Create app.py

initial commit

app.py

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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")