Created site

README.md

@@ -1,10 +1,10 @@
 ---
-title: Dictabert Joint Demo
-emoji: 🦀
-colorFrom: green
-colorTo: purple
+title: Syntax Tester
+emoji: 📚
+colorFrom: purple
+colorTo: gray
 sdk: streamlit
-sdk_version: 1.30.0
+sdk_version: 1.29.0
 app_file: app.py
 pinned: false
 license: cc-by-4.0

app.py

@@ -0,0 +1,110 @@
+import streamlit as st
+import base64
+from transformers import AutoModel, AutoTokenizer
+from graphviz import Digraph
+import json
+
+def display_tree(output):
+    size = str(int(len(output))) + ',5'
+    dpi = '300'
+    format = 'svg'
+    print(size, dpi)
+    
+    # Initialize Digraph object
+    dot = Digraph(engine='dot', format=format)
+    dot.attr('graph', rankdir='LR', rank='same', size=size, dpi=dpi)
+    
+    # Add nodes and edges
+    for i,word_info in enumerate(output):
+        word = word_info['word']  # Prepare word for RTL display
+        head_idx = word_info['dep_head_idx']
+        dep_func = word_info['dep_func']
+        
+        dot.node(str(i), word)
+        # Create an invisible edge from the previous word to this one to enforce order
+        if i > 0:
+            dot.edge(str(i), str(i - 1), style='invis')
+        if head_idx != -1:
+            dot.edge(str(i), str(head_idx), label=dep_func, constraint='False')
+
+
+    # Render the Digraph object
+    dot.render('syntax_tree', format=format, cleanup=True)
+    # Display the image in a scrollable container
+    st.markdown(
+        f"""
+            <div style="height:250px; width:75vw; overflow:auto; border:1px solid #ccc; margin-left:-15vw">
+                <img src="data:image/svg+xml;base64,{base64.b64encode(dot.pipe(format='svg')).decode()}" 
+                    style="display: block; margin: auto; max-height: 240px;">
+            </div>
+        """, unsafe_allow_html=True)
+    
+    #st.image('syntax_tree.' + format, use_column_width=True)
+        
+# Streamlit app title
+st.title('BERT Syntax Dependency Tree Visualizer')
+
+# Load Hugging Face token
+hf_token = st.secrets["HF_TOKEN"]  # Assuming you've set up the token in Streamlit secrets
+
+# Authenticate and load model
+tokenizer = AutoTokenizer.from_pretrained('dicta-il/dictabert-joint', use_auth_token=hf_token)
+model = AutoModel.from_pretrained('dicta-il/dictabert-joint', use_auth_token=hf_token, trust_remote_code=True)
+
+model.eval()
+
+# Checkbox for the compute_mst parameter
+compute_mst = st.checkbox('Compute Maximum Spanning Tree', value=True)
+
+output_style = st.selectbox(
+    'Output Style: ',
+    ('JSON', 'UD', 'IAHLT_UD'), index=1).lower()
+
+# User input
+sentence = st.text_input('Enter a sentence to analyze:')
+
+if sentence:
+    # Display the input sentence
+    st.text(sentence)
+
+    # Model prediction
+    output = model.predict([sentence], tokenizer, compute_syntax_mst=compute_mst, output_style=output_style)[0]
+    
+    if output_style == 'ud' or output_style == 'iahlt_ud':
+        ud_output = output
+        # convert to tree format of [dict(word, dep_head_idx, dep_func)]
+        tree = []
+        for l in ud_output[2:]:
+            parts = l.split('\t')
+            if '-' in parts[0]: continue
+            tree.append(dict(word=parts[1], dep_head_idx=int(parts[6]) - 1, dep_func=parts[7]))
+        display_tree(tree)
+
+        # Construct the table as a Markdown string
+        table_md = "<div dir='rtl' style='text-align: right;'>\n\n"  # Start with RTL div
+        
+        # Add the UD header lines
+        table_md += "##" + ud_output[0] + "\n"
+        table_md += "##" + ud_output[1] + "\n"
+        # Table header
+        table_md += "| " + " | ".join(["ID", "FORM", "LEMMA", "UPOS", "XPOS", "FEATS", "HEAD", "DEPREL", "DEPS", "MISC"]) + " |\n"
+        # Table alignment
+        table_md += "| " + " | ".join(["---"]*10) + " |\n"
+        for line in ud_output[2:]:
+            # Each UD line as a table row
+            cells = line.replace('_', '\\_').replace('|', '&#124;').split('\t')
+            table_md += "| " + " | ".join(cells) + " |\n"
+        table_md += "</div>"  # Close the RTL div
+        
+        # Display the table using a single markdown call
+        st.markdown(table_md, unsafe_allow_html=True)
+
+    else:
+        # display the tree
+        tree = [w['syntax'] for w in output['tokens']]
+        display_tree(tree)
+        
+        # and the full json
+        st.markdown("```json\n" + json.dumps(output, ensure_ascii=False, indent=2) + "\n```")
+
+

packages.txt

@@ -0,0 +1 @@
+graphviz

requirements.txt

@@ -0,0 +1,7 @@
+--extra-index-url https://download.pytorch.org/whl/cpu
+streamlit
+transformers
+torch
+graphviz
+arabic_reshaper
+python-bidi