Update app.py

app.py

@@ -1,57 +1,57 @@
-import streamlit as st
-import requests
-
-API_URL_FASTSPEECH2 = "https://api-inference.huggingface.co/models/facebook/fastspeech2-en-ljspeech"
-API_URL_DISTIL_WHISPER = "https://api-inference.huggingface.co/models/distil-whisper/distil-large-v3"
-API_URL_WAV2VEC2 = "https://api-inference.huggingface.co/models/facebook/wav2vec2-large-960h"
-
-def query(api_url, payload):
-    response = requests.post(api_url, json=payload)
-    if response.status_code == 200:
-        return response.content
-    else:
-        st.error(f"Erreur {response.status_code}: {response.text}")
-        return None
-
-def query_audio(api_url, audio_file):
-    response = requests.post(api_url, files={"file": audio_file})
-    if response.status_code == 200:
-        return response.json()
-    else:
-        st.error(f"Erreur {response.status_code}: {response.text}")
-        return None
-
-def main():
-    
-    st.title("Text to speech App")
-    # Sidebar pour la navigation entre les modèles
-    st.sidebar.image("img/istockphoto-1391947389-612x612.jpg")
-    model_selection = st.sidebar.selectbox("Sélectionnez le modèle", ["Text-to-Speech (fastspeech2-en-ljspeech)", "Speech-to-Text (distil-whisper/distil-large-v3)"])
-
-    if model_selection == "Text-to-Speech (fastspeech2-en-ljspeech)":
-        st.subheader("Text-to-Speech (fastspeech2-en-ljspeech)")
-        st.image("img/image2.png") 
-        st.text("le modele prend environ 20s pou se charger veiller patienter quelques secondes")
-        user_input_tts = st.text_area("Entrez le texte que vous souhaitez convertir en audio:")
-
-        if st.button("Convertir le texte en audio avec fastspeech2-en-ljspeech"):
-            payload_tts = {"inputs": user_input_tts}
-            audio_bytes = query(API_URL_FASTSPEECH2, payload_tts)
-
-            if audio_bytes:
-                st.audio(audio_bytes, format="audio/wav", start_time=0)
-
-    elif model_selection == "Speech-to-Text (distil-whisper/distil-large-v3)":
-        st.subheader("Speech-to-Text (distil-whisper/distil-large-v3)")
-        st.image("img/istockphoto-1391947389-612x612.jpg") 
-        audio_file = st.file_uploader("Téléchargez votre fichier audio", type=["wav", "mp3", "flac"])
-
-        if st.button("Convertir l'audio en texte avec distil-whisper/distil-large-v3") and audio_file is not None:
-            result_stt = query_audio(API_URL_DISTIL_WHISPER, audio_file)
-
-            if result_stt:
-                st.subheader("Texte transcrit:")
-                st.write(result_stt.get("text", "Aucune transcription trouvée."))
-
-if __name__ == "__main__":
-    main()
+import streamlit as st
+import requests
+
+API_URL_FASTSPEECH2 = "https://api-inference.huggingface.co/models/facebook/fastspeech2-en-ljspeech"
+API_URL_DISTIL_WHISPER = "https://api-inference.huggingface.co/models/distil-whisper/distil-large-v3"
+API_URL_WAV2VEC2 = "https://api-inference.huggingface.co/models/facebook/wav2vec2-large-960h"
+
+def query(api_url, payload):
+    response = requests.post(api_url, json=payload)
+    if response.status_code == 200:
+        return response.content
+    else:
+        st.error(f"Erreur {response.status_code}: {response.text}")
+        return None
+
+def query_audio(api_url, audio_file):
+    response = requests.post(api_url, files={"file": audio_file})
+    if response.status_code == 200:
+        return response.json()
+    else:
+        st.error(f"Erreur {response.status_code}: {response.text}")
+        return None
+
+def main():
+    
+    st.title("Text to speech App")
+    # Sidebar pour la navigation entre les modèles
+    st.sidebar.image("istockphoto-1391947389-612x612.jpg")
+    model_selection = st.sidebar.selectbox("Sélectionnez le modèle", ["Text-to-Speech (fastspeech2-en-ljspeech)", "Speech-to-Text (distil-whisper/distil-large-v3)"])
+
+    if model_selection == "Text-to-Speech (fastspeech2-en-ljspeech)":
+        st.subheader("Text-to-Speech (fastspeech2-en-ljspeech)")
+        st.image("image2.png") 
+        st.text("le modele prend environ 20s pou se charger veiller patienter quelques secondes")
+        user_input_tts = st.text_area("Entrez le texte que vous souhaitez convertir en audio:")
+
+        if st.button("Convertir le texte en audio avec fastspeech2-en-ljspeech"):
+            payload_tts = {"inputs": user_input_tts}
+            audio_bytes = query(API_URL_FASTSPEECH2, payload_tts)
+
+            if audio_bytes:
+                st.audio(audio_bytes, format="audio/wav", start_time=0)
+
+    elif model_selection == "Speech-to-Text (distil-whisper/distil-large-v3)":
+        st.subheader("Speech-to-Text (distil-whisper/distil-large-v3)")
+        st.image("istockphoto-1391947389-612x612.jpg") 
+        audio_file = st.file_uploader("Téléchargez votre fichier audio", type=["wav", "mp3", "flac"])
+
+        if st.button("Convertir l'audio en texte avec distil-whisper/distil-large-v3") and audio_file is not None:
+            result_stt = query_audio(API_URL_DISTIL_WHISPER, audio_file)
+
+            if result_stt:
+                st.subheader("Texte transcrit:")
+                st.write(result_stt.get("text", "Aucune transcription trouvée."))
+
+if __name__ == "__main__":
+    main()