code clean up on backend ec2, push up to end of SP23

.gitignore

@@ -6,7 +6,8 @@ __pycache__/
 ml-model/formula_images/
 ml-model/output/
 ml-model/crop_formula_images/
-
+ml-model/yolov5/config.py
+.vscode/
 
 output.zip
 *.zip
@@ -19,6 +20,7 @@ data/
 simese_data/
 venv/
 ml-model/model.pt
+ml-model/yolov5/preprocess_data/
 training_data/
 *.png
 im2latex/
@@ -29,3 +31,4 @@ venv/
 ml-model/paths_output.csv
 
 ml-model/web/__pycache__/
+datasets/

ml-model/model/image_to_latex.py

@@ -0,0 +1,22 @@
+from rapid_latex_ocr import LatexOCR
+
+image_resizer_path = 'models/image_resizer.onnx'
+encoder_path = 'models/encoder.onnx'
+decoder_path = 'models/decoder.onnx'
+tokenizer_json = 'models/tokenizer.json'
+model = LatexOCR(image_resizer_path=image_resizer_path,
+                encoder_path=encoder_path,
+                decoder_path=decoder_path,
+                tokenizer_json=tokenizer_json)
+
+img_path = "tests/test_files/6.png"
+with open(img_path, "rb") as f:
+    data = f. read()
+
+result, elapse = model(data)
+
+print(result)
+# {\frac{x^{2}}{a^{2}}}-{\frac{y^{2}}{b^{2}}}=1
+
+print(elapse)
+# 0.4131628000000003

ml-model/model/latex_to_tree.py

@@ -0,0 +1,93 @@
+import sympy as sp
+from sympy.parsing.latex import parse_latex
+from zss import Node, distance
+import networkx as nx
+import matplotlib.pyplot as plt
+
+
+def zss_to_nx(node, graph=None, parent=None):
+    if graph is None:
+        graph = nx.DiGraph()
+    graph.add_node(id(node), label=node.label)
+    if parent is not None:
+        graph.add_edge(id(parent), id(node))
+    for child in node.children:
+        zss_to_nx(child, graph, node)
+    return graph
+
+
+# Define some complex LaTeX expressions
+# expr1 represents our query
+latex_expr1 = r"\nabla J(\theta) = \frac{1}{m} \sum_{i=1}^m (h_\theta(x^{(i)}) - y^{(i)}) x^{(i)}"
+# expr represents our OCR'd expression from the file
+latex_expr2 = r"\nabla J(\Theta) = \frac{1}{m} \sum_{i=1}^m (h_\theta(z^{(i)}) - y^{(i)}) z^{(i)}"
+
+# Convert LaTeX to SymPy
+sympy_expr1 = parse_latex(latex_expr1)
+sympy_expr2 = parse_latex(latex_expr2)
+
+
+def sympy_to_zss(expr):
+    if isinstance(expr, sp.Symbol) or isinstance(expr, sp.Number):
+        return Node(str(expr))
+    else:
+        node = Node(str(expr.func))
+        for arg in expr.args:
+            child_node = sympy_to_zss(arg)
+            node.addkid(child_node)
+        return node
+
+
+# Convert the SymPy expression to a ZSS tree
+zss_tree1 = sympy_to_zss(sympy_expr1)
+zss_tree2 = sympy_to_zss(sympy_expr2)
+# print(zss_tree1)
+# print(zss_tree2)
+
+# Assuming zss_tree1 and zss_tree2 are your ZSS trees
+nx_tree1 = zss_to_nx(zss_tree1)
+nx_tree2 = zss_to_nx(zss_tree2)
+
+
+def hierarchy_pos(G, root=None, width=1., vert_gap=0.2, vert_loc=0, xcenter=0.5):
+    pos = _hierarchy_pos(G, root, width, vert_gap, vert_loc, xcenter)
+    return pos
+
+
+def _hierarchy_pos(G, root, width=1., vert_gap=0.2, vert_loc=0, xcenter=0.5, pos=None, parent=None, parsed=[]):
+    if pos is None:
+        pos = {root: (xcenter, vert_loc)}
+    else:
+        pos[root] = (xcenter, vert_loc)
+    children = list(G.neighbors(root))
+    if not isinstance(G, nx.DiGraph) and parent is not None:
+        children.remove(parent)
+    if len(children) != 0:
+        dx = width / len(children)
+        nextx = xcenter - width/2 - dx/2
+        for child in children:
+            nextx += dx
+            pos = _hierarchy_pos(G, child, width=dx, vert_gap=vert_gap,
+                                 vert_loc=vert_loc-vert_gap, xcenter=nextx,
+                                 pos=pos, parent=root, parsed=parsed)
+    return pos
+
+
+def draw_tree(tree):
+    pos = hierarchy_pos(tree, root=list(tree.nodes())
+                        [0])  # Specify the root node
+    labels = nx.get_node_attributes(tree, 'label')
+    nx.draw(tree, pos, labels=labels, with_labels=True,
+            node_size=3000, node_color='lightblue', font_size=10)
+    plt.show()
+
+
+# Draw the trees
+# draw_tree(nx_tree1)
+# draw_tree(nx_tree2)
+
+# Compare ZSS trees
+# make update non-zero to see difference in more updated tree vs. not
+distance = distance(zss_tree1, zss_tree2, get_children=Node.get_children,
+                    insert_cost=lambda node: 10, remove_cost=lambda node: 10, update_cost=lambda a, b: 1)
+print(distance)  # Output the tree edit distance