import re import gradio as gr from huggingface_hub import InferenceClient client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1") system_instructions = """ [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 with gr.Blocks() as demo: gr.HTML("""

Emoji Translator 🤗😻

Translate any text into emojis, and vice versa!

""") gr.Markdown(""" # Text to Emoji 📖➡️😻 """) with gr.Row(): text_uesr_input = gr.Textbox(label="Enter text 📚") output = gr.Textbox(label="Translation") with gr.Row(): translate_btn = gr.Button("Translate 🚀") translate_btn.click(fn=classify_task, inputs=text_uesr_input, outputs=output, api_name="translate_text") # Launch the app if __name__ == "__main__": demo.launch()