app.py

import gradio as gr
import tempfile
import imageio
import torch
import time
import os
import openai
from transformers import pipeline
from diffusers import DiffusionPipeline

# ---------- Secret Key Setup ----------
openai.api_key = os.getenv("OPENAI_KEY")  # ✅ Hugging Face Space Secret

# ---------- Config ----------
AVAILABLE_MODELS = {
    "Codette Fine-Tuned (v9)": "ft:gpt-4.1-2025-04-14:raiffs-bits:codette-final:BO907H7Z",
    "GPT-2 (small, fast)": "gpt2",
    "Falcon (TII UAE)": "tiiuae/falcon-7b-instruct",
    "Mistral (OpenAccess)": "mistralai/Mistral-7B-v0.1"
}

device = "cuda" if torch.cuda.is_available() else "cpu"
text_model_cache = {}
chat_memory = {}
MAX_PROMPTS_PER_SESSION = 5
THROTTLE_SECONDS = 30
last_usage_time = {}

# ---------- Load Image Generator ----------
try:
    image_generator = DiffusionPipeline.from_pretrained(
        "runwayml/stable-diffusion-v1-5",
        safety_checker=None,
        torch_dtype=torch.float16 if device == "cuda" else torch.float32
    )
    image_generator.to(device)
    image_enabled = True
except Exception as e:
    print(f"[Image Model Load Error]: {e}")
    image_generator = None
    image_enabled = False

# ---------- Load Video Generator ----------
try:
    video_pipeline = DiffusionPipeline.from_pretrained(
        "damo-vilab/text-to-video-ms-1.7b",
        safety_checker=None,
        torch_dtype=torch.float16 if device == "cuda" else torch.float32
    )
    video_pipeline.to(device)
    video_enabled = True
except Exception as e:
    print(f"[Video Model Load Error]: {e}")
    video_pipeline = None
    video_enabled = False

# ---------- Rate-Limited Terminal ----------
def codette_terminal_limited(prompt, model_name, generate_image, generate_video, session_id, batch_size, video_steps, fps):
    if session_id not in chat_memory:
        chat_memory[session_id] = []

    if prompt.lower() in ["exit", "quit"]:
        chat_memory[session_id] = []
        yield "🧠 Codette signing off... Session reset.", None, None
        return

    if model_name == "Codette Fine-Tuned (v9)":
        count = sum(1 for line in chat_memory[session_id] if line.startswith("🖋️ You >"))
        if count >= MAX_PROMPTS_PER_SESSION:
            yield "[🛑 Limit] Max 5 prompts per session.", None, None
            return
        now = time.time()
        if now - last_usage_time.get(session_id, 0) < THROTTLE_SECONDS:
            wait = int(THROTTLE_SECONDS - (now - last_usage_time[session_id]))
            yield f"[⏳ Wait] Try again in {wait} sec.", None, None
            return
        last_usage_time[session_id] = now

    if model_name == "Codette Fine-Tuned (v9)":
        try:
            response = openai.ChatCompletion.create(
                model=AVAILABLE_MODELS[model_name],
                messages=[{"role": "user", "content": prompt}],
                temperature=0.7,
                max_tokens=256
            )
            output = response.choices[0].message.content.strip()
        except Exception as e:
            yield f"[OpenAI error]: {e}", None, None
            return
    else:
        if model_name not in text_model_cache:
            try:
                text_model_cache[model_name] = pipeline(
                    "text-generation",
                    model=AVAILABLE_MODELS[model_name],
                    device=0 if device == "cuda" else -1
                )
            except Exception as e:
                yield f"[Text model error]: {e}", None, None
                return
        try:
            output = text_model_cache[model_name](prompt, max_length=100, do_sample=True, num_return_sequences=1)[0]['generated_text'].strip()
        except Exception as e:
            yield f"[Generation error]: {e}", None, None
            return

    response_so_far = ""
    for char in output:
        response_so_far += char
        temp_log = chat_memory[session_id][:]
        temp_log.append(f"🖋️ You > {prompt}")
        temp_log.append(f"🧠 Codette > {response_so_far}")
        yield "\n".join(temp_log[-10:]), None, None
        time.sleep(0.01)

    chat_memory[session_id].append(f"🖋️ You > {prompt}")
    chat_memory[session_id].append(f"🧠 Codette > {output}")

    imgs, vid = None, None
    if generate_image and image_enabled:
        try:
            result = image_generator(prompt, num_images_per_prompt=batch_size)
            imgs = result.images
        except Exception as e:
            response_so_far += f"\n[Image error]: {e}"

    if generate_video and video_enabled:
        try:
            result = video_pipeline(prompt, num_inference_steps=video_steps)
            frames = result.frames
            temp_video_path = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False).name
            imageio.mimsave(temp_video_path, frames, fps=fps)
            vid = temp_video_path
        except Exception as e:
            response_so_far += f"\n[Video error]: {e}"

    yield "\n".join(chat_memory[session_id][-10:]), imgs, vid

# ---------- Gradio UI ----------
with gr.Blocks(title="🧬 Codette Terminal – Streamed AI Chat") as demo:
    gr.Markdown("## 🧬 Codette Terminal (Chat + Image + Video + Rate-Limited Access)")
    gr.Markdown("Type a prompt, select a model, and generate responses, images, or video. Type `'exit'` to reset.")

    with gr.Row():
        session_id = gr.Textbox(value="session_default", visible=False)
        model_dropdown = gr.Dropdown(choices=list(AVAILABLE_MODELS.keys()), value="GPT-2 (small, fast)", label="Language Model")

    with gr.Row():
        generate_image_toggle = gr.Checkbox(label="Generate Image(s)?", value=False, interactive=image_enabled)
        generate_video_toggle = gr.Checkbox(label="Generate Video?", value=False, interactive=video_enabled)

    with gr.Row():
        batch_size_slider = gr.Slider(label="Number of Images", minimum=1, maximum=4, step=1, value=1)
        video_steps_slider = gr.Slider(label="Video Inference Steps", minimum=10, maximum=100, step=10, value=50)
        fps_slider = gr.Slider(label="Video FPS", minimum=4, maximum=24, step=2, value=8)

    user_input = gr.Textbox(label="Your Prompt", placeholder="e.g. A robot dreaming on Mars", lines=1)
    output_text = gr.Textbox(label="Codette Output", lines=15, interactive=False)
    output_image = gr.Gallery(label="Generated Image(s)", columns=2)
    output_video = gr.Video(label="Generated Video")

    user_input.submit(
        codette_terminal_limited,
        inputs=[
            user_input, model_dropdown, generate_image_toggle, generate_video_toggle,
            session_id, batch_size_slider, video_steps_slider, fps_slider
        ],
        outputs=[output_text, output_image, output_video]
    )

# ---------- Launch ----------
if __name__ == "__main__":
    demo.launch(mcp_server=True)  # ✅ tool-ready