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| import gradio as gr | |
| import torch | |
| from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline | |
| from peft import PeftModel, PeftConfig | |
| # Load the fine-tuned model and tokenizer | |
| model_name = "sagar007/phi-1_5-finetuned" # Updated model path | |
| config = PeftConfig.from_pretrained(model_name) | |
| # Check if CUDA is available | |
| if torch.cuda.is_available(): | |
| device_map = "auto" | |
| torch_dtype = torch.float16 | |
| else: | |
| device_map = "cpu" | |
| torch_dtype = torch.float32 | |
| model = AutoModelForCausalLM.from_pretrained( | |
| config.base_model_name_or_path, | |
| torch_dtype=torch_dtype, | |
| device_map=device_map | |
| ) | |
| model = PeftModel.from_pretrained(model, model_name) | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| # Create a text generation pipeline | |
| generator = pipeline("text-generation", model=model, tokenizer=tokenizer, device_map=device_map) | |
| def generate_text(prompt, max_length=100, temperature=0.7, top_p=0.9): | |
| """Generate text based on the input prompt.""" | |
| if not prompt.strip(): | |
| return "Please enter a prompt before generating text." | |
| try: | |
| generated = generator(prompt, max_length=max_length, do_sample=True, temperature=temperature, top_p=top_p) | |
| return generated[0]['generated_text'] | |
| except Exception as e: | |
| return f"An error occurred: {str(e)}" | |
| # Custom CSS for styling (unchanged) | |
| custom_css = """ | |
| ... (your existing CSS) | |
| """ | |
| # Create the Gradio interface | |
| with gr.Blocks(css=custom_css) as iface: | |
| gr.HTML("<div class='container'>") | |
| gr.HTML("<h1>🤖 Phi-1.5 Fine-tuned Text Generator</h1>") | |
| with gr.Row(): | |
| with gr.Column(): | |
| input_text = gr.Textbox(lines=5, label="Enter your prompt") | |
| max_length = gr.Slider(minimum=50, maximum=500, value=100, step=10, label="Max Length") | |
| temperature = gr.Slider(minimum=0.1, maximum=1.0, value=0.7, step=0.1, label="Temperature") | |
| top_p = gr.Slider(minimum=0.1, maximum=1.0, value=0.9, step=0.1, label="Top P") | |
| generate_button = gr.Button("Generate Text", variant="primary") | |
| with gr.Column(): | |
| output_text = gr.Textbox(lines=10, label="Generated Text") | |
| # Add example prompts | |
| gr.Examples( | |
| examples=[ | |
| "Explain the concept of machine learning.", | |
| "Write a short story about a robot learning to paint.", | |
| "What are some effective ways to reduce stress?", | |
| "Summarize the key points of climate change in simple terms.", | |
| "Create a step-by-step guide for making a perfect omelette.", | |
| "Describe the differences between classical and quantum computing.", | |
| "Write a motivational speech for a team starting a new project.", | |
| "Explain the importance of biodiversity in ecosystems.", | |
| "Compose a haiku about artificial intelligence.", | |
| "List five tips for effective time management.", | |
| "Describe the process of photosynthesis in layman's terms.", | |
| "Write a dialogue between two characters discussing the future of space exploration.", | |
| "Explain the concept of blockchain technology and its potential applications." | |
| ], | |
| inputs=input_text | |
| ) | |
| generate_button.click( | |
| generate_text, | |
| inputs=[input_text, max_length, temperature, top_p], | |
| outputs=output_text | |
| ) | |
| gr.HTML("<div class='footer'>Powered by Hugging Face and Gradio</div>") | |
| gr.HTML("<p>This model is a fine-tuned version of Phi-1.5, trained on the OpenAssistant dataset.</p>") | |
| gr.HTML("</div>") | |
| # Launch the app | |
| iface.launch() |