Update app.py

app.py

@@ -3,7 +3,6 @@ from transformers import pipeline
 import spacy
 from gradio_client import Client
 import re
-import httpx
 
 # Initialize models
 nlp = spacy.load("en_core_web_sm")
@@ -15,50 +14,52 @@ def preprocess_capitalization(text: str) -> str:
     processed_words = []
     
     for word in words:
+        # Check if the word is an acronym (all uppercase letters)
         if re.match(r"^[A-Z]+$", word):
             processed_words.append(word)  # Leave acronyms unchanged
+        # Check if the word has mixed capitalization (e.g., "HEllo")
         elif re.search(r"[A-Z]", word) and re.search(r"[a-z]", word):
             processed_words.append(word[0].upper() + word[1:].lower())  # Correct capitalization
         else:
-            processed_words.append(word)
+            processed_words.append(word)  # Leave other words unchanged
     
     return " ".join(processed_words)
 
 def preprocess_text(text: str):
     """Process text and return corrections with position information."""
     result = {
-        "suggestions": []
+        "spell_suggestions": [],
+        "other_suggestions": [],
+        "entities": [],
+        "tags": []
     }
 
-    # Step 1: Apply capitalization preprocessing (Spell type)
+    # Apply capitalization preprocessing
     capitalized_text = preprocess_capitalization(text)
     if capitalized_text != text:
-        result["suggestions"].append({
+        result["spell_suggestions"].append({
             "original": text,
             "corrected": capitalized_text,
             "type": "spell"
         })
         text = capitalized_text  # Update text for further processing
 
-    # Step 2: Transformer spell check (Spell type)
-    spell_checked = spell_checker(text, max_length=512)[0]['generated_text'].strip()
-    # Only add as a spell suggestion if it’s a true spelling change (not just punctuation)
-    if spell_checked != text and spell_checked.rstrip('!.') == text.rstrip('!.'):
-        # If the difference is only punctuation, skip adding it here
-        pass
-    elif spell_checked != text:
-        result["suggestions"].append({
+    # Transformer spell check
+    spell_checked = spell_checker(text, max_length=512)[0]['generated_text']
+    if spell_checked != text:
+        result["spell_suggestions"].append({
             "original": text,
             "corrected": spell_checked,
             "type": "spell"
         })
-        text = spell_checked  # Update text only for true spell corrections
 
-    # Step 3: Add "other" suggestion (e.g., punctuation)
-    if not text.endswith("!") and text.strip():
-        result["suggestions"].append({
-            "original": text,
-            "corrected": text + "!",
+    # Add example other suggestions (you would replace this with your actual logic)
+    for word in text.split():
+        if word.endswith('.') or word.endswith('?') or word.endswith('!'):
+            continue
+        result["other_suggestions"].append({
+            "original": word,
+            "corrected": word + "!",
             "type": "other"
         })
 
@@ -70,15 +71,14 @@ def preprocess_text(text: str):
     return text, result
 
 def preprocess_and_forward(text: str):
-    """Process text and forward to translation service with custom timeout."""
+    """Process text and forward to translation service."""
     original_text, preprocessing_result = preprocess_text(text)
     
+    # Forward original text to translation service
+    client = Client("Frenchizer/space_21")
     try:
-        client = Client("Frenchizer/space_21", httpx_timeout=httpx.Timeout(60.0))
         translation = client.predict(original_text)
         return translation, preprocessing_result
-    except httpx.ReadTimeout:
-        return "Error: Translation service timed out after 60 seconds. Please try again later.", preprocessing_result
     except Exception as e:
         return f"Error: {str(e)}", preprocessing_result