text_generator.py

import requests

from transformers import Tool
from transformers import pipeline

class TextGenerationTool(Tool):
    name = "text_generator"
    description = (
        "This is a tool for text generation. It takes a prompt as input and returns the generated text."
    )

    inputs = ["text"]
    outputs = ["text"]
        

def __call__(self, prompt: str):


    API_URL = "https://api-inference.huggingface.co/models/lukasdrg/clinical_longformer_same_tokens_220k"
    headers = {"Authorization": "Bearer "+os.environ['HF']+"}
    
    #def query(payload):
    generated_text = requests.post(API_URL, headers=headers, json=payload)
    #	return response.json()
        
    #output = query({
    #	"inputs": "The answer to the universe is <mask>.",
    #})


    
    # Replace the following line with your text generation logic
    #generated_text = f"Generated text based on the prompt: '{prompt}'"

    # Initialize the text generation pipeline
    #text_generator = pipeline("text-generation") llama mistralai/Mistral-7B-Instruct-v0.1
    #text_generator = pipeline(model="gpt2")
    #text_generator = pipeline(model="meta-llama/Llama-2-7b-chat-hf")

    # Generate text based on a prompt
    #generated_text = text_generator(prompt, max_length=500, num_return_sequences=1, temperature=0.7)

    # Print the generated text
    #print(generated_text)



    return generated_text