Delete app.py

app.py

@@ -1,202 +0,0 @@
-import gradio as gr
-import spaces
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-import torch
-from huggingface_hub import InferenceClient
-import os
-
-# 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 refine_with_llama(original_text, translation, direction, formality="neutral"):
-    if direction == "en_to_es":
-        refine_prompt = f"""You are an expert Mexican Spanish translator. Refine the following translation and explain your changes:
-
-Original English: {original_text}
-Initial Spanish translation: {translation}
-Formality level: {formality}
-
-Requirements:
-1. Use Mexican Spanish vocabulary and expressions
-2. Adjust for {formality} formality level
-3. Fix any contextual errors or awkward phrasing
-4. Preserve idiomatic expressions appropriately for Mexican Spanish
-
-Respond in this format:
-TRANSLATION: [your refined translation]
-EXPLANATION: [Brief explanation of changes made and why this version fits {formality} Mexican 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, 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", formality)
-        else:  # Spanish to English
-            initial_translation = translate_es_to_en(text)
-            refined_translation, explanation = refine_with_llama(text, initial_translation, "es_to_en", formality)
-        
-        return initial_translation, refined_translation, explanation
-    except Exception as e:
-        return f"Error: {str(e)}", "", ""
-
-# Create Gradio interface
-with gr.Blocks(title="Mexican Spanish Translator with Explanations") as demo:
-    gr.Markdown("# Mexican Spanish Translator")
-    gr.Markdown("Powered by NLLB-200 + Llama 4 with detailed explanations for Mexican Spanish formality levels")
-    
-    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"
-                )
-                formality = gr.Dropdown(
-                    choices=["informal", "neutral", "formal"],
-                    value="neutral",
-                    label="Formality Level (Mexican Spanish)"
-                )
-            
-            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
-            )
-    
-    # Connect function
-    translate_btn.click(
-        fn=complete_translation,
-        inputs=[input_text, direction, formality],
-        outputs=[initial_output, refined_output, explanation_output]
-    )
-    
-    input_text.submit(
-        fn=complete_translation,
-        inputs=[input_text, direction, formality],
-        outputs=[initial_output, refined_output, explanation_output]
-    )
-
-if __name__ == "__main__":
-    demo.launch()