text_generator.py

import requests
import os
from transformers import pipeline


from transformers import Tool
# Import other necessary libraries if needed

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/openchat/openchat_3.5"
        #headers = {"Authorization": "Bearer " +  os.environ['hf']}
        token=os.environ['HF_token']
        #payload = {
        #    "inputs": prompt  # Adjust this based on your model's input format
        #}

        #payload = {
        #            "inputs": "Can you please let us know more details about your ",
        #        }
        
        #def query(payload):
        #generated_text = requests.post(API_URL, headers=headers, json=payload).json()
        #print(generated_text)
        #return generated_text["text"]
            
        # 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(model="lgaalves/gpt2-dolly", token=token)
        text_generator = pipeline(model="microsoft/Orca-2-13b", token=token)

        # 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
        
        # Define the payload for the request
        #payload = {
        #    "inputs": prompt  # Adjust this based on your model's input format
        #}

        # Make the request to the API
        #generated_text = requests.post(API_URL, headers=headers, json=payload).json()

        # Extract and return the generated text
        #return generated_text["generated_text"]

# Uncomment and customize the following lines based on your text generation needs
# text_generator = pipeline(model="gpt2")
# generated_text = text_generator(prompt, max_length=500, num_return_sequences=1, temperature=0.7)

# Print the generated text if needed
# print(generated_text)