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Create app.py

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# app.py
import os
import gradio as gr
from huggingface_hub import InferenceApi
from typing import List, Tuple

HF_TOKEN = os.environ.get("HUGGINGFACEHUB_API_TOKEN")
if not HF_TOKEN:
    raise RuntimeError("Set HUGGINGFACEHUB_API_TOKEN in your Space secrets/settings.")

# Default models (you can replace these with your own hosted model names)
TEXT_MODEL = os.environ.get("YAGA_TEXT_MODEL", "mistralai/Mistral-7B-v0.1")  # hosted generation model
IMAGE_CAPTION_MODEL = os.environ.get("YAGA_IMAGE_MODEL", "Salesforce/blip-image-captioning-large")
WHISPER_MODEL = os.environ.get("YAGA_WHISPER_MODEL", "openai/whisper-large-v2")

# Inference API clients
text_api = InferenceApi(repo_id=TEXT_MODEL, token=HF_TOKEN)
image_api = InferenceApi(repo_id=IMAGE_CAPTION_MODEL, token=HF_TOKEN)
whisper_api = InferenceApi(repo_id=WHISPER_MODEL, token=HF_TOKEN)

SYSTEM_PROMPT = (
    "You are YAGA — an advanced, helpful, and safe AI assistant. "
    "Answer concisely and clearly. If user provided an image or audio, "
    "use the provided descriptions/transcripts as context."
)

# Simple in-memory conversation store per session id (Gradio session)
# For Spaces this is ephemeral; for production, persist to DB.
CONV_STORE = {}

def build_prompt(history: List[Tuple[str, str]], extra_context: str | None):
    parts = [f"SYSTEM: {SYSTEM_PROMPT}"]
    if extra_context:
        parts.append(f"CONTEXT: {extra_context}")
    for role, text in history:
        parts.append(f"{role}: {text}")
    parts.append("Assistant:")
    return "\n".join(parts)

def get_session_history(session_id: str):
    return CONV_STORE.setdefault(session_id, [])

def clear_chat(session_id: str):
    CONV_STORE[session_id] = []
    return []

def transcribe_audio(audio_bytes):
    # Whisper inference: input is bytes or file-like; InferenceApi accepts bytes
    try:
        out = whisper_api(inputs=audio_bytes)
        # InferenceApi may return plain text or dict depending on model; handle common shapes
        if isinstance(out, dict) and "text" in out:
            return out["text"]
        if isinstance(out, str):
            return out
        # fallback: try join if list
        return str(out)
    except Exception as e:
        return f"[transcription_error: {e}]"

def caption_image(image_bytes):
    try:
        out = image_api(inputs=image_bytes)
        # Many image caption models return plain text
        if isinstance(out, dict) and "generated_text" in out:
            return out["generated_text"]
        if isinstance(out, str):
            return out
        # fallback
        return str(out)
    except Exception as e:
        return f"[caption_error: {e}]"

def generate_reply(prompt: str, max_tokens: int = 256, temperature: float = 0.7):
    params = {"max_new_tokens": max_tokens, "temperature": temperature, "top_p":0.95}
    # Some Inference API models return dicts or plain text; just return textual result
    out = text_api(inputs=prompt, parameters=params)
    if isinstance(out, dict):
        # common place: {"generated_text": "..."}
        if "generated_text" in out:
            return out["generated_text"]
        # some endpoints return list of candidates
        if "error" in out:
            return f"[generation_error: {out['error']}]"
        try:
            return str(out)
        except:
            return "[generation_error: unexpected format]"
    if isinstance(out, str):
        return out
    return str(out)

def respond(session_id, user_message, image_file, audio_file, max_tokens=256, temperature=0.7):
    history = get_session_history(session_id)

    extra_ctx_parts = []
    if image_file is not None:
        img_bytes = image_file.read()
        caption = caption_image(img_bytes)
        extra_ctx_parts.append(f"Image caption: {caption}")
    if audio_file is not None:
        audio_bytes = audio_file.read()
        transcript = transcribe_audio(audio_bytes)
        extra_ctx_parts.append(f"Audio transcript: {transcript}")

    extra_context = "\n".join(extra_ctx_parts) if extra_ctx_parts else None

    # Append user message to history
    history.append(("User", user_message))

    prompt = build_prompt(history, extra_context)
    reply = generate_reply(prompt, max_tokens=int(max_tokens), temperature=float(temperature))

    history.append(("Assistant", reply))
    # Keep last N turns
    CONV_STORE[session_id] = history[-12*2:]
    # Convert to chat-like pairs for the frontend
    chat_pairs = [(r[0], r[1]) for r in CONV_STORE[session_id]]
    return chat_pairs, ""

with gr.Blocks(title="YAGA — HF Space Prototype") as demo:
    gr.Markdown("## YAGA — Multi-turn chat (Hugging Face Space)\nText, image and audio inputs supported.\n\n**Important:** Add your Hugging Face token to Space secrets as `HUGGINGFACEHUB_API_TOKEN`.")
    with gr.Row():
        with gr.Column(scale=3):
            chatbot = gr.Chatbot()
            msg = gr.Textbox(placeholder="Type your message here...", label="Message")
            with gr.Row():
                img = gr.Image(type="bytes", label="Optional image")
                aud = gr.Audio(source="upload", type="file", label="Optional audio")
            with gr.Row():
                max_t = gr.Slider(32, 1024, value=256, step=32, label="Max new tokens")
                temp = gr.Slider(0.0, 1.0, value=0.7, step=0.05, label="Temperature")
            with gr.Row():
                send = gr.Button("Send")
                clear = gr.Button("Clear chat")
        with gr.Column(scale=1):
            gr.Markdown("### Session Info")
            session_id_display = gr.Textbox(value="", interactive=False, label="Session ID (auto)")
            gr.Markdown("### Hints")
            gr.Markdown("- Upload an image to give the model visual context (it will be captioned).\n- Upload audio to transcribe via Whisper.\n- Replace `TEXT_MODEL` env var with your model repo for a custom model.")
    # use gradio session_state for session id
    def get_sid():
        import uuid
        return str(uuid.uuid4())
    sid = get_sid()
    session_id_display.value = sid

    def on_send(message, image_file, audio_file, max_tokens, temperature):
        pairs, _ = respond(sid, message, image_file, audio_file, max_tokens, temperature)
        # convert pairs to Chatbot messages: list of [user, assistant] pairs
        chat_display = []
        it = iter(pairs)
        # pairs is list of (role, text)
        # Build sequential chat list
        for role, text in pairs:
            # only show in alternating style: we will append tuple (role, text)
            chat_display.append((role, text))
        # For gradio Chatbot, we need list of [user, assistant] pairs grouped.
        # But we'll just return the last assistant message to append:
        last_user = pairs[-2][1] if len(pairs) >= 2 else ""
        last_assistant = pairs[-1][1]
        return gr.update(value=chat_display), ""

    send.click(on_send, [msg, img, aud, max_t, temp], [chatbot, msg])
    def on_clear():
        clear_chat(sid)
        return [], ""
    clear.click(on_clear, [], [chatbot, msg])

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

	# app.py
	import os
	import gradio as gr
	from huggingface_hub import InferenceApi
	from typing import List, Tuple

	HF_TOKEN = os.environ.get("HUGGINGFACEHUB_API_TOKEN")
	if not HF_TOKEN:
	raise RuntimeError("Set HUGGINGFACEHUB_API_TOKEN in your Space secrets/settings.")

	# Default models (you can replace these with your own hosted model names)
	TEXT_MODEL = os.environ.get("YAGA_TEXT_MODEL", "mistralai/Mistral-7B-v0.1") # hosted generation model
	IMAGE_CAPTION_MODEL = os.environ.get("YAGA_IMAGE_MODEL", "Salesforce/blip-image-captioning-large")
	WHISPER_MODEL = os.environ.get("YAGA_WHISPER_MODEL", "openai/whisper-large-v2")

	# Inference API clients
	text_api = InferenceApi(repo_id=TEXT_MODEL, token=HF_TOKEN)
	image_api = InferenceApi(repo_id=IMAGE_CAPTION_MODEL, token=HF_TOKEN)
	whisper_api = InferenceApi(repo_id=WHISPER_MODEL, token=HF_TOKEN)

	SYSTEM_PROMPT = (
	"You are YAGA — an advanced, helpful, and safe AI assistant. "
	"Answer concisely and clearly. If user provided an image or audio, "
	"use the provided descriptions/transcripts as context."
	)

	# Simple in-memory conversation store per session id (Gradio session)
	# For Spaces this is ephemeral; for production, persist to DB.
	CONV_STORE = {}

	def build_prompt(history: List[Tuple[str, str]], extra_context: str \| None):
	parts = [f"SYSTEM: {SYSTEM_PROMPT}"]
	if extra_context:
	parts.append(f"CONTEXT: {extra_context}")
	for role, text in history:
	parts.append(f"{role}: {text}")
	parts.append("Assistant:")
	return "\n".join(parts)

	def get_session_history(session_id: str):
	return CONV_STORE.setdefault(session_id, [])

	def clear_chat(session_id: str):
	CONV_STORE[session_id] = []
	return []

	def transcribe_audio(audio_bytes):
	# Whisper inference: input is bytes or file-like; InferenceApi accepts bytes
	try:
	out = whisper_api(inputs=audio_bytes)
	# InferenceApi may return plain text or dict depending on model; handle common shapes
	if isinstance(out, dict) and "text" in out:
	return out["text"]
	if isinstance(out, str):
	return out
	# fallback: try join if list
	return str(out)
	except Exception as e:
	return f"[transcription_error: {e}]"

	def caption_image(image_bytes):
	try:
	out = image_api(inputs=image_bytes)
	# Many image caption models return plain text
	if isinstance(out, dict) and "generated_text" in out:
	return out["generated_text"]
	if isinstance(out, str):
	return out
	# fallback
	return str(out)
	except Exception as e:
	return f"[caption_error: {e}]"

	def generate_reply(prompt: str, max_tokens: int = 256, temperature: float = 0.7):
	params = {"max_new_tokens": max_tokens, "temperature": temperature, "top_p":0.95}
	# Some Inference API models return dicts or plain text; just return textual result
	out = text_api(inputs=prompt, parameters=params)
	if isinstance(out, dict):
	# common place: {"generated_text": "..."}
	if "generated_text" in out:
	return out["generated_text"]
	# some endpoints return list of candidates
	if "error" in out:
	return f"[generation_error: {out['error']}]"
	try:
	return str(out)
	except:
	return "[generation_error: unexpected format]"
	if isinstance(out, str):
	return out
	return str(out)

	def respond(session_id, user_message, image_file, audio_file, max_tokens=256, temperature=0.7):
	history = get_session_history(session_id)

	extra_ctx_parts = []
	if image_file is not None:
	img_bytes = image_file.read()
	caption = caption_image(img_bytes)
	extra_ctx_parts.append(f"Image caption: {caption}")
	if audio_file is not None:
	audio_bytes = audio_file.read()
	transcript = transcribe_audio(audio_bytes)
	extra_ctx_parts.append(f"Audio transcript: {transcript}")

	extra_context = "\n".join(extra_ctx_parts) if extra_ctx_parts else None

	# Append user message to history
	history.append(("User", user_message))

	prompt = build_prompt(history, extra_context)
	reply = generate_reply(prompt, max_tokens=int(max_tokens), temperature=float(temperature))

	history.append(("Assistant", reply))
	# Keep last N turns
	CONV_STORE[session_id] = history[-12*2:]
	# Convert to chat-like pairs for the frontend
	chat_pairs = [(r[0], r[1]) for r in CONV_STORE[session_id]]
	return chat_pairs, ""

	with gr.Blocks(title="YAGA — HF Space Prototype") as demo:
	gr.Markdown("## YAGA — Multi-turn chat (Hugging Face Space)\nText, image and audio inputs supported.\n\nImportant: Add your Hugging Face token to Space secrets as `HUGGINGFACEHUB_API_TOKEN`.")
	with gr.Row():
	with gr.Column(scale=3):
	chatbot = gr.Chatbot()
	msg = gr.Textbox(placeholder="Type your message here...", label="Message")
	with gr.Row():
	img = gr.Image(type="bytes", label="Optional image")
	aud = gr.Audio(source="upload", type="file", label="Optional audio")
	with gr.Row():
	max_t = gr.Slider(32, 1024, value=256, step=32, label="Max new tokens")
	temp = gr.Slider(0.0, 1.0, value=0.7, step=0.05, label="Temperature")
	with gr.Row():
	send = gr.Button("Send")
	clear = gr.Button("Clear chat")
	with gr.Column(scale=1):
	gr.Markdown("### Session Info")
	session_id_display = gr.Textbox(value="", interactive=False, label="Session ID (auto)")
	gr.Markdown("### Hints")
	gr.Markdown("- Upload an image to give the model visual context (it will be captioned).\n- Upload audio to transcribe via Whisper.\n- Replace `TEXT_MODEL` env var with your model repo for a custom model.")
	# use gradio session_state for session id
	def get_sid():
	import uuid
	return str(uuid.uuid4())
	sid = get_sid()
	session_id_display.value = sid

	def on_send(message, image_file, audio_file, max_tokens, temperature):
	pairs, _ = respond(sid, message, image_file, audio_file, max_tokens, temperature)
	# convert pairs to Chatbot messages: list of [user, assistant] pairs
	chat_display = []
	it = iter(pairs)
	# pairs is list of (role, text)
	# Build sequential chat list
	for role, text in pairs:
	# only show in alternating style: we will append tuple (role, text)
	chat_display.append((role, text))
	# For gradio Chatbot, we need list of [user, assistant] pairs grouped.
	# But we'll just return the last assistant message to append:
	last_user = pairs[-2][1] if len(pairs) >= 2 else ""
	last_assistant = pairs[-1][1]
	return gr.update(value=chat_display), ""

	send.click(on_send, [msg, img, aud, max_t, temp], [chatbot, msg])
	def on_clear():
	clear_chat(sid)
	return [], ""
	clear.click(on_clear, [], [chatbot, msg])

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