Update app.py

app.py

@@ -28,29 +28,37 @@ def preprocess_capitalization(text: str) -> str:
 def preprocess_text(text: str):
     """Process text and return corrections with position information."""
     result = {
-        "spell_suggestions": [],
-        "entities": [],
-        "tags": []
+        "suggestions": []  # Rename to generic "suggestions" for flexibility
     }
 
-    # Apply capitalization preprocessing
+    # Apply capitalization preprocessing (Spell type)
     capitalized_text = preprocess_capitalization(text)
     if capitalized_text != text:
-        result["spell_suggestions"].append({
+        result["suggestions"].append({
             "original": text,
-            "corrected": capitalized_text
+            "corrected": capitalized_text,
+            "type": "spell"  # Add type
         })
-        text = capitalized_text  # Update text for further processing
+        text = capitalized_text
 
-    # Transformer spell check
+    # Transformer spell check (Spell type)
     spell_checked = spell_checker(text, max_length=512)[0]['generated_text']
     if spell_checked != text:
-        result["spell_suggestions"].append({
+        result["suggestions"].append({
             "original": text,
-            "corrected": spell_checked
+            "corrected": spell_checked,
+            "type": "spell"  # Add type
         })
 
-    # Add entities and tags
+    # Example "other" suggestion (e.g., adding punctuation)
+    if not text.endswith("!") and text.strip():
+        result["suggestions"].append({
+            "original": text,
+            "corrected": text + "!",
+            "type": "other"  # Add type for non-spell suggestions
+        })
+
+    # Add entities and tags (unchanged)
     doc = nlp(text)
     result["entities"] = [{"text": ent.text, "label": ent.label_} for ent in doc.ents]
     result["tags"] = [token.text for token in doc if token.text.startswith(('#', '@'))]
@@ -60,16 +68,14 @@ def preprocess_text(text: str):
 def preprocess_and_forward(text: str):
     """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")
+    client = Client("Frenchizer/space_17")
     try:
         translation = client.predict(original_text)
         return translation, preprocessing_result
     except Exception as e:
         return f"Error: {str(e)}", preprocessing_result
 
-# Gradio interface
+# Update Gradio outputs to match
 with gr.Blocks() as demo:
     input_text = gr.Textbox(label="Input Text")
     output_text = gr.Textbox(label="Output Text")