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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
with gr.Blocks() as demo:
gr.HTML("""
<center><h1>Emoji Translator π€π»</h1>
<h3>Translate any text into emojis, and vice versa!</h3>
</center>
""")
gr.Markdown("""
# Text to Emoji πβ‘οΈπ»
""")
with gr.Row():
text_uesr_input = gr.Textbox(label="Enter text π")
output = gr.Textbox(label="Translation")
with gr.Row():
translate_btn = gr.Button("Translate π")
translate_btn.click(fn=classify_task, inputs=text_uesr_input,
outputs=output, api_name="translate_text")
# Launch the app
if __name__ == "__main__":
demo.launch()
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