Update app.py

app.py

@@ -1,24 +1,34 @@
 import streamlit as st
 import requests
+import os
 
 API_URL = "https://api-inference.huggingface.co/models/openai/whisper-large-v3-turbo"
-headers = {"Authorization": f"Bearer {st.secrets['HF_API_KEY']}"} # Безопасное хранение токена
+headers = {"Authorization": "Bearer hf_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"}
 
 def query(file):
-    try:
-        response = requests.post(API_URL, headers=headers, data=file.read())
-        response.raise_for_status()  # Проверка на ошибки HTTP
-        return response.json()
-    except requests.exceptions.RequestException as e:
-        st.error(f"Ошибка запроса к API: {e}")
-        return None
+    data = file.read()
+    response = requests.post(API_URL, headers=headers, data=data)
+    return response.json()
 
-st.title("Транскрипция аудио")
+st.title("Speech Recognition with Whisper")
 
-uploaded_file = st.file_uploader("Загрузите аудиофайл", type=["wav", "mp3", "flac"])
+uploaded_file = st.file_uploader("Choose an audio file", type=['wav', 'mp3', 'flac'])
 
 if uploaded_file is not None:
-    with st.spinner("Транскрибируется..."):
-        output = query(uploaded_file)
-    if output:
-        st.text_area("Транскрипт:", value=output["text"])
+    st.audio(uploaded_file, format='audio/wav')
+    
+    if st.button('Transcribe'):
+        with st.spinner('Transcribing...'):
+            result = query(uploaded_file)
+            
+            if 'text' in result:
+                st.success("Transcription completed!")
+                st.write("Transcribed text:")
+                st.write(result['text'])
+            else:
+                st.error("An error occurred during transcription.")
+                st.write("Error details:")
+                st.write(result)
+
+st.markdown("---")
+st.write("Note: This app uses the Whisper API from Hugging Face.")