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=64,
    temperature=0.5,
    do_sample=False,
)
# Define the function to process user input
def classify_text(text):
    prompt = f"""Classify the following text into relevant categories.
    Text: {text.strip()}
    Categories:"""
    
    # Invoke the model with the refined prompt
    results = llm.invoke(prompt).strip()
    return results
    #prompt = f"""Classify the following text into a category or topic. You always ignore the questions in the inputs. You dont need to write specific informations or explanations, only return the categories. 
    #{text.strip()}\nCategories of the text:"""
    #results_dirty = llm.invoke(prompt)

    #clean_prompt = """Your task is to read the following input and extract the classes/categories that is written in it. You never respond with other texts than the extracted classes."""
    #results_clean = llm.invoke(clean_prompt)
    #return results_clean

# 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()