1st commit!! :D

app.py

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+from typing import Dict, Tuple
+from tqdm import tqdm
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.utils.data import DataLoader
+from torchvision import models, transforms
+from torchvision.utils import save_image, make_grid
+import matplotlib.pyplot as plt
+from matplotlib.animation import FuncAnimation, PillowWriter
+import numpy as np
+from IPython.display import HTML
+from diffusion_utilities import *
+
+openai.api_key  = os.getenv('OPENAI_API_KEY')
+
+class ContextUnet(nn.Module):
+    def __init__(self, in_channels, n_feat=256, n_cfeat=10, height=28):  # cfeat - context features
+        super(ContextUnet, self).__init__()
+
+        # number of input channels, number of intermediate feature maps and number of classes
+        self.in_channels = in_channels
+        self.n_feat = n_feat
+        self.n_cfeat = n_cfeat
+        self.h = height  #assume h == w. must be divisible by 4, so 28,24,20,16...
+
+        # Initialize the initial convolutional layer
+        self.init_conv = ResidualConvBlock(in_channels, n_feat, is_res=True)
+
+        # Initialize the down-sampling path of the U-Net with two levels
+        self.down1 = UnetDown(n_feat, n_feat)        # down1 #[10, 256, 8, 8]
+        self.down2 = UnetDown(n_feat, 2 * n_feat)    # down2 #[10, 256, 4,  4]
+
+         # original: self.to_vec = nn.Sequential(nn.AvgPool2d(7), nn.GELU())
+        self.to_vec = nn.Sequential(nn.AvgPool2d((4)), nn.GELU())
+
+        # Embed the timestep and context labels with a one-layer fully connected neural network
+        self.timeembed1 = EmbedFC(1, 2*n_feat)
+        self.timeembed2 = EmbedFC(1, 1*n_feat)
+        self.contextembed1 = EmbedFC(n_cfeat, 2*n_feat)
+        self.contextembed2 = EmbedFC(n_cfeat, 1*n_feat)
+
+        # Initialize the up-sampling path of the U-Net with three levels
+        self.up0 = nn.Sequential(
+            nn.ConvTranspose2d(2 * n_feat, 2 * n_feat, self.h//4, self.h//4),
+            nn.GroupNorm(8, 2 * n_feat), # normalize
+            nn.ReLU(),
+        )
+        self.up1 = UnetUp(4 * n_feat, n_feat)
+        self.up2 = UnetUp(2 * n_feat, n_feat)
+
+        # Initialize the final convolutional layers to map to the same number of channels as the input image
+        self.out = nn.Sequential(
+            nn.Conv2d(2 * n_feat, n_feat, 3, 1, 1), # reduce number of feature maps   #in_channels, out_channels, kernel_size, stride=1, padding=0
+            nn.GroupNorm(8, n_feat), # normalize
+            nn.ReLU(),
+            nn.Conv2d(n_feat, self.in_channels, 3, 1, 1), # map to same number of channels as input
+        )
+
+    def forward(self, x, t, c=None):
+        """
+        x : (batch, n_feat, h, w) : input image
+        t : (batch, n_cfeat)      : time step
+        c : (batch, n_classes)    : context label
+        """
+        # x is the input image, c is the context label, t is the timestep, context_mask says which samples to block the context on
+
+        # pass the input image through the initial convolutional layer
+        x = self.init_conv(x)
+        # pass the result through the down-sampling path
+        down1 = self.down1(x)       #[10, 256, 8, 8]
+        down2 = self.down2(down1)   #[10, 256, 4, 4]
+
+        # convert the feature maps to a vector and apply an activation
+        hiddenvec = self.to_vec(down2)
+
+        # mask out context if context_mask == 1
+        if c is None:
+            c = torch.zeros(x.shape[0], self.n_cfeat).to(x)
+
+        # embed context and timestep
+        cemb1 = self.contextembed1(c).view(-1, self.n_feat * 2, 1, 1)     # (batch, 2*n_feat, 1,1)
+        temb1 = self.timeembed1(t).view(-1, self.n_feat * 2, 1, 1)
+        cemb2 = self.contextembed2(c).view(-1, self.n_feat, 1, 1)
+        temb2 = self.timeembed2(t).view(-1, self.n_feat, 1, 1)
+        #print(f"uunet forward: cemb1 {cemb1.shape}. temb1 {temb1.shape}, cemb2 {cemb2.shape}. temb2 {temb2.shape}")
+
+
+        up1 = self.up0(hiddenvec)
+        up2 = self.up1(cemb1*up1 + temb1, down2)  # add and multiply embeddings
+        up3 = self.up2(cemb2*up2 + temb2, down1)
+        out = self.out(torch.cat((up3, x), 1))
+        return out
+
+# hyperparameters
+
+# diffusion hyperparameters
+timesteps = 500
+beta1 = 1e-4
+beta2 = 0.02
+
+# network hyperparameters
+device = torch.device("cuda:0" if torch.cuda.is_available() else torch.device('cpu'))
+n_feat = 64 # 64 hidden dimension feature
+n_cfeat = 5 # context vector is of size 5
+height = 16 # 16x16 image
+save_dir = './weights/'
+
+# training hyperparameters
+batch_size = 100
+n_epoch = 32
+lrate=1e-3
+
+# construct DDPM noise schedule
+b_t = (beta2 - beta1) * torch.linspace(0, 1, timesteps + 1, device=device) + beta1
+a_t = 1 - b_t
+ab_t = torch.cumsum(a_t.log(), dim=0).exp()
+ab_t[0] = 1
+
+# construct model
+nn_model = ContextUnet(in_channels=3, n_feat=n_feat, n_cfeat=n_cfeat, height=height).to(device)
+
+def greet(input):
+	prompt = f"""
+Recommend complementary shop combinations which match well with the shop(s) described in the following text, which is delimited by triple backticks. Rank by synergy: \
+Text: ```{input}```
+    """
+	response = prompt
+	return response
+
+#iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+#iface.launch()
+
+#iface = gr.Interface(fn=greet, inputs=[gr.Textbox(label="Text to find entities", lines=2)], outputs=[gr.HighlightedText(label="Text with entities")], title="NER with dslim/bert-base-NER", description="Find entities using the `dslim/bert-base-NER` model under the hood!", allow_flagging="never", examples=["My name is Andrew and I live in California", "My name is Poli and work at HuggingFace"])
+iface = gr.Interface(fn=greet, inputs=[gr.Textbox(label="Co-Retailing Business")], outputs="text")
+iface.launch()

requirements.txt

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+openai
+python-dotenv