Create app.py

app.py

@@ -1,64 +1,217 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
+import torch
+import os
+from datasets import load_dataset
+from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments
+from peft import LoraConfig, prepare_model_for_kbit_training
+from trl import SFTTrainer
+import json
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+def fine_tune_model():
+    """Fine-tune model for personal assistant with progress updates"""
+    
+    try:
+        yield "🔄 Starting fine-tuning process...\n"
+        
+        # Use a manageable model for Spaces
+        model_name = "microsoft/DialoGPT-medium"
+        yield f"📥 Loading model: {model_name}\n"
+        
+        # Load tokenizer
+        tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side="left")
+        tokenizer.pad_token = tokenizer.eos_token
+        yield "✅ Tokenizer loaded successfully\n"
+        
+        # Load model
+        model = AutoModelForCausalLM.from_pretrained(
+            model_name,
+            device_map="auto" if torch.cuda.is_available() else None,
+        )
+        yield "✅ Model loaded successfully\n"
+        
+        # Load dataset
+        yield "📊 Loading training dataset...\n"
+        dataset = load_dataset("databricks/databricks-dolly-15k", split="train[:1000]")
+        yield f"✅ Dataset loaded: {len(dataset)} examples\n"
+        
+        # Format dataset
+        def format_example(example):
+            instruction = example["instruction"]
+            response = example["response"]
+            context = example.get("context", "")
+            
+            if context:
+                text = f"Human: {instruction}\nContext: {context}\nAssistant: {response}<|endoftext|>"
+            else:
+                text = f"Human: {instruction}\nAssistant: {response}<|endoftext|>"
+            return {"text": text}
+        
+        processed_dataset = dataset.map(format_example)
+        yield "✅ Dataset formatted for training\n"
+        
+        # LoRA configuration
+        peft_config = LoraConfig(
+            lora_alpha=16,
+            lora_dropout=0.1,
+            r=8,
+            bias="none",
+            task_type="CAUSAL_LM",
+            target_modules=["c_attn", "c_proj"],
+        )
+        yield "✅ LoRA configuration set\n"
+        
+        # Training arguments
+        training_arguments = TrainingArguments(
+            output_dir="./results",
+            per_device_train_batch_size=1,
+            gradient_accumulation_steps=4,
+            logging_steps=10,
+            save_steps=100,
+            learning_rate=5e-5,
+            num_train_epochs=1,
+            warmup_steps=50,
+            remove_unused_columns=False,
+            dataloader_pin_memory=False,
+        )
+        yield "✅ Training configuration set\n"
+        
+        # Create trainer
+        trainer = SFTTrainer(
+            model=model,
+            train_dataset=processed_dataset,
+            tokenizer=tokenizer,
+            args=training_arguments,
+            peft_config=peft_config,
+            dataset_text_field="text",
+        )
+        yield "🚀 Starting model training...\n"
+        
+        # Start training
+        trainer.train()
+        yield "✅ Training completed successfully!\n"
+        
+        # Save model
+        trainer.save_model("./fine_tuned_assistant")
+        tokenizer.save_pretrained("./fine_tuned_assistant")
+        yield "💾 Model saved successfully!\n"
+        
+        yield "🎉 Fine-tuning process completed! Your personal assistant is ready!\n"
+        
+    except Exception as e:
+        yield f"❌ Error during fine-tuning: {str(e)}\n"
 
+def chat_with_assistant(message, history):
+    """Simple chat interface for testing"""
+    if not message.strip():
+        return history, ""
+    
+    # Placeholder response (you can implement actual model inference later)
+    responses = [
+        f"Thank you for your message: '{message}'. As your personal assistant, I'm here to help!",
+        f"I appreciate you reaching out about '{message}'. How can I assist you further?",
+        f"Regarding '{message}', I'm happy to help. What specific assistance do you need?",
+        f"I understand you mentioned '{message}'. As your supportive assistant, I'm here for you!"
+    ]
+    
+    import random
+    response = random.choice(responses)
+    
+    history.append((message, response))
+    return history, ""
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
+# Create Gradio interface
+with gr.Blocks(title="Personal Assistant Fine-Tuning", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("""
+    # 🤖 Personal Assistant Fine-Tuning on Hugging Face Spaces
+    
+    Welcome to your personal AI assistant training platform! This space allows you to:
+    - Fine-tune a language model to be your personal assistant
+    - Test the assistant's responses
+    - Create a kind, supportive, and intelligent AI companion
+    """)
+    
+    with gr.Tab("🔧 Fine-Tune Model"):
+        gr.Markdown("""
+        ### Train Your Personal Assistant
+        
+        Click the button below to start fine-tuning your model. This process will:
+        1. Load a pre-trained conversational model
+        2. Train it on assistant-style conversations
+        3. Optimize it to be supportive and helpful
+        
+        **Note:** Training may take 30-60 minutes depending on your hardware tier.
+        """)
+        
+        tune_button = gr.Button("🚀 Start Fine-Tuning", variant="primary", size="lg")
+        tune_output = gr.Textbox(
+            label="Training Progress", 
+            lines=15, 
+            max_lines=20,
+            show_copy_button=True,
+            interactive=False
+        )
+        
+        tune_button.click(
+            fn=fine_tune_model,
+            outputs=tune_output
+        )
+    
+    with gr.Tab("💬 Test Assistant"):
+        gr.Markdown("""
+        ### Chat with Your Assistant
+        
+        Once fine-tuning is complete, use this interface to test your personal assistant.
+        Your assistant is designed to be kind, supportive, and intelligent.
+        """)
+        
+        chatbot = gr.Chatbot(
+            height=500,
+            show_copy_button=True,
+            bubble_full_width=False
+        )
+        
+        with gr.Row():
+            msg = gr.Textbox(
+                label="Your message",
+                placeholder="Type your message here...",
+                lines=2,
+                scale=4
+            )
+            send_btn = gr.Button("Send", variant="primary", scale=1)
+        
+        clear_btn = gr.Button("🗑️ Clear Chat", variant="secondary")
+        
+        # Event handlers
+        msg.submit(chat_with_assistant, [msg, chatbot], [chatbot, msg])
+        send_btn.click(chat_with_assistant, [msg, chatbot], [chatbot, msg])
+        clear_btn.click(lambda: [], None, chatbot, queue=False)
+    
+    with gr.Tab("ℹ️ Info"):
+        gr.Markdown("""
+        ### About This Space
+        
+        This Hugging Face Space is designed to help you create your own personal AI assistant through fine-tuning.
+        
+        **Features:**
+        - 🤖 Fine-tune conversational AI models
+        - 💬 Interactive chat interface for testing
+        - 🎯 Optimized for supportive, intelligent responses
+        - 📊 Real-time training progress monitoring
+        
+        **Hardware Recommendations:**
+        - **Free CPU:** Good for testing the interface
+        - **T4 Small GPU ($0.60/hr):** Recommended for actual training
+        - **A10G Small ($1.05/hr):** Faster training, better performance
+        
+        **Tips for Success:**
+        1. Start with free tier to test everything works
+        2. Upgrade to GPU when ready for actual training
+        3. Monitor training progress in the Fine-Tune tab
+        4. Test your assistant in the Chat tab after training
+        
+        Created with ❤️ for building personal AI assistants!
+        """)
 
 if __name__ == "__main__":
+    demo.queue()
     demo.launch()