add app.py

app.py

@@ -0,0 +1,154 @@
+import gradio as gr
+import torch
+from transformers import BartTokenizer, BartForConditionalGeneration
+from peft import PeftModel
+
+# Model configuration
+MODEL_NAME = "facebook/bart-large"
+PEFT_MODEL_ID = "mohamedmostafa259/bart-emoji-translator"
+MAX_LENGTH = 32
+
+CREATIVITY_SETTINGS = {
+    0: {"top_p": 0.2, "temperature": 0.5},  # Strict
+    1: {"top_p": 0.4, "temperature": 1.0},  # Balanced
+    2: {"top_p": 0.6, "temperature": 1.5},  # Creative
+}
+
+# Load model and tokenizer
+@gr.cache
+def load_model():
+    print("Loading model...")
+    base_model = BartForConditionalGeneration.from_pretrained(MODEL_NAME)
+    model = PeftModel.from_pretrained(base_model, PEFT_MODEL_ID)
+    tokenizer = BartTokenizer.from_pretrained(PEFT_MODEL_ID)
+    
+    # Set to eval mode
+    model.eval()
+    
+    # Move to GPU if available
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    model.to(device)
+    
+    print(f"Model loaded on {device}")
+    return model, tokenizer, device
+
+# Initialize model
+model, tokenizer, device = load_model()
+
+# Translation function
+def translate_to_emoji(text, creativity_level=1):
+    if not text.strip():
+        return "⚠️ Please enter some text!"
+    
+    try:
+        tokens = tokenizer(text, return_tensors="pt", max_length=MAX_LENGTH, truncation=True).to(device)
+        with torch.no_grad():
+            outputs = model.generate(
+                **tokens,
+                max_length=MAX_LENGTH,
+                num_beams=1,
+                do_sample=True,
+                temperature=CREATIVITY_SETTINGS[creativity_level]["temperature"],
+                top_p=CREATIVITY_SETTINGS[creativity_level]["top_p"],
+            )
+        result = tokenizer.decode(outputs[0], skip_special_tokens=True).replace(" ", "") 
+        return result if result else "🤷 (No translation generated)"
+    
+    except Exception as e:
+        return f"❌ Error: {str(e)}"
+
+# Create Gradio interface
+with gr.Blocks(title="🎭 Emoji Translator", theme=gr.themes.Soft()) as demo:
+    gr.Markdown(
+        """
+        # 🎭 BART Emoji Translator
+        
+        Transform your text into emojis! This model uses curriculum learning and LoRA fine-tuning 
+        to translate English sentences into appropriate emoji sequences.
+        
+        **Examples:** Try "I am happy", "I eat dinner with my family", or "I feel misunderstood"
+        """
+    )
+    
+    with gr.Row():
+        with gr.Column(scale=2):
+            text_input = gr.Textbox(
+                label="📝 Enter your text",
+                placeholder="Type something like: I am happy today...",
+                lines=3
+            )
+            
+            # --- Creativity Slider (0, 1, 2) ---
+            creativity = gr.Slider(
+                minimum=0,
+                maximum=2,
+                value=1,
+                step=1,
+                label="🎨 Creativity",
+                info="0 = strict, 1 = balanced, 2 = creative"
+            )
+
+            translate_btn = gr.Button("✨ Translate to Emojis", variant="primary")
+        
+        with gr.Column(scale=1):
+            emoji_output = gr.Textbox(
+                label="🎉 Emoji Translation",
+                lines=3,
+                scale=2
+            )
+    
+    # Example inputs
+    gr.Examples(
+        examples=[
+            ["I am happy", 0],
+            ["I feel sad", 2],
+            ["I eat dinner with my family", 1],
+            ["I tweeted the news to my followers", 1],
+            ["Parents sleep together to have more children", 1],
+        ],
+        inputs=[text_input, creativity],
+        outputs=emoji_output,
+        fn=translate_to_emoji,
+        cache_examples=True,
+    )
+    
+    # Button click event
+    translate_btn.click(
+        fn=translate_to_emoji,
+        inputs=[text_input, creativity],
+        outputs=emoji_output
+    )
+    
+    # Also trigger on Enter key
+    text_input.submit(
+        fn=translate_to_emoji,
+        inputs=[text_input, creativity],
+        outputs=emoji_output
+    )
+    
+    gr.Markdown(
+        """
+        ---
+        ### 📚 Model Information
+        
+        - **Model:** BART-Large with LoRA fine-tuning
+        - **Training:** 6-phase curriculum learning with strategic data retention
+        - **Parameters:** 128 LoRA rank, 256 alpha
+        - **Dataset:** Progressive difficulty from single emojis to complex sequences
+        
+        ### 🔗 Links
+        
+        - [Model on HuggingFace](https://huggingface.co/mohamedmostafa259/bart-emoji-translator)
+        - [Training Code](https://www.kaggle.com/code/mohamedmostafa259/emoji-translator-curriculum)
+        
+        ### ⚠️ Limitations
+        
+        - Works best with English text under 32 tokens
+        - May not recognize very rare or newly created emojis
+        - Performance varies with text complexity
+        """
+    )
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch()