Update app.py

app.py

@@ -2,40 +2,31 @@ import gradio as gr
 from transformers import pipeline
 import spacy
 from textblob import TextBlob
-import json
 import requests
 from gradio_client import Client
 
 # Initialize models
-nlp = spacy.load("en_core_web_sm")  # Use "en_core_web_trf" if more accuracy is needed
+nlp = spacy.load("en_core_web_trf")  # More accurate NER
 spell_checker = pipeline("text2text-generation", model="oliverguhr/spelling-correction-english-base")
 
-def preprocess_and_forward(text: str) -> dict:
+def preprocess_and_forward(text: str) -> str:
     """
     Processes the input text and forwards it to the Gradio client for space_17.
+    Returns only the final translated text.
     """
-    # Preprocess the text
-    processed_text, preprocessing_results = preprocess_text(text)
-
-    # Instantiate the Gradio Client for space_17
-    client = Client("Frenchizer/space_17")
-
-    # Forward preprocessed text to the Gradio client for space_17
-    try:
-        context_response = client.predict(processed_text)
-        preprocessing_results["context_response"] = context_response
-    except Exception as e:
-        preprocessing_results["context_error"] = str(e)
-
-    return preprocessing_results
+    processed_text, _ = preprocess_text(text)
+    return forward_to_translation(processed_text)
 
 def preprocess_text(text: str):
+    """
+    Applies spell-checking and named entity recognition (NER) to preprocess text.
+    """
     result = {
         "spell_suggestions": [],
         "entities": [],
         "tags": []
     }
-    
+
     # Basic spell checking using TextBlob
     corrected_text = str(TextBlob(text).correct())
     if corrected_text != text:
@@ -59,14 +50,28 @@ def preprocess_text(text: str):
     # Extract potential tags (hashtags, mentions, etc.)
     result["tags"] = [token.text for token in doc if token.text.startswith(('#', '@'))]
 
-    return text
+    # Choose the best-corrected version
+    final_text = spell_checked if spell_checked != text else corrected_text
+
+    return final_text, result
+
+def forward_to_translation(text: str) -> str:
+    """
+    Sends preprocessed text to space_17 for translation and returns only the translated text.
+    """
+    client = Client("Frenchizer/space_17")
+
+    try:
+        return client.predict(text)
+    except Exception as e:
+        return f"Error: {str(e)}"
 
 # Gradio interface
 with gr.Blocks() as demo:
     input_text = gr.Textbox(label="Input Text")
-    output_json = gr.JSON(label="Processing Results")
+    output_text = gr.Textbox(label="Output Text")  # Returns only text
     preprocess_button = gr.Button("Process")
-    preprocess_button.click(fn=preprocess_and_forward, inputs=[input_text], outputs=[output_json])
+    preprocess_button.click(fn=preprocess_and_forward, inputs=[input_text], outputs=[output_text])
 
 if __name__ == "__main__":
     demo.launch()