Spaces:
Runtime error
Runtime error
| import gradio as gr | |
| from ctransformers import AutoModelForCausalLM, AutoTokenizer | |
| import torch | |
| # Load the model | |
| model = AutoModelForCausalLM.from_pretrained("TheBloke/Mistral-7B-Instruct-v0.1-GGUF", model_file="mistral-7b-instruct-v0.1.Q5_K_S.gguf", model_type="mistral", gpu_layers=0) | |
| ins = '''[INST] <<SYS>> | |
| You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. | |
| If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information. | |
| <</SYS>> | |
| {} [/INST] | |
| ''' | |
| # Define the conversation history | |
| conversation_history = [] | |
| css = ".generating {visibility: hidden}" | |
| def generate_response(input_text): | |
| global conversation_history | |
| # Append the user's input to the conversation history | |
| conversation_history.append({"role": "system", "content": input_text}) | |
| response_text = model(conversation_history) | |
| conversation_history.append({"role": "user", "content": input_text}) | |
| conversation_history.append({"role": "assistant", "content": response_text}) | |
| return response_text | |
| with gr.Blocks( analytics_enabled=False, css=css) as demo: | |
| with gr.Column(): | |
| gr.Markdown( | |
| """ ## Mistral-7b | |
| Type in the box below and click the button to generate answers to your most pressing questions! | |
| """ | |
| ) | |
| with gr.Row(): | |
| with gr.Column(scale=3): | |
| instruction = gr.Textbox(placeholder="Type Your message here...", label="Question", elem_id="q-input") | |
| with gr.Box(): | |
| gr.Markdown("**Answer**") | |
| output = gr.Markdown(elem_id="q-output") | |
| submit = gr.Button("Generate", variant="primary") | |
| submit.click(generate_response, inputs=[instruction], outputs=[output]) | |
| instruction.submit(generate_response, inputs=[instruction], outputs=[output]) | |
| demo.queue(concurrency_count=1).launch(debug=False,share=True) |