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import requests | |
import os | |
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/mistralai/Mistral-7B-v0.1" | |
headers = {"Authorization": + os.environ['hf']} | |
def query(payload): | |
response = requests.post(API_URL, headers=headers, json=payload) | |
print(response) | |
return response.json() | |
output = query({ | |
"inputs": prompt, | |
}) | |
# 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) | |