app.py

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

MODEL_NAME = "basmala12/smollm_finetuning5"

# Load tokenizer & model once at startup
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
model.eval()


def respond(message, history, system_message, max_tokens, temperature, top_p):
    """
    Safer, generic factual mode:
    - uses chat template properly
    - deterministic decoding (no sampling)
    - generic conciseness filter (1–2 sentences, word cap)
    - NO hardcoded answers for specific questions
    """

    # Build conversation for chat template
    messages = [{"role": "system", "content": system_message}]

    # history is a list of {"role": "user"/"assistant", "content": str}
    messages.extend(history)

    # Add current user message
    messages.append({"role": "user", "content": message})

    # Apply chat template
    prompt = tokenizer.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True,
    )

    inputs = tokenizer(prompt, return_tensors="pt")

    # Deterministic generation: safer, less hallucination than sampling
    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=max_tokens,
            do_sample=False,   # no randomness
            temperature=0.0,   # ignored when do_sample=False, but explicit
        )

    # Take only the newly generated tokens (after the prompt)
    generated_tokens = outputs[0][inputs["input_ids"].shape[1]:]
    answer = tokenizer.decode(generated_tokens, skip_special_tokens=True).strip()

    # ---------- Generic conciseness: first 1–2 sentences, word cap ----------
    import re

    # Keep only first 1–2 sentences
    sentences = re.split(r'(?<=[.!?])\s+', answer)
    answer = " ".join(sentences[:2])

    # Word cap (e.g. ~40 words)
    words = answer.split()
    if len(words) > 40:
        answer = " ".join(words[:40]) + "."

    return answer


chatbot = gr.ChatInterface(
    fn=respond,
    type="messages",
    additional_inputs=[
        gr.Textbox(
            value=(
                "Give short, factual answers with brief logical reasoning. "
                "If you are not sure, say you are not sure instead of guessing."
            ),
            label="System message",
        ),
        gr.Slider(1, 512, value=256, step=1, label="Max new tokens"),
        gr.Slider(0.1, 4.0, value=0.7, step=0.1, label="Temperature (ignored in deterministic mode)"),
        gr.Slider(0.1, 1.0, value=0.9, step=0.05, label="Top-p (ignored in deterministic mode)"),
    ],
)

if __name__ == "__main__":
    chatbot.launch()