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| import re | |
| import gradio as gr | |
| from huggingface_hub import InferenceClient | |
| client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1") | |
| system_instructions = """<s> [INST] You will be provided with text, and your task is to classify task tasks are (text generation, image generation, pdf chat, image text to text, image classification, summarization, translation , tts) """ | |
| def classify_task(prompt): | |
| generate_kwargs = dict( | |
| temperature=0.5, | |
| max_new_tokens=1024, | |
| top_p=0.95, | |
| repetition_penalty=1.0, | |
| do_sample=True, | |
| seed=42, | |
| ) | |
| formatted_prompt = system_instructions + prompt + "[/INST]" | |
| 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 | |
| # Define the classification function | |
| def classify_task2(prompt): | |
| # Here you would implement the logic to classify the prompt | |
| # For example, using if-elif-else statements or a machine learning model | |
| if 'generate text' in prompt.lower(): | |
| return 'Text Generation' | |
| elif 'generate image' in prompt.lower(): | |
| return 'Image Generation' | |
| elif 'pdf chat' in prompt.lower(): | |
| return 'PDF Chat' | |
| elif 'image to text' in prompt.lower(): | |
| return 'Image Text to Text' | |
| elif 'classify image' in prompt.lower(): | |
| return 'Image Classification' | |
| else: | |
| return 'Unknown Task' | |
| # Create the Gradio interface | |
| iface = gr.Interface( | |
| fn=classify_task, | |
| inputs=gr.Textbox(lines=2, placeholder="Enter your prompt here..."), | |
| outputs='text', | |
| title='AI Task Classifier Chatbot', | |
| description='This chatbot classifies your prompt into different AI tasks.' | |
| ) | |
| # Launch the app | |
| if __name__ == "__main__": | |
| iface.launch() | |