app.py

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

# 1. Setup the Model Name
model_name = "haykgrigorian/TimeCapsuleLLM-v2-llama-1.2B"

# 2. Load the Model and Tokenizer
print("Loading model... this usually takes 1-2 minutes on first run.")
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# 3. Define the Generate Function
def generate_text(prompt, max_tokens=100, temperature=0.7):
    # FIX: We added return_token_type_ids=False to stop the error
    inputs = tokenizer(prompt, return_tensors="pt", return_token_type_ids=False)
    
    # Generate
    with torch.no_grad():
        outputs = model.generate(
            **inputs, 
            max_new_tokens=int(max_tokens),
            temperature=float(temperature),
            do_sample=True,
            pad_token_id=tokenizer.eos_token_id
        )
    
    # Decode result
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# 4. Launch the Gradio Interface
iface = gr.Interface(
    fn=generate_text,
    inputs=[
        gr.Textbox(label="Prompt", placeholder="Enter your text here..."),
        gr.Slider(minimum=10, maximum=300, value=100, label="Max New Tokens"),
        gr.Slider(minimum=0.1, maximum=1.0, value=0.7, label="Temperature")
    ],
    outputs="text",
    title="TimeCapsule LLM API",
    description="API for n8n connection."
)

iface.launch()