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app.py

@@ -90,6 +90,7 @@ def model(prompt):
     highlighted_accepted_sentences = highlight_common_words_dict(common_grams, selected_sentences, "Paraphrased Sentences")
     highlighted_discarded_sentences = highlight_common_words_dict(common_grams, discarded_sentences, "Discarded Sentences")
 
+
     # Initialize empty list to hold the trees
     trees = []
 

tree.py

@@ -1,3 +1,161 @@
+# import plotly.graph_objects as go
+# import textwrap
+# import re
+# from collections import defaultdict
+
+# def generate_subplot(paraphrased_sentence, scheme_sentences, sampled_sentence, highlight_info):
+#     # Combine nodes into one list with appropriate labels
+#     nodes = [paraphrased_sentence] + scheme_sentences + sampled_sentence
+#     nodes[0] += ' L0'  # Paraphrased sentence is level 0
+#     para_len = len(scheme_sentences)
+#     for i in range(1, para_len + 1):
+#         nodes[i] += ' L1'  # Scheme sentences are level 1
+#     for i in range(para_len + 1, len(nodes)):
+#         nodes[i] += ' L2'  # Sampled sentences are level 2
+
+#     # Define the highlight_words function
+#     def highlight_words(sentence, color_map):
+#         for word, color in color_map.items():
+#             sentence = re.sub(f"\\b{word}\\b", f"{{{{{word}}}}}", sentence, flags=re.IGNORECASE)
+#         return sentence
+
+#     # Clean and wrap nodes, and highlight specified words globally
+#     cleaned_nodes = [re.sub(r'\sL[0-9]$', '', node) for node in nodes]
+#     global_color_map = dict(highlight_info)
+#     highlighted_nodes = [highlight_words(node, global_color_map) for node in cleaned_nodes]
+#     wrapped_nodes = ['<br>'.join(textwrap.wrap(node, width=30)) for node in highlighted_nodes]
+
+#     # Function to determine tree levels and create edges dynamically
+#     def get_levels_and_edges(nodes):
+#         levels = {}
+#         edges = []
+#         for i, node in enumerate(nodes):
+#             level = int(node.split()[-1][1])
+#             levels[i] = level
+
+#         # Add edges from L0 to all L1 nodes
+#         root_node = next(i for i, level in levels.items() if level == 0)
+#         for i, level in levels.items():
+#             if level == 1:
+#                 edges.append((root_node, i))
+
+#         # Add edges from each L1 node to their corresponding L2 nodes
+#         l1_indices = [i for i, level in levels.items() if level == 1]
+#         l2_indices = [i for i, level in levels.items() if level == 2]
+
+#         for i, l1_node in enumerate(l1_indices):
+#             l2_start = i * 4
+#             for j in range(4):
+#                 l2_index = l2_start + j
+#                 if l2_index < len(l2_indices):
+#                     edges.append((l1_node, l2_indices[l2_index]))
+
+#         # Add edges from each L2 node to their corresponding L3 nodes
+#         l2_indices = [i for i, level in levels.items() if level == 2]
+#         l3_indices = [i for i, level in levels.items() if level == 3]
+
+#         l2_to_l3_map = {l2_node: [] for l2_node in l2_indices}
+
+#         # Map L3 nodes to L2 nodes
+#         for l3_node in l3_indices:
+#             l2_node = l3_node % len(l2_indices)
+#             l2_to_l3_map[l2_indices[l2_node]].append(l3_node)
+
+#         for l2_node, l3_nodes in l2_to_l3_map.items():
+#             for l3_node in l3_nodes:
+#                 edges.append((l2_node, l3_node))
+
+#         return levels, edges
+
+#     # Get levels and dynamic edges
+#     levels, edges = get_levels_and_edges(nodes)
+#     max_level = max(levels.values(), default=0)
+
+#     # Calculate positions
+#     positions = {}
+#     level_heights = defaultdict(int)
+#     for node, level in levels.items():
+#         level_heights[level] += 1
+
+#     y_offsets = {level: - (height - 1) / 2 for level, height in level_heights.items()}
+#     x_gap = 2
+#     l1_y_gap = 10
+#     l2_y_gap = 6
+
+#     for node, level in levels.items():
+#         if level == 1:
+#             positions[node] = (-level * x_gap, y_offsets[level] * l1_y_gap)
+#         elif level == 2:
+#             positions[node] = (-level * x_gap, y_offsets[level] * l2_y_gap)
+#         else:
+#             positions[node] = (-level * x_gap, y_offsets[level] * l2_y_gap)
+#         y_offsets[level] += 1
+
+#     # Function to highlight words in a wrapped node string
+#     def color_highlighted_words(node, color_map):
+#         parts = re.split(r'(\{\{.*?\}\})', node)
+#         colored_parts = []
+#         for part in parts:
+#             match = re.match(r'\{\{(.*?)\}\}', part)
+#             if match:
+#                 word = match.group(1)
+#                 color = color_map.get(word, 'black')
+#                 colored_parts.append(f"<span style='color: {color};'>{word}</span>")
+#             else:
+#                 colored_parts.append(part)
+#         return ''.join(colored_parts)
+
+#     # Create figure
+#     fig = go.Figure()
+
+#     # Add nodes to the figure
+#     for i, node in enumerate(wrapped_nodes):
+#         colored_node = color_highlighted_words(node, global_color_map)
+#         x, y = positions[i]
+#         fig.add_trace(go.Scatter(
+#             x=[-x],  # Reflect the x coordinate
+#             y=[y],
+#             mode='markers',
+#             marker=dict(size=10, color='blue'),
+#             hoverinfo='none'
+#         ))
+#         fig.add_annotation(
+#             x=-x,  # Reflect the x coordinate
+#             y=y,
+#             text=colored_node,
+#             showarrow=False,
+#             xshift=15,
+#             align="center",
+#             font=dict(size=8),
+#             bordercolor='black',
+#             borderwidth=1,
+#             borderpad=2,
+#             bgcolor='white',
+#             width=150
+#         )
+
+#     # Add edges to the figure
+#     for edge in edges:
+#         x0, y0 = positions[edge[0]]
+#         x1, y1 = positions[edge[1]]
+#         fig.add_trace(go.Scatter(
+#             x=[-x0, -x1],  # Reflect the x coordinates
+#             y=[y0, y1],
+#             mode='lines',
+#             line=dict(color='black', width=1)
+#         ))
+
+#     fig.update_layout(
+#         showlegend=False,
+#         margin=dict(t=20, b=20, l=20, r=20),
+#         xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
+#         yaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
+#         width=1200,  # Adjusted width to accommodate more levels
+#         height=1000   # Adjusted height to accommodate more levels
+#     )
+
+#     return fig
+
 import plotly.graph_objects as go
 import textwrap
 import re
@@ -105,6 +263,25 @@ def generate_subplot(paraphrased_sentence, scheme_sentences, sampled_sentence, h
                 colored_parts.append(part)
         return ''.join(colored_parts)
 
+    # Define the text for each edge
+    edge_texts = [
+        "Highest Entropy Masking",
+        "Pseudo-random Masking",
+        "Random Masking",
+        "Greedy Sampling",
+        "Temperature Sampling",
+        "Exponential Minimum Sampling",
+        "Inverse Transform Sampling",
+        "Greedy Sampling",
+        "Temperature Sampling",
+        "Exponential Minimum Sampling",
+        "Inverse Transform Sampling",
+        "Greedy Sampling",
+        "Temperature Sampling",
+        "Exponential Minimum Sampling",
+        "Inverse Transform Sampling"
+    ]
+
     # Create figure
     fig = go.Figure()
 
@@ -134,8 +311,8 @@ def generate_subplot(paraphrased_sentence, scheme_sentences, sampled_sentence, h
             width=150
         )
 
-    # Add edges to the figure
-    for edge in edges:
+    # Add edges and text above each edge
+    for i, edge in enumerate(edges):
         x0, y0 = positions[edge[0]]
         x1, y1 = positions[edge[1]]
         fig.add_trace(go.Scatter(
@@ -145,6 +322,23 @@ def generate_subplot(paraphrased_sentence, scheme_sentences, sampled_sentence, h
             line=dict(color='black', width=1)
         ))
 
+        # Calculate the midpoint of the edge
+        mid_x = (-x0 + -x1) / 2
+        mid_y = (y0 + y1) / 2
+
+        # Adjust y position to shift text upwards
+        text_y_position = mid_y + 0.8  # Increase this value to shift the text further upwards
+
+        # Add text annotation above the edge
+        fig.add_annotation(
+            x=mid_x,
+            y=text_y_position,
+            text=edge_texts[i],  # Use the text specific to this edge
+            showarrow=False,
+            font=dict(size=10),
+            align="center"
+        )
+
     fig.update_layout(
         showlegend=False,
         margin=dict(t=20, b=20, l=20, r=20),
@@ -154,4 +348,5 @@ def generate_subplot(paraphrased_sentence, scheme_sentences, sampled_sentence, h
         height=1000   # Adjusted height to accommodate more levels
     )
 
-    return fig
+    return fig
+