Create app.py

app.py

@@ -0,0 +1,59 @@
+import spacy
+import os
+import shutil
+import gradio as gr
+from booknlp.booknlp import BookNLP
+from spacy.cli import download
+
+# Ensure Spacy model is downloaded
+def ensure_spacy_model():
+    try:
+        # Try to load the model to see if it's already installed
+        spacy.load("en_core_web_sm")
+    except OSError:
+        # If not installed, download the model
+        download("en_core_web_sm")
+
+# Initialize Spacy model
+ensure_spacy_model()
+
+# Initialize BookNLP model
+model_params = {
+    "pipeline": "entity,quote,supersense,event,coref", 
+    "model": "big"
+}
+booknlp = BookNLP("en", model_params)
+
+# Define function to process file
+def process_book(file):
+    input_file = file.name
+    output_dir = "output_dir/booknlp_output/"
+    book_id = os.path.splitext(os.path.basename(input_file))[0]
+    
+    # Create output directory if it doesn't exist
+    if os.path.exists(output_dir):
+        shutil.rmtree(output_dir)
+    os.makedirs(output_dir)
+
+    # Run BookNLP
+    booknlp.process(input_file, output_dir, book_id)
+    
+    # Zip the output folder
+    shutil.make_archive(f"{output_dir}/{book_id}_output", 'zip', output_dir)
+    return f"{output_dir}/{book_id}_output.zip"
+
+# Gradio Interface
+def gradio_interface():
+    file_input = gr.inputs.File(file_types=['.txt'], label="Upload a .txt file")
+    file_output = gr.outputs.File(label="Download the output files")
+    
+    gr.Interface(
+        fn=process_book, 
+        inputs=file_input, 
+        outputs=file_output, 
+        title="BookNLP Processor",
+        description="Upload a .txt book and process it using BookNLP. Download the processed output files."
+    ).launch()
+
+if __name__ == "__main__":
+    gradio_interface()