Update app.py

app.py

@@ -1,42 +1,52 @@
-from processing import extract_text, preprocess_text_generalized, get_embeddings_from_huggingface
-import gradio as gr
-import numpy as np
-
-def process_file(file_path):
-    try:
-        # Step 1: Extract text
-        extracted_text = extract_text(file_path)
-        
-        # Step 2: Preprocess text
-        cleaned_text = preprocess_text_generalized(extracted_text)
-        
-        # Step 3: Generate embeddings
-        embeddings = get_embeddings_from_huggingface(cleaned_text)
-        
-        # Step 4: Save embeddings to a temporary file
-        temp_file_path = "embeddings.npy"
-        np.save(temp_file_path, embeddings)
-        
-        # Return the top 10 embeddings and the file path for download
-        top_10_embeddings = embeddings[:10].tolist()
-        return f"Top 10 Embeddings: {top_10_embeddings}", temp_file_path
-    except Exception as e:
-        return str(e), None
-
-# Define Gradio Interface
-interface = gr.Interface(
-    fn=process_file,
-    inputs=gr.File(label="Upload a file (CSV, PDF, JSON)", type="filepath"),
-    outputs=[
-        gr.Textbox(label="Top 10 Embeddings"),
-        gr.File(label="Download Full Embeddings"),
-    ],
-    title="Embedding Converter Using Hugging Face Model",
-    description=(
-        "Upload a file (CSV, PDF, or JSON) to  generate embeddings using "
-        "Hugging Face models. View the top 10 embeddings and download  entire embedding file."
-    ),
-)
-
-if __name__ == "__main__":
-    interface.launch()
+from processing import extract_text, preprocess_text_generalized, get_embeddings_from_huggingface
+import gradio as gr
+import numpy as np
+import spacy
+import os
+
+# Check if SpaCy model is downloaded; if not, download it
+try:
+    nlp = spacy.load("en_core_web_sm")
+except OSError:
+    os.system("python -m spacy download en_core_web_sm")
+    nlp = spacy.load("en_core_web_sm")
+
+
+def process_file(file_path):
+    try:
+        # Step 1: Extract text
+        extracted_text = extract_text(file_path)
+        
+        # Step 2: Preprocess text
+        cleaned_text = preprocess_text_generalized(extracted_text)
+        
+        # Step 3: Generate embeddings
+        embeddings = get_embeddings_from_huggingface(cleaned_text)
+        
+        # Step 4: Save embeddings to a temporary file
+        temp_file_path = "embeddings.npy"
+        np.save(temp_file_path, embeddings)
+        
+        # Return the top 10 embeddings and the file path for download
+        top_10_embeddings = embeddings[:10].tolist()
+        return f"Top 10 Embeddings: {top_10_embeddings}", temp_file_path
+    except Exception as e:
+        return str(e), None
+
+# Define Gradio Interface
+interface = gr.Interface(
+    fn=process_file,
+    inputs=gr.File(label="Upload a file (CSV, PDF, JSON)", type="filepath"),
+    outputs=[
+        gr.Textbox(label="Top 10 Embeddings"),
+        gr.File(label="Download Full Embeddings"),
+    ],
+    title="Embedding Converter Using Hugging Face Model",
+    description=(
+        "Upload a file (CSV, PDF, or JSON) to  generate embeddings using "
+        "Hugging Face models. View the top 10 embeddings and download  entire embedding file."
+    ),
+)
+
+if __name__ == "__main__":
+    interface.launch()