(array_original, additional_pixels, image_size):
     # Adds pixels to the thickness of each component of the box
     A = array_original
-    A_updated = numpy.zeros((int(image_size), int(image_size)))  # Initializes A matrix with 0 values
+    A_updated = np.zeros((int(image_size), int(image_size)))  # Initializes A matrix with 0 values
     for dens in range(additional_pixels):
         for i in range(1, image_size - 1):
             for j in range(1, image_size - 1):
@@ -278,7 +279,6 @@ if st.button("Generate Endpoint Images"):
     plt.figure(1)
     st.pyplot(plt.figure(1))
 
-'''
 # Load the models from existing huggingface model
 # Load the encoder model
 # encoder_model_boxes = huggingface_hub.from_pretrained_keras("cmudrc/2d-lattice-encoder")
@@ -286,5 +286,5 @@ encoder_model = TFAutoModel.from_pretrained("cmudrc/2d-lattice-encoder")
 # Load the decoder model
 # decoder_model_boxes = huggingface_hub.from_pretrained_keras("cmudrc/2d-lattice-decoder")
 decoder_model = TFAutoModel.from_pretrained("cmudrc/2d-lattice-decoder")
-'''
+
 

requirements.txt

@@ -1,8 +1,4 @@
-huggingface_hub==0.12.0
 matplotlib==3.5.2
 numpy==1.21.5
 scipy==1.9.1
 streamlit==1.18.1
-tensorflow==2.10.0
-torch==2.0.0
-transformers==4.26.0