First iteration from Copilot without changes

app.py

@@ -0,0 +1,123 @@
+# app.py
+
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer, TrainingArguments, TextDataset, DataCollatorForLanguageModeling
+import torch
+import os
+
+# Check if CUDA is available
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Model name
+PRETRAINED_MODEL = "distilgpt2"
+MODEL_DIR = "./fine_tuned_model"
+
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained(PRETRAINED_MODEL)
+
+def fine_tune_model(files):
+    # Combine uploaded files into one text
+    if not files:
+        return "No files uploaded."
+    text_data = ""
+    for file in files:
+        text = file.decode('utf-8')
+        text_data += text + "\n"
+
+    # Save combined text to a file
+    with open("train.txt", "w") as f:
+        f.write(text_data)
+
+    # Create dataset
+    dataset = TextDataset(
+        tokenizer=tokenizer,
+        file_path="train.txt",
+        block_size=128
+    )
+
+    data_collator = DataCollatorForLanguageModeling(
+        tokenizer=tokenizer, mlm=False,
+    )
+
+    # Load pre-trained model
+    model = AutoModelForCausalLM.from_pretrained(PRETRAINED_MODEL)
+    model.to(device)
+
+    # Set training arguments
+    training_args = TrainingArguments(
+        output_dir=MODEL_DIR,
+        overwrite_output_dir=True,
+        num_train_epochs=1,
+        per_device_train_batch_size=4,
+        save_steps=500,
+        save_total_limit=2,
+        logging_steps=100,
+    )
+
+    # Initialize Trainer
+    trainer = Trainer(
+        model=model,
+        args=training_args,
+        data_collator=data_collator,
+        train_dataset=dataset,
+    )
+
+    # Fine-tune model
+    trainer.train()
+
+    # Save the model
+    trainer.save_model(MODEL_DIR)
+    tokenizer.save_pretrained(MODEL_DIR)
+
+    return "Fine-tuning completed successfully!"
+
+def generate_response(prompt, temperature, max_length, top_p):
+    # Load fine-tuned model if available
+    if os.path.exists(MODEL_DIR):
+        model = AutoModelForCausalLM.from_pretrained(MODEL_DIR)
+        tokenizer = AutoTokenizer.from_pretrained(MODEL_DIR)
+    else:
+        model = AutoModelForCausalLM.from_pretrained(PRETRAINED_MODEL)
+        tokenizer = AutoTokenizer.from_pretrained(PRETRAINED_MODEL)
+    model.to(device)
+
+    # Encode prompt
+    input_ids = tokenizer.encode(prompt, return_tensors='pt').to(device)
+
+    # Generate output
+    output = model.generate(
+        input_ids,
+        do_sample=True,
+        max_length=int(max_length),
+        temperature=float(temperature),
+        top_p=float(top_p),
+        pad_token_id=tokenizer.eos_token_id
+    )
+
+    response = tokenizer.decode(output[0], skip_special_tokens=True)
+    return response
+
+# Build Gradio Interface
+
+with gr.Blocks() as demo:
+    gr.Markdown("# 🚀 Language Model Fine-Tuner and Chatbot")
+
+    with gr.Tab("Fine-Tune Model"):
+        gr.Markdown("## 📚 Fine-Tune the Model with Your Documents")
+        file_inputs = gr.File(label="Upload Text Files", file_count="multiple", type="bytes")
+        fine_tune_button = gr.Button("Start Fine-Tuning")
+        fine_tune_status = gr.Textbox(label="Status", interactive=False)
+        fine_tune_button.click(fine_tune_model, inputs=file_inputs, outputs=fine_tune_status)
+
+    with gr.Tab("Chat with Model"):
+        gr.Markdown("## 💬 Chat with the Fine-Tuned Model")
+        user_input = gr.Textbox(label="Your Message")
+        with gr.Accordion("Advanced Parameters", open=False):
+            temperature = gr.Slider(0.1, 1.0, value=0.7, label="Temperature")
+            max_length = gr.Slider(20, 200, value=100, step=10, label="Max Length")
+            top_p = gr.Slider(0.1, 1.0, value=0.9, label="Top-p")
+        generate_button = gr.Button("Generate Response")
+        bot_response = gr.Textbox(label="Model Response", interactive=False)
+        generate_button.click(generate_response, inputs=[user_input, temperature, max_length, top_p], outputs=bot_response)
+
+demo.launch()