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Text-Generations-using-mistralai-Mistral-7B-Instruct-v0.2

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Update app.py

c64277b verified 3 months ago

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	from transformers import AutoModelForCausalLM, AutoTokenizer
	# import gradio as gr

	# Load pre-trained model and tokenizer
	model_name = "mistralai/Mistral-7B-Instruct-v0.2"
	tokenizer = AutoTokenizer.from_pretrained(model_name)
	model = AutoModelForCausalLM.from_pretrained(model_name)

	def generate_response(input_text):
	"""
	Generate response based on the given user messages.
	Parameters:
	- input_text (str): A single string containing all user messages.
	Returns:
	- response (str): The generated response.
	"""
	# Tokenize the input text
	inputs = tokenizer.encode(input_text, return_tensors="pt", max_length=512, truncation=True)
	# Generate response
	generated_ids = model.generate(inputs, max_length=1024, do_sample=True)
	# Decode the generated response
	response = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
	return response

	# Define Gradio interface components
	input_chat = gr.Textbox(lines=5, label="Input Chat", placeholder="Enter chat messages...")
	output_response = gr.Textbox(label="Generated Response", placeholder="Generated response will appear here...")

	# Create Gradio interface
	gr.Interface(generate_response, input_chat, output_response,
	title="Chat Response Generation",
	description="Generate responses based on user messages using Mistral AI model.",
	theme="default",
	allow_flagging="never").launch()