import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load your fine-tuned GPT-2 model from Hugging Face
MODEL_NAME = "hackergeek98/therapist01"  # Replace with your model name
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)

# Initialize conversation history
conversation_history = ""

# Function to generate responses using GPT-2
def generate_response(user_input):
    global conversation_history

    # Update conversation history with user input
    conversation_history += f"User: {user_input}\n"
    
    # Tokenize the conversation history
    inputs = tokenizer(conversation_history, return_tensors="pt", truncation=True, max_length=1024)

    # Generate a response from the model
    outputs = model.generate(inputs['input_ids'], max_length=1024, num_return_sequences=1, no_repeat_ngram_size=2)

    # Decode the model's output
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)

    # Update conversation history with the model's response
    conversation_history += f"Therapist: {response}\n"

    # Return the therapist's response
    return response

# Create Gradio interface
interface = gr.Interface(
    fn=generate_response,
    inputs=gr.Textbox(label="Enter your message", lines=2),
    outputs=gr.Textbox(label="Therapist Response", lines=2),
    title="Virtual Therapist",
    description="A fine-tuned GPT-2 model acting as a virtual therapist. Chat with the model and receive responses as if you are talking to a therapist."
)

# Launch the app
if __name__ == "__main__":
    interface.launch()