Update app.py

app.py

@@ -3,9 +3,11 @@ from transformers import pipeline
 import spacy
 from gradio_client import Client
 import re
+
 # Initialize models
 nlp = spacy.load("en_core_web_sm")
 spell_checker = pipeline("text2text-generation", model="oliverguhr/spelling-correction-english-base")
+
 def preprocess_capitalization(text: str) -> str:
     """Preprocess input text to handle capitalization rules."""
     words = text.split(" ")
@@ -22,6 +24,7 @@ def preprocess_capitalization(text: str) -> str:
             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 = {
@@ -30,6 +33,7 @@ def preprocess_text(text: str):
         "entities": [],
         "tags": []
     }
+
     # Apply capitalization preprocessing
     capitalized_text = preprocess_capitalization(text)
     if capitalized_text != text:
@@ -39,6 +43,7 @@ def preprocess_text(text: str):
             "type": "spell"
         })
         text = capitalized_text  # Update text for further processing
+
     # Transformer spell check - only for words that look misspelled
     spell_checked = spell_checker(text, max_length=512)[0]['generated_text']
     if spell_checked != text:
@@ -53,6 +58,7 @@ def preprocess_text(text: str):
                     "corrected": corrected,
                     "type": "spell"
                 })
+
     # Add fluency/style suggestions (other suggestions)
     # Only add if the word isn't already in spell suggestions
     spell_originals = {s["original"] for s in result["spell_suggestions"]}
@@ -70,11 +76,14 @@ def preprocess_text(text: str):
                 "corrected": word + "!",
                 "type": "other"
             })
+
     # Add entities and tags
     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(('#', '@'))]
+
     return text, result
+
 def translate_text(text: str):
     """Just translate the text without preprocessing."""
     client = Client("Frenchizer/space_21")
@@ -90,6 +99,7 @@ def translate_text(text: str):
         return translation, result
     except Exception as e:
         return f"Error: {str(e)}", {}
+
 def preprocess_and_forward(text: str):
     """Process text and forward to translation service."""
     original_text, preprocessing_result = preprocess_text(text)
@@ -101,139 +111,24 @@ def preprocess_and_forward(text: str):
         return translation, preprocessing_result
     except Exception as e:
         return f"Error: {str(e)}", preprocessing_result
+
 def translate_only(text: str):
     """Endpoint that only does translation without preprocessing or suggestions."""
     translation, empty_result = translate_text(text)
     return translation, empty_result
 
-# Function to format and display suggestions
-def format_suggestions(preprocessing_result):
-    if not preprocessing_result:
-        return "No suggestions available."
-    
-    formatted_output = []
-    
-    # Format spell suggestions
-    if preprocessing_result["spell_suggestions"]:
-        formatted_output.append("## Spelling Suggestions")
-        for suggestion in preprocessing_result["spell_suggestions"]:
-            formatted_output.append(f"• '{suggestion['original']}' → '{suggestion['corrected']}'")
-    
-    # Format other suggestions
-    if preprocessing_result["other_suggestions"]:
-        formatted_output.append("## Style Suggestions")
-        for suggestion in preprocessing_result["other_suggestions"]:
-            formatted_output.append(f"• '{suggestion['original']}' → '{suggestion['corrected']}'")
-    
-    # Format entities
-    if preprocessing_result["entities"]:
-        formatted_output.append("## Detected Entities")
-        for entity in preprocessing_result["entities"]:
-            formatted_output.append(f"• {entity['text']} ({entity['label']})")
-    
-    # Format tags
-    if preprocessing_result["tags"]:
-        formatted_output.append("## Detected Tags")
-        for tag in preprocessing_result["tags"]:
-            formatted_output.append(f"• {tag}")
-    
-    if not formatted_output:
-        return "No suggestions available."
-    
-    return "\n".join(formatted_output)
-
 # Gradio interface
-with gr.Blocks(title="Text Processor & Translator") as demo:
-    gr.Markdown("# Text Processing and Translation Tool")
-    gr.Markdown("Process, correct, and translate your text with advanced NLP features.")
-    
-    with gr.Tab("Full Processing"):
-        with gr.Row():
-            with gr.Column():
-                input_text_full = gr.Textbox(label="Input Text", placeholder="Enter text to process and translate...", lines=5)
-                process_translate_button = gr.Button("Process & Translate", variant="primary")
-            
-            with gr.Column():
-                output_text_full = gr.Textbox(label="Translated Text", lines=5)
-                suggestions_markdown = gr.Markdown(label="Suggestions")
-        
-        # Connect the full processing pipeline
-        def process_and_display(text):
-            translation, preprocessing_result = preprocess_and_forward(text)
-            suggestions = format_suggestions(preprocessing_result)
-            return translation, suggestions
-        
-        process_translate_button.click(
-            fn=process_and_display, 
-            inputs=[input_text_full], 
-            outputs=[output_text_full, suggestions_markdown]
-        )
-    
+with gr.Blocks() as demo:
+    with gr.Tab("Main"):
+        input_text = gr.Textbox(label="Input Text")
+        output_text = gr.Textbox(label="Output Text")
+        preprocess_button = gr.Button("Process and Translate")
+        preprocess_button.click(fn=preprocess_and_forward, inputs=input_text, outputs=output_text)
     with gr.Tab("Translation Only"):
-        with gr.Row():
-            with gr.Column():
-                input_text_translate = gr.Textbox(label="Input Text", placeholder="Enter text to translate only...", lines=5)
-                translate_button = gr.Button("Translate", variant="primary")
-            
-            with gr.Column():
-                output_text_translate = gr.Textbox(label="Translated Text", lines=5)
-        
-        # Connect translation-only pipeline
-        translate_button.click(
-            fn=translate_only,
-            inputs=[input_text_translate],
-            outputs=[output_text_translate]
-        )
-    
-    with gr.Tab("Text Analysis Only"):
-        with gr.Row():
-            with gr.Column():
-                input_text_analysis = gr.Textbox(label="Input Text", placeholder="Enter text to analyze...", lines=5)
-                analyze_button = gr.Button("Analyze Text", variant="primary")
-            
-            with gr.Column():
-                analysis_markdown = gr.Markdown(label="Analysis Results")
-        
-        # Connect analysis-only pipeline
-        def analyze_only(text):
-            _, preprocessing_result = preprocess_text(text)
-            suggestions = format_suggestions(preprocessing_result)
-            return suggestions
-        
-        analyze_button.click(
-            fn=analyze_only,
-            inputs=[input_text_analysis],
-            outputs=[analysis_markdown]
-        )
-    
-    with gr.Tab("Help"):
-        gr.Markdown("""
-        # How to Use This Tool
-        
-        This application provides three main functionalities:
-        
-        ## 1. Full Processing
-        - Corrects spelling and capitalization errors
-        - Identifies named entities and tags
-        - Offers style suggestions
-        - Translates the processed text to French
-        
-        ## 2. Translation Only
-        - Directly translates your text to French without any preprocessing
-        - Useful when you just need a quick translation
-        
-        ## 3. Text Analysis Only
-        - Analyzes your text for errors and improvement opportunities
-        - Identifies named entities and tags
-        - Doesn't perform any translation
-        
-        ## Features
-        - **Spelling Correction**: Identifies and corrects spelling errors
-        - **Capitalization Fixes**: Corrects improper capitalization while preserving acronyms
-        - **Style Suggestions**: Offers improvement suggestions for better writing
-        - **Entity Recognition**: Identifies people, organizations, locations, etc.
-        - **Tag Detection**: Finds hashtags and mentions in your text
-        """)
-
+        translate_input = gr.Textbox(label="Input Text")
+        translate_output = gr.Textbox(label="Output Text")
+        translate_button = gr.Button("Translate")
+        translate_button.click(fn=translate_only, inputs=translate_input, outputs=translate_output)
+                                
 if __name__ == "__main__":
     demo.launch()