app.py

import os
from pathlib import Path

import gradio as gr
from ctransformers import AutoModelForCausalLM
from huggingface_hub import hf_hub_download

# -----------------------------
# Configuration
# -----------------------------
# Option A: local file path inside your Space repo (recommended folder: "models/")
LOCAL_MODEL_FILE = os.environ.get("LOCAL_MODEL_FILE", "models/falcon3-1b-instruct-q4_k_m.gguf")

# Option B: download from Hugging Face Hub (set these in Space Variables)
MODEL_REPO_ID = os.environ.get("MODEL_REPO_ID", "")          # e.g. "TheBloke/Falcon3-1B-Instruct-GGUF"
MODEL_FILENAME = os.environ.get("MODEL_FILENAME", "")        # e.g. "falcon3-1b-instruct-q4_k_m.gguf"

# Inference knobs
MODEL_TYPE = os.environ.get("MODEL_TYPE", "falcon")
GPU_LAYERS = int(os.environ.get("GPU_LAYERS", "0"))
CONTEXT_LENGTH = int(os.environ.get("CONTEXT_LENGTH", "4096"))

# -----------------------------
# Resolve model path
# -----------------------------
APP_DIR = Path(__file__).resolve().parent
local_path = (APP_DIR / LOCAL_MODEL_FILE).resolve()

def resolve_model_path() -> str:
    # 1) Prefer local file (if you committed it to the Space repo)
    if local_path.exists():
        return str(local_path)

    # 2) Otherwise download from Hub (requires MODEL_REPO_ID + MODEL_FILENAME)
    if not MODEL_REPO_ID or not MODEL_FILENAME:
        raise FileNotFoundError(
            f"Model not found locally at: {local_path}\n\n"
            "AND Hub download is not configured.\n"
            "Fix ONE of these:\n"
            "A) Commit the GGUF into your Space repo (recommended: put it in /models).\n"
            "   - Ensure it exists at the path in LOCAL_MODEL_FILE.\n"
            "B) Configure Hub download:\n"
            "   - In Space Settings > Variables:\n"
            "       MODEL_REPO_ID = <your repo_id>\n"
            "       MODEL_FILENAME = <your .gguf filename>\n"
            "   - If repo is gated/private, add Space Secret:\n"
            "       HF_TOKEN = <your HF access token>\n"
        )

    token = os.environ.get("HF_TOKEN")  # optional; required for private/gated repos
    downloaded = hf_hub_download(
        repo_id=MODEL_REPO_ID,
        filename=MODEL_FILENAME,
        token=token,
    )
    return downloaded

MODEL_PATH = resolve_model_path()

# -----------------------------
# Load model
# -----------------------------
llm = AutoModelForCausalLM.from_pretrained(
    MODEL_PATH,
    model_type=MODEL_TYPE,
    gpu_layers=GPU_LAYERS,
    context_length=CONTEXT_LENGTH,
)

# -----------------------------
# Prompt template
# -----------------------------
SYSTEM_PROMPT = """
You are an expert instructional designer who writes clear,
engaging PowerPoint scripts with slide titles, bullet points,
and speaker notes.
""".strip()

def build_prompt(topic, slide_count):
    return f"""{SYSTEM_PROMPT}

Write a complete PowerPoint script with EXACTLY {slide_count} slides.

Topic: {topic}

Return the output in this structure:

Title Slide:
- Title:
- Subtitle:
- Speaker Notes:

Slide 1:
- Slide Title:
- Bullet Points:
- Speaker Notes:

Slide 2:
- Slide Title:
- Bullet Points:
- Speaker Notes:

Continue until you reach Slide {slide_count}.
"""

# -----------------------------
# Generation function
# -----------------------------
def generate_script(topic, slide_count, max_tokens, temperature, top_p):
    topic = (topic or "").strip()
    if not topic:
        return "Please enter a topic."

    try:
        slide_count = int(slide_count)
    except Exception:
        return "Number of slides must be a whole number."

    if slide_count < 1 or slide_count > 50:
        return "Number of slides must be between 1 and 50."

    prompt = build_prompt(topic, slide_count)

    output = ""
    for token in llm(
        prompt,
        max_new_tokens=int(max_tokens),
        temperature=float(temperature),
        top_p=float(top_p),
        stream=True,
    ):
        output += token

    return output.strip()

# -----------------------------
# Gradio UI
# -----------------------------
def build_ui():
    with gr.Blocks(title="Falcon3-1B PPT Script Generator") as demo:
        gr.Markdown(
            """
            # Falcon3-1B-Instruct (GGUF)
            ### PowerPoint Script Generator (Topic + Slide Count)

            Provide a topic and the number of slides you want.
            The model will generate a structured slide-by-slide script.
            """
        )

        with gr.Row():
            with gr.Column():
                topic = gr.Textbox(label="Topic", placeholder="e.g., AI Maturity Model")
                slide_count = gr.Number(label="Number of Slides", value=8, precision=0)

                max_tokens = gr.Slider(256, 4096, value=1500, label="Max Tokens")
                temperature = gr.Slider(0.1, 1.5, value=0.7, label="Temperature")
                top_p = gr.Slider(0.1, 1.0, value=0.9, label="Top-p")

                generate_btn = gr.Button("Generate Script")

            with gr.Column():
                output = gr.Textbox(label="Generated PowerPoint Script", lines=25)

        generate_btn.click(
            generate_script,
            inputs=[topic, slide_count, max_tokens, temperature, top_p],
            outputs=output,
        )

    return demo

demo = build_ui()

if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=int(os.environ.get("PORT", 7860)))