From f53bab65367180277e6e53712fd3d3ec5d911dc8 Mon Sep 17 00:00:00 2001
From: "Esmail A.Gumaan" <113830751+Esmail-ibraheem@users.noreply.github.com>
Date: Tue, 16 Apr 2024 10:26:51 +0300
Subject: [PATCH] Create app.py

---
 app.py | 79 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 79 insertions(+)
 create mode 100644 app.py

diff --git a/app.py b/app.py
new file mode 100644
index 0000000..8e85874
--- /dev/null
+++ b/app.py
@@ -0,0 +1,79 @@
+import torch 
+import torch.nn as nn 
+from torch.distributions.uniform import Uniform
+import pandas as pd 
+import matplotlib.pyplot as plt 
+import seaborn as sns
+import streamlit as st
+
+# Streamlit UI for user input
+st.title('Word Embedding Visualization')
+
+# Function to get user input
+def get_user_input():
+    tokens = []
+    for i in range(4):
+        token = st.text_input(f"Enter token {i+1}")
+        tokens.append(token)
+    return tokens
+
+# Get user input
+tokens = get_user_input()
+
+# Define inputs and labels tensors
+inputs = torch.tensor([[1, 0, 0, 0], 
+                       [0, 1, 0, 0],
+                       [0, 0, 1, 0],
+                       [0, 0, 0, 1]])
+
+labels = torch.tensor([[0, 1, 0, 0], 
+                       [0, 0, 1, 0],
+                       [0, 0, 0, 1],
+                       [0, 1, 0, 0]])
+
+# Define the WordEmbeddingFromScratch model class
+class WordEmbeddingFromScratch(nn.Module):
+    def __init__(self) -> None:
+        super().__init__()
+        min_value = -0.5 
+        max_value = 0.5 
+
+        self.input_w1 = nn.Parameter(Uniform(min_value, max_value).sample((4,)))
+        self.input_w2 = nn.Parameter(Uniform(min_value, max_value).sample((4,)))
+        self.output_w1 = nn.Parameter(Uniform(min_value, max_value).sample((4,)))
+        self.output_w2 = nn.Parameter(Uniform(min_value, max_value).sample((4,)))
+
+    def forward(self, input):
+        input = input[0]
+
+        inputs_to_top_hidden = torch.matmul(input, self.input_w1)
+        inputs_to_bottom_hidden = torch.matmul(input, self.input_w2)
+        
+        output = (inputs_to_top_hidden[:, None] * self.output_w1) + \
+                 (inputs_to_bottom_hidden[:, None] * self.output_w2)
+        
+        return output
+
+# Create an instance of the model
+model = WordEmbeddingFromScratch()
+
+# Plot the embeddings
+plt.figure(figsize=(8, 8))
+
+for i, token in enumerate(tokens):
+    plt.text(model.output_w1[i].item(), model.output_w2[i].item(), token, 
+             horizontalalignment='left', 
+             size='medium', 
+             color='black',
+             weight='semibold')
+
+plt.scatter(model.output_w1.detach().numpy(), model.output_w2.detach().numpy())
+plt.xlabel('w1')
+plt.ylabel('w2')
+plt.title('Word Embeddings')
+st.pyplot()
+
+# Display the model's output for the entered tokens
+with torch.no_grad():
+    outputs = model(inputs)
+    print(torch.softmax(outputs, dim=1))