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| from huggingface_hub import InferenceClient | |
| import gradio as gr | |
| import random | |
| API_URL = "https://api-inference.huggingface.co/models/" | |
| client = InferenceClient( | |
| "mistralai/Mistral-7B-Instruct-v0.2" | |
| ) # mistralai/Mixtral-7B-Instruct-v0.1 or mistralai/Mixtral-7B-Instruct-v0.2 | |
| def format_prompt(message, history): | |
| prompt = "You a React Developer when told to develop a project that functions in a certain way ensure you write the project file structure sructure then write all the codes in the most advanced way possible and in other cases you can try inserting pictures via url then write good code and ensure they are the best, dont write any simple codes please. and sue some few comments on your codes." | |
| for user_prompt, bot_response in history: | |
| prompt += f"[INST] {user_prompt} [/INST]" | |
| prompt += f" {bot_response}</s> " | |
| prompt += f"[INST] {message} [/INST]" | |
| return prompt | |
| def generate(prompt, history, temperature=0.7, max_new_tokens=200000048, top_p=0.95, repetition_penalty=1): | |
| temperature = float(temperature) | |
| if temperature < 1e-2: | |
| temperature = 1e-2 | |
| top_p = float(top_p) | |
| generate_kwargs = dict( | |
| temperature=temperature, | |
| max_new_tokens=max_new_tokens, | |
| top_p=top_p, | |
| repetition_penalty=repetition_penalty, | |
| do_sample=True, | |
| seed=random.randint(0, 10**7), | |
| ) | |
| formatted_prompt = format_prompt(prompt, history) | |
| stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False) | |
| output = "" | |
| for response in stream: | |
| output += response.token.text | |
| yield output | |
| return output | |
| customCSS = """ | |
| #component-7 { # this is the default element ID of the chat component | |
| height: 1600px; # adjust the height as needed | |
| flex-grow: 4; | |
| } | |
| """ | |
| with gr.Blocks(theme=gr.themes.Soft()) as demo: | |
| gr.ChatInterface( | |
| generate, | |
| examples=[ | |
| ["Hello"] | |
| ], | |
| ) | |
| demo.queue(concurrency_count=75, max_size=100).launch(debug=True) |