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import gradio as gr | |
# Load the text-generation pipeline with Mistral model | |
from langchain_huggingface import HuggingFaceEndpoint | |
# Initialize the LLM and other components | |
llm = HuggingFaceEndpoint( | |
repo_id="mistralai/Mistral-7B-Instruct-v0.3", | |
task="text-generation", | |
max_new_tokens=4096, | |
temperature=0.5, | |
do_sample=False, | |
) | |
# Define the function to process user input | |
def classify_text(text): | |
prompt = "Classify the following text into a category or topic:" | |
input_text = f"{prompt}\n{text}" | |
results = llm.invoke(input_text) | |
return results | |
# Create Gradio interface | |
interface = gr.Interface( | |
fn=classify_text, | |
inputs=gr.Textbox(lines=4, placeholder="Enter your text here..."), | |
outputs=gr.Textbox(lines=4), | |
title="Text Classification with Mistral", | |
description="Enter some text to classify it into a category or topic using the Mistral-7B-Instruct-v0.3 model." | |
) | |
# Launch the app | |
if __name__ == "__main__": | |
interface.launch() | |