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| import streamlit as st | |
| import requests | |
| import os | |
| import tempfile | |
| import io | |
| import soundfile as sf | |
| # Hugging Face API Key (Make sure to add it in your HF Space secrets) | |
| HF_API_KEY = os.getenv("HF_API_KEY") | |
| HEADERS = {"Authorization": f"Bearer {HF_API_KEY}"} | |
| # Hugging Face Model APIs | |
| HF_SPEECH_TO_TEXT_API = "https://api-inference.huggingface.co/models/openai/whisper-small" | |
| HF_CHATBOT_API = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct" | |
| HF_TEXT_TO_SPEECH_API = "https://api-inference.huggingface.co/models/facebook/mms-tts-eng" | |
| # Chinese Zodiac Avatars with Personalities | |
| avatars = { | |
| "Rat": "Clever and resourceful. Loves puzzles and word games.", | |
| "Ox": "Patient and wise. Encourages steady learning.", | |
| "Tiger": "Brave and adventurous. Loves storytelling!", | |
| "Rabbit": "Gentle and kind. Makes learning fun and safe.", | |
| "Dragon": "Bold and confident. Loves to challenge students!", | |
| "Snake": "Calm and analytical. Gives insightful explanations.", | |
| "Horse": "Energetic and playful. Encourages fast-paced learning!", | |
| "Goat": "Creative and artistic. Uses imagination in learning.", | |
| "Monkey": "Curious and mischievous. Always has fun facts!", | |
| "Rooster": "Diligent and disciplined. Helps with structured learning.", | |
| "Dog": "Loyal and friendly. Encourages confidence.", | |
| "Pig": "Easygoing and supportive. Encourages relaxed learning." | |
| } | |
| # Function to process speech-to-text | |
| def speech_to_text(audio_path): | |
| with open(audio_path, "rb") as f: | |
| files = {"file": f} | |
| response = requests.post(HF_SPEECH_TO_TEXT_API, headers=HEADERS, files=files) | |
| return response.json().get("text", "Could not transcribe audio.") | |
| # Function to get chatbot response | |
| def chatbot_response(user_input, avatar): | |
| personality = avatars.get(avatar, "Friendly and supportive.") | |
| prompt = f"You are {avatar}, an AI speaking coach. You are {personality}.\n" | |
| prompt += f"Help the student practice English in a fun and engaging way.\n" | |
| prompt += f"User: {user_input}\n{avatar}:" | |
| payload = {"inputs": prompt} | |
| response = requests.post(HF_CHATBOT_API, headers=HEADERS, json=payload) | |
| return response.json().get("generated_text", "I'm here to help!") | |
| # Function for text-to-speech | |
| def text_to_speech(text): | |
| payload = {"inputs": text} | |
| response = requests.post(HF_TEXT_TO_SPEECH_API, headers=HEADERS, json=payload) | |
| return response.content | |
| # Streamlit UI | |
| st.title("🎙️ AI Speaking Coach - Talking Pals") | |
| st.write("Choose an avatar and start speaking!") | |
| # Select Avatar | |
| avatar = st.selectbox("Pick your speaking coach:", list(avatars.keys())) | |
| st.write(f"**{avatar}** - {avatars[avatar]}") | |
| # Upload or record audio | |
| audio_file = st.file_uploader("Record or upload your voice", type=["wav", "mp3"]) | |
| show_text = st.checkbox("Show conversation text") | |
| if audio_file: | |
| with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmpfile: | |
| tmpfile.write(audio_file.getvalue()) | |
| tmpfile_path = tmpfile.name | |
| st.audio(tmpfile_path) | |
| user_text = speech_to_text(tmpfile_path) | |
| if show_text: | |
| st.write(f"**You:** {user_text}") | |
| ai_reply = chatbot_response(user_text, avatar) | |
| if show_text: | |
| st.write(f"**{avatar}:** {ai_reply}") | |
| speech_audio = text_to_speech(ai_reply) | |
| audio_bytes = io.BytesIO(speech_audio) | |
| st.audio(audio_bytes, format="audio/wav") | |