app.py

import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

# Initialize the model and tokenizer
cuda = "cuda:0" if torch.cuda.is_available() else "cpu"
model = AutoModelForCausalLM.from_pretrained("goendalf666/salesGPT_v2").to(cuda)
tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-1_5")

def interact_with_model(user_input):
    # Construct conversation text for the model
    conversation_text = (
        "You are in the role of a Salesman. "
        "Here is a conversation: "
        f"Customer: {user_input} Salesman: "
    )

    # Tokenize inputs
    inputs = tokenizer(conversation_text, return_tensors="pt").to(cuda)

    # Generate response
    outputs = model.generate(**inputs, max_length=512)
    response_text = tokenizer.batch_decode(outputs)[0]

    # Extract only the newly generated text
    new_text_start = len(conversation_text)
    new_generated_text = response_text[new_text_start:].strip()

    # Find where the next "Customer:" is, and truncate the text there
    end_index = new_generated_text.find("Customer:")
    if end_index != -1:
        new_generated_text = new_generated_text[:end_index].strip()

    # Ignore if the model puts "Salesman: " itself at the beginning
    if new_generated_text.startswith("Salesman:"):
        new_generated_text = new_generated_text[len("Salesman:"):].strip()

    # Return the model's response
    return new_generated_text

# Create Gradio Interface and launch it
iface = gr.Interface(fn=interact_with_model, inputs="text", outputs="text")
iface.launch()