Update app.py

app.py

@@ -23,7 +23,6 @@ def query(api_url, payload=None, data=None):
         return None
     return response_json
 
-
 # Define the function to translate speech
 def translate_speech(audio_file):
     print(f"Type of audio: {type(audio_file)}, Value of audio: {audio_file}")  # Debug line
@@ -33,19 +32,21 @@ def translate_speech(audio_file):
     output = query(ASR_API_URL, data=data)
     print(f"Output: {output}")  # Debug line
 
-    # Check if 'error' key exists in the output
-    if 'error' in output:
-        print(f"Error: {output['error']}")
-        estimated_time = output.get('estimated_time')
-        if estimated_time:
-            print(f"Estimated time for the model to load: {estimated_time} seconds")
-        return
-
-    # Check if 'text' key exists in the output
-    if 'text' in output:
-        transcription = output["text"]
+    # Check if output is not None
+    if output is not None:
+        # Check if 'error' key exists in the output
+        if 'error' in output:
+            print(f"Error: {output['error']}")
+            return
+
+        # Check if 'text' key exists in the output
+        if 'text' in output:
+            transcription = output["text"]
+        else:
+            print("Key 'text' does not exist in the output.")
+            return
     else:
-        print("Key 'text' does not exist in the output.")
+        print("Output is None.")
         return
 
     # Use the translation pipeline to translate the transcription
@@ -58,31 +59,6 @@ def translate_speech(audio_file):
     # Display the audio output
     return Audio(audio_bytes)
 
-    # print(f"Type of audio: {type(audio_file)}, Value of audio: {audio_file}")  # Debug line
-
-    # # Use the ASR pipeline to transcribe the audio
-    # data = audio_file.read()
-    # output = query(ASR_API_URL, data=data)
-    # print(f"Output: {output}")  # Debug line
-
-    # # Check if 'text' key exists in the output
-    # if 'text' in output:
-    #     transcription = output["text"]
-    # else:
-    #     print("Key 'text' does not exist in the output.")
-    #     return
-
-    # # Use the translation pipeline to translate the transcription
-    # translated_text = query(TRANSLATION_API_URL, {"inputs": transcription})
-
-    # # Use the TTS pipeline to synthesize the translated text
-    # response = requests.post(TTS_API_URL, headers=headers, json={"inputs": translated_text})
-    # audio_bytes = response.content
-
-    # # Display the audio output
-    # return Audio(audio_bytes)
-
-
 # Define the Gradio interface
 iface = gr.Interface(
     fn=translate_speech,