Delete app.py

app.py

@@ -1,322 +0,0 @@
-import gradio as gr
-import spaces
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-import torch
-from huggingface_hub import InferenceClient
-import os
-import fitz  # PyMuPDF for PDF processing
-from PIL import Image
-import pytesseract
-
-# Initialize Cerebras client for Llama 4
-cerebras_client = InferenceClient(
-    "meta-llama/Llama-4-Scout-17B-16E-Instruct",
-    provider="cerebras",
-    token=os.getenv("HF_TOKEN"),
-)
-
-# Global variables for models and tokenizers
-en_es_tokenizer = None
-en_es_model = None
-es_en_tokenizer = None
-es_en_model = None
-
-@spaces.GPU(duration=60)
-def translate_en_to_es(text):
-    global en_es_tokenizer, en_es_model
-    
-    # Initialize EN->ES model if needed
-    if en_es_tokenizer is None or en_es_model is None:
-        en_es_tokenizer = AutoTokenizer.from_pretrained("facebook/nllb-200-distilled-600M", src_lang="eng_Latn", tgt_lang="spa_Latn")
-        en_es_model = AutoModelForSeq2SeqLM.from_pretrained(
-            "facebook/nllb-200-distilled-600M",
-            torch_dtype=torch.float16
-        ).cuda()
-    
-    # Translate
-    inputs = en_es_tokenizer(text, return_tensors="pt", max_length=512, truncation=True).to("cuda")
-    with torch.no_grad():
-        outputs = en_es_model.generate(
-            **inputs,
-            forced_bos_token_id=en_es_tokenizer.convert_tokens_to_ids("spa_Latn"),
-            max_length=512,
-            num_beams=5,
-            early_stopping=True
-        )
-    
-    translation = en_es_tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return translation
-
-@spaces.GPU(duration=60)
-def translate_es_to_en(text):
-    global es_en_tokenizer, es_en_model
-    
-    # Initialize ES->EN model if needed
-    if es_en_tokenizer is None or es_en_model is None:
-        es_en_tokenizer = AutoTokenizer.from_pretrained("facebook/nllb-200-distilled-600M", src_lang="spa_Latn", tgt_lang="eng_Latn")
-        es_en_model = AutoModelForSeq2SeqLM.from_pretrained(
-            "facebook/nllb-200-distilled-600M",
-            torch_dtype=torch.float16
-        ).cuda()
-    
-    # Translate
-    inputs = es_en_tokenizer(text, return_tensors="pt", max_length=512, truncation=True).to("cuda")
-    with torch.no_grad():
-        outputs = es_en_model.generate(
-            **inputs,
-            forced_bos_token_id=es_en_tokenizer.convert_tokens_to_ids("eng_Latn"),
-            max_length=512,
-            num_beams=5,
-            early_stopping=True
-        )
-    
-    translation = es_en_tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return translation
-
-def extract_text_from_pdf(file_path):
-    """Extract text from PDF file"""
-    try:
-        doc = fitz.open(file_path)
-        text = ""
-        for page in doc:
-            text += page.get_text()
-        doc.close()
-        return text
-    except Exception as e:
-        return f"Error extracting text from PDF: {str(e)}"
-
-def extract_text_from_image(file_path):
-    """Extract text from image using OCR"""
-    try:
-        image = Image.open(file_path)
-        text = pytesseract.image_to_string(image)
-        return text
-    except Exception as e:
-        return f"Error extracting text from image: {str(e)}"
-
-def process_uploaded_file(file):
-    """Process uploaded file and extract text"""
-    if file is None:
-        return "No file uploaded"
-    
-    file_path = file.name
-    file_extension = os.path.splitext(file_path)[1].lower()
-    
-    if file_extension == '.pdf':
-        return extract_text_from_pdf(file_path)
-    elif file_extension in ['.png', '.jpg', '.jpeg', '.tiff', '.bmp']:
-        return extract_text_from_image(file_path)
-    else:
-        return "Unsupported file format. Please upload PDF or image files."
-
-def refine_with_llama(original_text, translation, direction, region="Mexico", formality="neutral"):
-    if direction == "en_to_es":
-        refine_prompt = f"""You are an expert Spanish translator specializing in {region} Spanish. Refine the following translation and explain your changes:
-
-Original English: {original_text}
-Initial Spanish translation: {translation}
-Region: {region}
-Formality level: {formality}
-
-Requirements:
-1. Use {region} Spanish vocabulary and expressions
-2. Adjust for {formality} formality level
-3. Fix any contextual errors or awkward phrasing
-4. Preserve idiomatic expressions appropriately for {region} Spanish
-
-Respond in this format:
-TRANSLATION: [your refined translation]
-EXPLANATION: [Brief explanation of changes made and why this version fits {formality} {region} Spanish better]"""
-    else:
-        refine_prompt = f"""You are an expert English translator. Refine the following translation and explain your changes:
-
-Original Spanish: {original_text}
-Initial English translation: {translation}
-Formality level: {formality}
-
-Requirements:
-1. Use natural English expressions
-2. Adjust for {formality} formality level
-3. Fix any contextual errors or awkward phrasing
-4. Preserve meaning while making it sound natural
-
-Respond in this format:
-TRANSLATION: [your refined translation]
-EXPLANATION: [Brief explanation of changes made and why this version fits {formality} English better]"""
-
-    try:
-        response = cerebras_client.chat_completion(
-            messages=[{"role": "user", "content": refine_prompt}],
-            max_tokens=512,
-            temperature=0.3
-        )
-        
-        # Parse response to extract translation and explanation
-        content = response.choices[0].message.content.strip()
-        
-        if "TRANSLATION:" in content and "EXPLANATION:" in content:
-            translation_part = content.split("TRANSLATION:")[1].split("EXPLANATION:")[0].strip()
-            explanation_part = content.split("EXPLANATION:")[1].strip()
-            return translation_part, explanation_part
-        else:
-            return content, "Explanation not available in expected format"
-            
-    except Exception as e:
-        return f"Refinement error: {str(e)}", ""
-
-def complete_translation(text, direction, region, formality):
-    if not text.strip():
-        return "", "", ""
-    
-    try:
-        # Step 1: Initial translation
-        if direction == "English to Spanish":
-            initial_translation = translate_en_to_es(text)
-            refined_translation, explanation = refine_with_llama(text, initial_translation, "en_to_es", region, formality)
-        else:  # Spanish to English
-            initial_translation = translate_es_to_en(text)
-            refined_translation, explanation = refine_with_llama(text, initial_translation, "es_to_en", region, formality)
-        
-        return initial_translation, refined_translation, explanation
-    except Exception as e:
-        return f"Error: {str(e)}", "", ""
-
-def translate_from_file(file, direction, region, formality):
-    # Extract text from uploaded file
-    extracted_text = process_uploaded_file(file)
-    
-    if "Error" in extracted_text or "No file" in extracted_text:
-        return extracted_text, "", "", ""
-    
-    # Translate extracted text
-    initial_translation, refined_translation, explanation = complete_translation(extracted_text, direction, region, formality)
-    
-    return extracted_text, initial_translation, refined_translation, explanation
-
-# Create Gradio interface
-with gr.Blocks(title="Document Translation with Regional Spanish") as demo:
-    gr.Markdown("# Document Translation with Regional Spanish")
-    gr.Markdown("Upload PDFs or images for OCR, or type text directly. Powered by NLLB-200 + Llama 4 with regional variants")
-    
-    with gr.Tabs():
-        # Text Translation Tab
-        with gr.TabItem("Text Translation"):
-            with gr.Row():
-                with gr.Column(scale=2):
-                    input_text = gr.Textbox(
-                        label="Text to Translate",
-                        placeholder="Enter text in English or Spanish...",
-                        lines=6
-                    )
-                    
-                    with gr.Row():
-                        direction = gr.Dropdown(
-                            choices=["English to Spanish", "Spanish to English"],
-                            value="English to Spanish",
-                            label="Translation Direction"
-                        )
-                        
-                    with gr.Row():
-                        region = gr.Dropdown(
-                            choices=["Mexico", "Spain", "Argentina", "Colombia", "Peru", "General"],
-                            value="Mexico",
-                            label="Spanish Variant"
-                        )
-                        formality = gr.Dropdown(
-                            choices=["informal", "neutral", "formal"],
-                            value="neutral",
-                            label="Formality Level"
-                        )
-                    
-                    translate_btn = gr.Button("Translate", variant="primary", size="lg")
-                
-                with gr.Column(scale=2):
-                    initial_output = gr.Textbox(
-                        label="Initial Translation (NLLB-200)",
-                        lines=2,
-                        interactive=False
-                    )
-                    refined_output = gr.Textbox(
-                        label="Refined Translation (Llama 4)",
-                        lines=2,
-                        interactive=False
-                    )
-                    explanation_output = gr.Textbox(
-                        label="Explanation of Changes",
-                        lines=4,
-                        interactive=False
-                    )
-        
-        # Document Upload Tab
-        with gr.TabItem("Document Translation"):
-            with gr.Row():
-                with gr.Column(scale=2):
-                    file_input = gr.File(
-                        label="Upload PDF or Image",
-                        file_types=[".pdf", ".png", ".jpg", ".jpeg", ".tiff", ".bmp"]
-                    )
-                    
-                    with gr.Row():
-                        doc_direction = gr.Dropdown(
-                            choices=["English to Spanish", "Spanish to English"],
-                            value="English to Spanish",
-                            label="Translation Direction"
-                        )
-                        
-                    with gr.Row():
-                        doc_region = gr.Dropdown(
-                            choices=["Mexico", "Spain", "Argentina", "Colombia", "Peru", "General"],
-                            value="Mexico",
-                            label="Spanish Variant"
-                        )
-                        doc_formality = gr.Dropdown(
-                            choices=["informal", "neutral", "formal"],
-                            value="neutral",
-                            label="Formality Level"
-                        )
-                    
-                    translate_doc_btn = gr.Button("Extract & Translate", variant="primary", size="lg")
-                
-                with gr.Column(scale=2):
-                    extracted_text = gr.Textbox(
-                        label="Extracted Text",
-                        lines=3,
-                        interactive=False
-                    )
-                    doc_initial = gr.Textbox(
-                        label="Initial Translation (NLLB-200)",
-                        lines=3,
-                        interactive=False
-                    )
-                    doc_refined = gr.Textbox(
-                        label="Refined Translation (Llama 4)",
-                        lines=3,
-                        interactive=False
-                    )
-                    doc_explanation = gr.Textbox(
-                        label="Explanation of Changes",
-                        lines=3,
-                        interactive=False
-                    )
-    
-    # Connect functions
-    translate_btn.click(
-        fn=complete_translation,
-        inputs=[input_text, direction, region, formality],
-        outputs=[initial_output, refined_output, explanation_output]
-    )
-    
-    input_text.submit(
-        fn=complete_translation,
-        inputs=[input_text, direction, region, formality],
-        outputs=[initial_output, refined_output, explanation_output]
-    )
-    
-    translate_doc_btn.click(
-        fn=translate_from_file,
-        inputs=[file_input, doc_direction, doc_region, doc_formality],
-        outputs=[extracted_text, doc_initial, doc_refined, doc_explanation]
-    )
-
-if __name__ == "__main__":
-    demo.launch()