src/streamlit_app.py

# """
# Streamlit demo for the Math Tutor with multilingual & voice support.
# Run: streamlit run demo.py
# """

# import streamlit as st
# import io
# import wave

# from gtts import gTTS
# import tempfile
# from tutor.core import (
#     CurriculumLoader,
#     ChildASRAdapter,
#     ResponseScorer,
#     LearnerState,
#     FeedbackGenerator,
#     LocalProgressStore,
# )
# import os
# from huggingface_hub import InferenceClient

# client = InferenceClient(
#     provider="nscale",
#     api_key=os.environ["HF_TOKEN"],
# )

# # =========================
# # 🔊 VOICE ENGINE (NEW)
# # =========================
# def speak(text, lang="en"):
#     lang_map = {
#         "en": "en",
#         "fr": "fr",
#         "kin": "en"   # fallback safe
#     }

#     tts = gTTS(text=text, lang=lang_map.get(lang, "en"))

#     with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as f:
#         tts.save(f.name)
#         return f.name


# # =========================
# # PAGE CONFIG
# # =========================
# st.set_page_config(
#     page_title="Math Tutor",
#     page_icon="🧮",
#     layout="centered"
# )

# # =========================
# # SESSION STATE
# # =========================
# if 'curriculum' not in st.session_state:
#     st.session_state.curriculum = CurriculumLoader()
# if 'learner_state' not in st.session_state:
#     st.session_state.learner_state = LearnerState('demo_learner')
# if 'item_index' not in st.session_state:
#     st.session_state.item_index = 0
# if 'store' not in st.session_state:
#     st.session_state.store = LocalProgressStore()
#     st.session_state.store.add_learner('demo_learner', 'Demo Child', 'en')
# if 'asr' not in st.session_state:
#     st.session_state.asr = ChildASRAdapter()
# if 'scorer' not in st.session_state:
#     st.session_state.scorer = ResponseScorer()
# if 'language' not in st.session_state:
#     st.session_state.language = 'en'

# # =========================
# # MAIN UI
# # =========================
# st.title("🧮 AI Math Tutor for Early Learners")

# curriculum = st.session_state.curriculum
# learner_state = st.session_state.learner_state
# store = st.session_state.store
# asr = st.session_state.asr
# scorer = st.session_state.scorer

# # =========================
# # SIDEBAR
# # =========================
# with st.sidebar:
#     st.markdown("### ⚙️ Settings")

#     language_map = {
#         'English 🇬🇧': 'en',
#         'Français 🇫🇷': 'fr',
#         'Kinyarwanda 🇷🇼': 'kin'
#     }

#     lang_display = st.radio(
#         "Language:",
#         list(language_map.keys())
#     )

#     st.session_state.language = language_map[lang_display]

# # =========================
# # GET CURRENT ITEM
# # =========================
# if st.session_state.item_index < len(curriculum.items):

#     current_item = curriculum.items[st.session_state.item_index]

#     st.markdown("### 👂 Listen and Answer")

#     lang_key = f"stem_{st.session_state.language}"
#     question = current_item.get(lang_key, current_item.get('stem_en'))

#     st.markdown(f"**Question:** {question}")

#     # =========================
#     # 🔊 SPEAK QUESTION (NEW)
#     # =========================
#     if st.button("🔊 Listen Question"):
#         audio_q = speak(question, st.session_state.language)
#         st.audio(audio_q)

#     # =========================
#     # VISUAL GROUNDING (IMPROVED)
#     # =========================
#     visual = current_item.get('visual')

#     if visual:
#         parts = visual.split('_')
#         name = parts[0]

#         nums = [int(s) for s in parts if s.isdigit()]
#         count = nums[0] if nums else None

#         emoji_map = {
#             'apples': '🍎',
#             'goats': '🐐',
#             'beads': '🔵',
#             'default': '🔢'
#         }

#         symbol = emoji_map.get(name.lower(), '🔢')

#         if count:
#             st.markdown("### 🧠 Visual Learning")
#             st.markdown(symbol * min(count, 20))

#     # =========================
#     # INPUT
#     # =========================
#     transcript = st.text_input(
#         "Your Answer:"
#     )

#     # =========================
#     # SUBMIT
#     # =========================
#     if st.button("Submit Answer", type="primary"):

#         if transcript.strip():

#             # Score
#             correct = scorer.score_response(
#                 current_item.get('answer_int', 0),
#                 transcript,
#                 current_item
#             )

#             # Detect language (code-switch awareness)
#             detected_lang = asr.detect_language(transcript)

#             # Save
#             store.add_response(
#                 'demo_learner',
#                 current_item.get('skill'),
#                 current_item.get('id'),
#                 correct,
#                 transcript
#             )

#             learner_state.record_response(
#                 current_item.get('skill'),
#                 correct
#             )

#             # =========================
#             # FEEDBACK ENGINE
#             # =========================
#             feedback = FeedbackGenerator.generate_feedback(
#                 correct,
#                 st.session_state.language,
#                 current_item.get('answer_int')
#             )

#             # =========================
#             # CODE-SWITCH HANDLING (NEW)
#             # =========================
#             if detected_lang != st.session_state.language:
#                 feedback += " (I noticed mixed language input 👍)"

#             # =========================
#             # SHOW RESULT
#             # =========================
#             if correct:
#                 st.success(f"✅ {feedback}")
#             else:
#                 st.error(f"❌ {feedback}")

#             # =========================
#             # 🔊 VOICE RESPONSE (NEW)
#             # =========================
#             audio = speak(feedback, st.session_state.language)
#             st.audio(audio, autoplay=True)

#             # NEXT QUESTION
#             st.session_state.item_index += 1
#             st.rerun()

#         else:
#             st.warning("Please enter an answer")

# # =========================
# # END SCREEN
# # =========================
# else:
#     st.success("🎉 Great job! You finished!")

#     if st.button("Restart"):
#         st.session_state.item_index = 0
#         st.rerun()

import streamlit as st
import os
from huggingface_hub import InferenceClient

# -----------------------------
# PAGE SETUP
# -----------------------------
st.set_page_config(page_title="Text to Image AI", layout="centered")
st.title("🎨 Text → Image Generator (Pro)")

# -----------------------------
# LOAD TOKEN
# -----------------------------
HF_TOKEN = os.getenv("HF_TOKEN") or st.secrets.get("HF_TOKEN")

if not HF_TOKEN:
    st.error("❌ HF_TOKEN is missing. Add it in Secrets.")
    st.stop()

# -----------------------------
# HUGGING FACE CLIENT
# -----------------------------
client = InferenceClient(
    provider="nscale",
    api_key=HF_TOKEN,
)

# -----------------------------
# UI INPUT
# -----------------------------
prompt = st.text_input("Enter your prompt (e.g. astronaut riding a horse)")

# -----------------------------
# GENERATE IMAGE
# -----------------------------
if st.button("Generate Image"):
    if not prompt:
        st.warning("Please enter a prompt")
    else:
        with st.spinner("Generating image... ⏳"):
            try:
                image = client.text_to_image(
                    prompt,
                    model="stabilityai/stable-diffusion-xl-base-1.0",
                )

                st.image(image, caption=prompt)
                st.success("Done 🎉")

            except Exception as e:
                st.error("Error generating image:")
                st.write(str(e))