Create app.py main script

app.py

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+#import gradio as gr
+from frame_semantic_transformer import FrameSemanticTransformer
+from transformers import T5ForConditionalGeneration, T5TokenizerFast
+from frame_semantic_transformer.data.frame_types import Frame
+from frame_semantic_transformer.data.loaders.loader import InferenceLoader
+import pandas as pd
+import ast
+import re
+from nltk.stem import SnowballStemmer
+
+# Huggingface model path
+huggingface_model_path = "nelsonjq/frame-semantic-transformer-french-small"
+
+# Load the model and tokenizer from Huggingface
+model = T5ForConditionalGeneration.from_pretrained(huggingface_model_path)
+tokenizer = T5TokenizerFast.from_pretrained(huggingface_model_path)
+
+# Load the DataFrame
+!mkdir -p Asfalda
+!wget --output-document Asfalda/frame_lus_df.tsv https://seafile.unistra.fr/f/0155ced00b8d441eb131/?dl=1
+frame_lus_df = pd.read_csv("Asfalda/frame_lus_df.tsv", delimiter='\t')
+
+# Filter out 'Other_sense' and normalize frame names
+frame_lus_df = frame_lus_df[frame_lus_df['Name'] != 'Other_sense']
+frame_lus_df['Name'] = frame_lus_df['Name'].apply(lambda x: x.split(".")[0])
+frame_lus_df['Name'] = frame_lus_df['Name'].str.replace(r'^[Ff][Rr][Vv]_', '', regex=True)
+
+# Add missing frame names
+new_row_Suasion = {
+    'Name': 'Suasion',
+    'Core_Elms': "['Content', 'Cognizer', 'Persuader', 'Target', 'Text', 'Action', 'Addressee']",
+    'Non_Core_Elms': "['Speaker', 'Topic']",
+    'Lus': "['convaincre.v', 'convertir.v', 'persuader.v', 'convaincant.a', 'persuasion.n', 'dissuader.v', 'apprendre.v', 'dissuasion.n', 'décider.v']",
+    'Lus_simple': "['convaincre', 'convertir', 'persuader', 'convaincant', 'persuasion', 'dissuader', 'apprendre', 'dissuasion', 'décider']"
+}
+
+new_row_Arriving = {
+    'Name': 'Arriving',
+    'Core_Elms': "['Theme', 'Goal']",
+    'Non_Core_Elms': "['Target', 'Means']",
+    'Lus': "['gagner.v']",
+    'Lus_simple': "['gagner']"
+}
+
+new_row_Suasion_df = pd.DataFrame([new_row_Suasion])
+new_row_Arriving_df = pd.DataFrame([new_row_Arriving])
+
+frame_lus_df = pd.concat([frame_lus_df, new_row_Suasion_df, new_row_Arriving_df], ignore_index=True)
+
+# Define FrenchInferenceLoader
+french_stemmer = SnowballStemmer("french")
+
+def extract_frame(df_row_frame) -> Frame:
+    name = df_row_frame['Name']
+    core_elms = ast.literal_eval(df_row_frame['Core_Elms'])
+    non_core_elms = ast.literal_eval(df_row_frame['Non_Core_Elms'])
+    lus = ast.literal_eval(df_row_frame['Lus'])
+    return Frame(name=name, core_elements=core_elms, non_core_elements=non_core_elms, lexical_units=lus)
+
+class FrenchInferenceLoader(InferenceLoader):
+    def __init__(self, french_framenet_df_file):
+        self.frames = []
+        for index, row in french_framenet_df_file.iterrows():
+            frame = extract_frame(row)
+            self.frames.append(frame)
+
+    def load_frames(self):
+        return self.frames
+
+    def normalize_lexical_unit_text(self, lu: str) -> str:
+        normalized_lu = lu.lower()
+        if '.' in normalized_lu:
+            normalized_lu = normalized_lu.split('.')[0]
+        normalized_lu = re.sub(r"[^a-z0-9 ]", "", normalized_lu)
+        return french_stemmer.stem(normalized_lu)
+
+# Initialize the FrenchInferenceLoader and FrameSemanticTransformer
+inference_loader = FrenchInferenceLoader(frame_lus_df)
+transformer = FrameSemanticTransformer(huggingface_model_path, inference_loader=inference_loader)
+
+# Function to process the input sentence and display frame detection results
+def detect_frames_in_text(input_text):
+    result = transformer.detect_frames(input_text)
+    output = f"Results found in the sentence:\n\n{result.sentence}\n"
+    for frame in result.frames:
+        output += f"\nFRAME: {frame.name}\n\nFrame Elements:\n"
+        for element in frame.frame_elements:
+            output += f"\t\t{element.name}: {element.text}\n"
+    return output
+
+# Gradio interface
+iface = gr.Interface(
+    fn=detect_frames_in_text,
+    inputs="text",
+    outputs="text",
+    title="French Frame Detection App",
+    description="Enter a French sentence to detect frames and frame elements using the FrameSemanticTransformer model."
+)
+
+# Launch the app
+iface.launch()