app.py

# app.py
import os
import time
import requests
import gradio as gr
import google.generativeai as genai
from google.api_core.exceptions import ResourceExhausted

# -----------------------
# Config / Secrets
# -----------------------
GEMINI_API_KEY = os.environ.get("GEMINI_API_KEY")
HF_API_TOKEN = os.environ.get("HF_API_TOKEN")  # required for TTS
HF_TTS_MODEL = os.environ.get("HF_TTS_MODEL", "microsoft/speecht5_tts")
AUDIO_TMP_DIR = "/tmp"

if not GEMINI_API_KEY:
    raise RuntimeError("Missing GEMINI_API_KEY in environment. Add it to HF Space Secrets.")

if not HF_API_TOKEN:
    print("Warning: HF_API_TOKEN not set. Audio will be unavailable until set in Space Secrets.")

# Configure Gemini SDK
genai.configure(api_key=GEMINI_API_KEY)
gemini_model = genai.GenerativeModel("gemini-2.5-flash")

# -----------------------
# In-memory chat memory
# -----------------------
class SimpleMemory:
    def __init__(self, max_messages=40):
        self.max_messages = max_messages
        self.history = []  # list of (role, text) where role in {"user","bot"}

    def add(self, role, text):
        self.history.append((role, text))
        if len(self.history) > self.max_messages:
            self.history = self.history[-self.max_messages:]

    def as_prompt_text(self):
        lines = []
        for role, txt in self.history:
            if role == "user":
                lines.append(f"User: {txt}")
            else:
                lines.append(f"Chatbot: {txt}")
        return "\n".join(lines)

memory = SimpleMemory(max_messages=40)

# -----------------------
# Prompt template
# -----------------------
PROMPT_TEMPLATE = """You are a helpful assistant.
{chat_history}
User: {user_message}
Chatbot:"""

# -----------------------
# Robust Gemini generator (tries multiple formats)
# Returns (text, error)
# -----------------------
def generate_text_with_gemini(user_message):
    chat_history_text = memory.as_prompt_text()
    full_prompt = PROMPT_TEMPLATE.format(chat_history=chat_history_text, user_message=user_message)

    # 1) raw prompt
    try:
        resp = gemini_model.generate_content(full_prompt)
        text = getattr(resp, "text", None) or str(resp)
        return text, None
    except ResourceExhausted as e:
        print("Gemini quota exhausted (raw):", e)
        return None, "Gemini quota exceeded. Please try again later."
    except Exception as e1:
        print("generate_content(raw) failed, trying messages:", repr(e1))

    # 2) messages with plain content
    try:
        messages = [
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": full_prompt}
        ]
        resp = gemini_model.generate_content(messages=messages)
        text = getattr(resp, "text", None) or str(resp)
        return text, None
    except ResourceExhausted as e:
        print("Gemini quota exhausted (messages):", e)
        return None, "Gemini quota exceeded. Please try again later."
    except Exception as e2:
        print("generate_content(messages) failed, trying typed content:", repr(e2))

    # 3) messages with typed content
    try:
        messages2 = [
            {"role": "system", "content": [{"type": "text", "text": "You are a helpful assistant."}]},
            {"role": "user", "content": [{"type": "text", "text": full_prompt}]}
        ]
        resp = gemini_model.generate_content(messages=messages2)
        text = getattr(resp, "text", None) or str(resp)
        return text, None
    except ResourceExhausted as e:
        print("Gemini quota exhausted (messages2):", e)
        return None, "Gemini quota exceeded. Please try again later."
    except Exception as efinal:
        print("Gemini all attempts failed:", repr(efinal))
        return None, f"Gemini error: {repr(efinal)}"

# -----------------------
# Hugging Face Router-aware TTS
# Tries legacy api-inference endpoint, then router.huggingface.co
# Returns (path, error)
# -----------------------
def generate_audio_hf_inference(text):
    if not HF_API_TOKEN:
        return "", "HF_API_TOKEN not configured for TTS."

    model = HF_TTS_MODEL  # e.g. "microsoft/speecht5_tts"
    router_url = f"/static-proxy?url=https%3A%2F%2Frouter.huggingface.co%2Fmodels%2F%3Cspan class="hljs-subst">{model}"
    legacy_url = f"/static-proxy?url=https%3A%2F%2Fapi-inference.huggingface.co%2Fmodels%2F%3Cspan class="hljs-subst">{model}"

    headers = {"Authorization": f"Bearer {HF_API_TOKEN}"}
    payload = {"inputs": text}

    def _save_bytes(content, content_type_hint=""):
        ct = content_type_hint or ""
        ext = ".mp3" if "mpeg" in ct or "audio/mpeg" in ct else ".wav"
        filename = f"audio_{int(time.time()*1000)}_{abs(hash(text))%100000}{ext}"
        path = os.path.join(AUDIO_TMP_DIR, filename)
        with open(path, "wb") as f:
            f.write(content)
        return path

    last_err = None
    for url in [legacy_url, router_url]:
        try:
            h = headers.copy()
            h["Accept"] = "audio/mpeg, audio/wav, */*"
            resp = requests.post(url, headers=h, json=payload, timeout=60)
        except Exception as e:
            last_err = f"HuggingFace request to {url} failed: {e}"
            print(last_err)
            continue

        if resp.status_code == 410:
            last_err = f"HuggingFace returned 410 for {url}: {resp.text}"
            print(last_err)
            continue

        if resp.status_code == 200:
            try:
                content_type = resp.headers.get("content-type", "")
                path = _save_bytes(resp.content, content_type)
                print(f"HuggingFace TTS: audio saved to {path} using URL {url} (content-type={content_type})")
                return path, ""
            except Exception as e:
                last_err = f"Failed to save HF audio from {url}: {e}"
                print(last_err)
                continue
        else:
            try:
                body = resp.json()
            except Exception:
                body = resp.text
            last_err = f"HuggingFace TTS error {resp.status_code} from {url}: {body}"
            print(last_err)
            if resp.status_code in (401, 403):
                # auth problem — break early
                break
            continue

    return "", last_err or "Unknown HuggingFace error"

# -----------------------
# Convert memory -> messages list for Gradio
# -----------------------
def convert_memory_to_messages(history):
    messages = []
    for role, msg in history:
        role_out = "assistant" if role == "bot" else "user"
        messages.append({"role": role_out, "content": msg})
    return messages

# -----------------------
# Combined chat workflow
# Returns (messages_list, audio_path, error)
# -----------------------
def process_user_message(user_message):
    text, gen_err = generate_text_with_gemini(user_message)
    if gen_err:
        memory.add("user", user_message)
        fallback = "Sorry — the assistant is temporarily unavailable: " + gen_err
        memory.add("bot", fallback)
        return convert_memory_to_messages(memory.history), "", gen_err

    memory.add("user", user_message)
    memory.add("bot", text)

    audio_path, audio_err = generate_audio_hf_inference(text)
    if audio_err:
        print("Audio generation error (HF):", audio_err)

    return convert_memory_to_messages(memory.history), audio_path or "", audio_err or ""

# -----------------------
# Gradio UI (Blocks) with debug UI
# -----------------------
with gr.Blocks() as demo:
    gr.Markdown("## 🤖 Gemini + Hugging Face TTS Chatbot\n\nAudio generated via Hugging Face Inference (router).")
    chatbot = gr.Chatbot()
    with gr.Row():
        txt = gr.Textbox(show_label=False, placeholder="Type your message and press Enter")
        send_btn = gr.Button("Send")
    audio_player = gr.Audio(label="Last reply audio (if available)", visible=False)
    debug_box = gr.Textbox(label="Last debug message (audio path or error)", interactive=False, visible=False)

    def submit_message(message):
        messages, audio_path, err = process_user_message(message)
        if audio_path:
            debug_msg = f"Audio saved: {audio_path}"
            return messages, gr.update(value=audio_path, visible=True), gr.update(value=debug_msg, visible=True)
        elif err:
            return messages, gr.update(value=None, visible=False), gr.update(value=err, visible=True)
        else:
            return messages, gr.update(value=None, visible=False), gr.update(value="No audio generated", visible=True)

    send_btn.click(fn=submit_message, inputs=[txt], outputs=[chatbot, audio_player, debug_box])
    txt.submit(fn=submit_message, inputs=[txt], outputs=[chatbot, audio_player, debug_box])

# Launch
if __name__ == "__main__":
    demo.launch(debug=True)