app.py

import torch
import gradio as gr
from transformers import BartTokenizer, BartForConditionalGeneration
from transformers.modeling_outputs import BaseModelOutput

# ==========================================
# 1. SETUP: Load Model (Global Scope)
# ==========================================
model_name = "facebook/bart-large" # "facebook/bart-base"
print(f"Loading {model_name}...")
tokenizer = BartTokenizer.from_pretrained(model_name)
model = BartForConditionalGeneration.from_pretrained(model_name)
model.eval() # Set to evaluation mode

# ==========================================
# 2. CORE LOGIC FUNCTIONS
# ==========================================

def text_to_embedding(text):
    """Encodes text into the BART Latent Space (Vectors)."""
    inputs = tokenizer(text, return_tensors="pt")
    with torch.no_grad():
        encoder_outputs = model.model.encoder(**inputs)
    return encoder_outputs.last_hidden_state

def embedding_to_text(embedding_tensor):
    """Decodes a Vector back into Text."""
    encoder_outputs_wrapped = BaseModelOutput(last_hidden_state=embedding_tensor)
    with torch.no_grad():
        generated_ids = model.generate(
            encoder_outputs=encoder_outputs_wrapped,
            max_length=50,
            num_beams=4,
            early_stopping=True
        )
    decoded_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
    return decoded_text

# ==========================================
# 3. GRADIO INTERFACE FUNCTIONS
# ==========================================

def run_reconstruction(text):
    if not text:
        return "", "Please enter text."
        
    # 1. Encode
    vector = text_to_embedding(text)
    
    # 2. Decode
    reconstructed = embedding_to_text(vector)
    
    # 3. Get Stats
    shape_info = f"Vector Shape: {vector.shape} (Batch, Tokens, Dimensions)"
    preview = f"First 5 values: {vector[0][0][:5].numpy().tolist()}"
    
    debug_info = f"{shape_info}\n{preview}"
    
    return reconstructed, debug_info

def run_mixing(text1, text2):
    if not text1 or not text2:
        return "Please enter two sentences."

    # 1. Get vectors
    v1 = text_to_embedding(text1)
    v2 = text_to_embedding(text2)

    # 2. Align lengths (Truncate to minimum length)
    # Note: In a production app, you might want to pad instead of truncate,
    # but for this specific "averaging" demo, truncation prevents dimension mismatch errors.
    min_len = min(v1.shape[1], v2.shape[1])
    
    v1_cut = v1[:, :min_len, :]
    v2_cut = v2[:, :min_len, :]

    # 3. Math: Average the vectors
    v_mixed = (v1_cut + v2_cut) / 2.0

    # 4. Decode
    mixed_text = embedding_to_text(v_mixed)
    
    return mixed_text

# ==========================================
# 4. BUILD UI
# ==========================================

with gr.Blocks(title="BART Latent Space Explorer", theme=gr.themes.Soft()) as demo:
    gr.Markdown("# 🧠 BART Latent Space Explorer")
    gr.Markdown("This tool uses `facebook/bart-base` to convert text into mathematical vectors (Embeddings) and back.")

    with gr.Tabs():
        
        # --- TAB 1: RECONSTRUCTION ---
        with gr.TabItem("1. Auto-Encoder Test"):
            gr.Markdown("Type a sentence. The model will turn it into numbers, then turn those numbers back into text.")
            
            with gr.Row():
                with gr.Column():
                    input_text = gr.Textbox(label="Original Sentence", value="The cat sat on the mat.")
                    btn_recon = gr.Button("Encode & Decode", variant="primary")
                
                with gr.Column():
                    output_recon = gr.Textbox(label="Reconstructed Text")
                    output_debug = gr.Code(label="Vector Stats", language="json")

            btn_recon.click(run_reconstruction, inputs=input_text, outputs=[output_recon, output_debug])

        # --- TAB 2: VECTOR MIXING ---
        with gr.TabItem("2. Vector Mixing (Math)"):
            gr.Markdown("Type two different sentences. We will average their mathematical representations. Results may be surreal!")
            
            with gr.Row():
                with gr.Column():
                    mix_in_1 = gr.Textbox(label="Sentence A", value="The weather is sunny.")
                    mix_in_2 = gr.Textbox(label="Sentence B", value="The weather is rainy.")
                    btn_mix = gr.Button("Calculate Average Meaning", variant="primary")
                
                with gr.Column():
                    mix_out = gr.Textbox(label="The AI's 'Middle Ground' Thought", lines=4)

            btn_mix.click(run_mixing, inputs=[mix_in_1, mix_in_2], outputs=mix_out)

if __name__ == "__main__":
    demo.launch()