Create app.py

app.py

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+import gradio as gr
+from hazm import Normalizer, word_tokenize, Lemmatizer, POSTagger, Chunker, DependencyParser
+
+# Initialize Hazm components
+normalizer = Normalizer()
+lemmatizer = Lemmatizer()
+tagger = POSTagger(model='resources/postagger.model')
+chunker = Chunker(model='resources/chunker.model')
+parser = DependencyParser(tagger=tagger, lemmatizer=lemmatizer)
+
+def process_text(text, operations):
+    result = {}
+    if 'normalize' in operations:
+        text = normalizer.normalize(text)
+        result['Normalized Text'] = text
+    if 'tokenize' in operations:
+        tokens = word_tokenize(text)
+        result['Tokens'] = tokens
+    if 'lemmatize' in operations:
+        lemmas = [lemmatizer.lemmatize(token) for token in word_tokenize(text)]
+        result['Lemmas'] = lemmas
+    if 'pos_tag' in operations:
+        pos_tags = tagger.tag(word_tokenize(text))
+        result['POS Tags'] = pos_tags
+    if 'chunk' in operations:
+        pos_tags = tagger.tag(word_tokenize(text))
+        chunks = chunker.parse(pos_tags)
+        result['Chunks'] = str(chunks)
+    if 'dependency_parse' in operations:
+        parse_tree = parser.parse(word_tokenize(text))
+        result['Dependency Parse'] = str(parse_tree)
+    return result
+
+# Define Gradio interface
+operations = ['normalize', 'tokenize', 'lemmatize', 'pos_tag', 'chunk', 'dependency_parse']
+iface = gr.Interface(
+    fn=process_text,
+    inputs=[
+        gr.inputs.Textbox(lines=10, label="Input Text"),
+        gr.inputs.CheckboxGroup(operations, label="Operations")
+    ],
+    outputs="json",
+    title="Persian Text Processor with Hazm",
+    description="Select operations to perform on the input text using Hazm."
+)
+
+if __name__ == "__main__":
+    iface.launch()