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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']
#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)
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