# import streamlit as st
# from PIL import Image
# import base64
# import transformers



# model_name = 'Intel/neural-chat-7b-v3-1'
# model = transformers.AutoModelForCausalLM.from_pretrained(model_name)
# tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)

# def generate_response(system_input, user_input):

#     # Format the input using the provided template
#     prompt = f"### System:\n{system_input}\n### User:\n{user_input}\n### Assistant:\n"

#     # Tokenize and encode the prompt
#     inputs = tokenizer.encode(prompt, return_tensors="pt", add_special_tokens=False)

#     # Generate a response
#     outputs = model.generate(inputs, max_length=1000, num_return_sequences=1)
#     response = tokenizer.decode(outputs[0], skip_special_tokens=True)

#     # Extract only the assistant's response
#     return response.split("### Assistant:\n")[-1]


# # Example usage
# system_input = "You are a employee in the customer succes department of a company called Retraced that works in sustainability and traceability"
# prompt = st.text_input(str("Insert here you prompt?"))
# response = generate_response(system_input, prompt)
# st.write(response)