Update app.py

app.py

@@ -3,53 +3,50 @@ from transformers import pipeline
 import edge_tts
 import numpy as np
 import asyncio
+import os
 
-# Print Gradio version for debugging
-print(f"Gradio version: {gr.__version__}")
-
-# Load speech-to-text model (Whisper small for Farsi)
+# Load STT and chatbot pipelines
 stt = pipeline("automatic-speech-recognition", model="openai/whisper-small")
-
-# Load chatbot model (GPT2 fine-tuned for Farsi)
 chatbot = pipeline("text-generation", model="HooshvareLab/gpt2-fa")
 
-# Function to convert text to speech using edge-tts
-async def tts(text, voice="fa-IR-FaridNeural"):
+async def tts(text: str, voice: str = "fa-IR-FaridNeural"):
     communicate = edge_tts.Communicate(text, voice)
     audio_data = b""
     async for chunk in communicate.stream():
         if chunk["type"] == "audio":
             audio_data += chunk["data"]
     audio_array = np.frombuffer(audio_data, dtype=np.int16)
-    sample_rate = 24000  # As per edge-tts documentation
+    sample_rate = 24000
     return sample_rate, audio_array
 
-# Main function: Audio-to-audio pipeline
 async def audio_to_audio(audio_input):
     if audio_input is None:
         return None, "No audio input received."
     sample_rate_in, data_in = audio_input
     audio = {"array": data_in, "sampling_rate": sample_rate_in}
-    
-    # Step 1: Convert speech to text
+    # 1. ASR → text
     text = stt(audio)["text"]
-    
-    # Step 2: Generate chatbot response
+    # 2. Generate response
     response = chatbot(text, max_length=50, num_return_sequences=1)[0]["generated_text"]
-    
-    # Step 3: Convert text to speech
-    sample_rate_out, data_out = await tts(response)
-    
-    return (sample_rate_out, data_out)
+    # 3. TTS
+    return await tts(response)
 
 # Gradio interface
 demo = gr.Interface(
     fn=audio_to_audio,
-    inputs=gr.Audio(source="microphone", type="numpy", label="Speak in Farsi"),
+    inputs=gr.Audio(
+        sources=["microphone"],      # Use 'sources' instead of deprecated 'source' :contentReference[oaicite:2]{index=2}
+        type="numpy",
+        label="Speak in Farsi"
+    ),
     outputs=gr.Audio(type="numpy", label="Response in Farsi"),
     title="Farsi Audio Chatbot",
-    description="Speak in Farsi, and the app will respond in Farsi audio."
+    description="Speak in Farsi, and the app will respond in Farsi audio.",
+    allow_flagging="never"
 )
 
-# Launch the app
-demo.launch()
+if __name__ == "__main__":
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=int(os.environ.get("PORT", 7860))
+    )