Upload app.py

app.py

@@ -1,10 +1,68 @@
+#!/usr/bin/env python3
+"""
+Gradio App for EeshaAI/Zeeb Training Space
+==========================================
+Provides a web UI to trigger LoRA fine-tuning of OLMo 2 1B Instruct
+on tokenized video data, then push the trained model to EeshaAI/zeeb.
+"""
+
 import gradio as gr
+from train_on_hf_spaces import train
+
+
+def run_training():
+    """Run the training pipeline and stream logs to the UI."""
+    for log_msg in train("tokenized_dataset.json"):
+        yield log_msg
+
+
+with gr.Blocks(
+    title="Zeeb — Video-LLM Trainer",
+    theme=gr.themes.Soft(),
+    css="""
+        #logbox { font-family: 'Courier New', monospace; font-size: 13px; line-height: 1.5; }
+        #title { text-align: center; }
+        .contain { max-width: 800px; margin: auto; }
+    """
+) as demo:
+
+    gr.Markdown(
+        """
+        # 🎬 Zeeb — Video-LLM Trainer
+        Fine-tune **OLMo 2 1B Instruct** with **LoRA (r=4)** to generate video tokens.
+        Trained model is automatically pushed to [EeshaAI/zeeb](https://huggingface.co/EeshaAI/zeeb).
+        """,
+        elem_id="title",
+    )
+
+    with gr.Row():
+        train_btn = gr.Button("🚀 Start Training", variant="primary", size="lg")
+
+    logbox = gr.Textbox(
+        label="Training Log",
+        elem_id="logbox",
+        lines=25,
+        max_lines=100,
+        interactive=False,
+        show_copy_button=True,
+    )
+
+    gr.Markdown(
+        """
+        ### What happens when you click "Start Training"?
+        1. 📦 Downloads **OLMo 2 1B Instruct** from HuggingFace
+        2. 🔤 Expands vocabulary with **1,024 visual tokens** (`<v_0>` ... `<v_1023>`)
+        3. 🔧 Applies **LoRA r=4** to q_proj & v_proj (minimal memory)
+        4. 🔥 Trains for **3 epochs** on the tokenized video dataset
+        5. 🔀 Merges LoRA weights back into the base model
+        6. 🚀 Pushes the merged model to **EeshaAI/zeeb**
+
+        ⚠️ Training on CPU takes time (~10-30 min depending on dataset size).
+        """
+    )
+
+    train_btn.click(fn=run_training, outputs=logbox)
+
 
-with gr.Blocks(fill_height=True) as demo:
-    with gr.Sidebar():
-        gr.Markdown("# Inference Provider")
-        gr.Markdown("This Space showcases the stabilityai/stable-diffusion-3.5-large model, served by the fal-ai API. Sign in with your Hugging Face account to use this API.")
-        button = gr.LoginButton("Sign in")
-    gr.load("models/stabilityai/stable-diffusion-3.5-large", accept_token=button, provider="fal-ai")
-    
-demo.launch()
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860)