Update app.py

app.py

@@ -70,49 +70,71 @@ def text_to_speech(input_text: TextInput):
 @app.post("/chat/")
 async def chat_with_llm(file: UploadFile = File(...)):
     """Process input WAV, send text to LLM, and return generated response as WAV."""
-    with open("input_chat.wav", "wb") as audio_file:
-        audio_file.write(await file.read())
-    
-    # Load WAV file
-    fs, recorded_waveform = wav.read("input_chat.wav")
-    os.remove("input_chat.wav")
-    recorded_waveform = recorded_waveform.astype(np.float32) / 32767.0
-    waveform_bytes = recorded_waveform.tobytes()
-    user_message = ggwave.decode(instance, waveform_bytes)
-    print("user_message" + user_message.decode("utf-8") )
-    # Send to LLM
-    chat_completion = client.chat.completions.create(
-        messages=[
-            {
-            "role": "system",
-            "content": "you are a helpful assistant. answer alway in one sentence"
-        },
-            {"role": "user", "content": user_message.decode("utf-8")}],
-        model="llama-3.3-70b-versatile",
-    )
-    llm_response = chat_completion.choices[0].message.content
-    print(llm_response)
-    # Convert response to audio
-    """Convert text to a WAV audio file using ggwave and return as response."""
-    encoded_waveform = ggwave.encode(llm_response , protocolId=1, volume=100)
-    
-    # Convert byte data into float32 array
-    waveform_float32 = np.frombuffer(encoded_waveform, dtype=np.float32)
-    
-    # Normalize float32 data to the range of int16
-    waveform_int16 = np.int16(waveform_float32 * 32767)
     
-    # Save to buffer instead of a file
-    buffer = io.BytesIO()
-    with wave.open(buffer, "wb") as wf:
-        wf.setnchannels(1)                  # Mono audio
-        wf.setsampwidth(2)                  # 2 bytes per sample (16-bit PCM)
-        wf.setframerate(48000)              # Sample rate
-        wf.writeframes(waveform_int16.tobytes())  # Write waveform as bytes
-    
-    buffer.seek(0)
+    # Read the file content into memory without saving to disk
+    file_content = await file.read()
     
-    return Response(content=buffer.getvalue(), media_type="audio/wav", headers={
-        "X-User-Message": user_message.decode("utf-8"),
-        "X-LLM-Response": llm_response
-    })
+    # Create a BytesIO object to use with wav.read
+    with io.BytesIO(file_content) as buffer:
+        try:
+            fs, recorded_waveform = wav.read(buffer)
+            recorded_waveform = recorded_waveform.astype(np.float32) / 32767.0
+            waveform_bytes = recorded_waveform.tobytes()
+            user_message = ggwave.decode(instance, waveform_bytes)
+            
+            if user_message is None:
+                return Response(
+                    content="No message detected in audio",
+                    media_type="text/plain",
+                    status_code=400
+                )
+                
+            print("user_message: " + user_message.decode("utf-8"))
+            
+            # Send to LLM
+            chat_completion = client.chat.completions.create(
+                messages=[
+                    {"role": "system", "content": "you are a helpful assistant. answer always in one sentence"},
+                    {"role": "user", "content": user_message.decode("utf-8")}
+                ],
+                model="llama-3.3-70b-versatile",
+            )
+            
+            llm_response = chat_completion.choices[0].message.content
+            print(llm_response)
+            
+            # Convert response to audio
+            encoded_waveform = ggwave.encode(llm_response, protocolId=1, volume=100)
+            
+            # Convert byte data into float32 array
+            waveform_float32 = np.frombuffer(encoded_waveform, dtype=np.float32)
+            
+            # Normalize float32 data to the range of int16
+            waveform_int16 = np.int16(waveform_float32 * 32767)
+            
+            # Save to buffer instead of a file
+            buffer = io.BytesIO()
+            with wave.open(buffer, "wb") as wf:
+                wf.setnchannels(1)  # Mono audio
+                wf.setsampwidth(2)  # 2 bytes per sample (16-bit PCM)
+                wf.setframerate(48000)  # Sample rate
+                wf.writeframes(waveform_int16.tobytes())  # Write waveform as bytes
+            
+            buffer.seek(0)
+            
+            return Response(
+                content=buffer.getvalue(), 
+                media_type="audio/wav",
+                headers={
+                    "X-User-Message": user_message.decode("utf-8"),
+                    "X-LLM-Response": llm_response
+                }
+            )
+            
+        except Exception as e:
+            print(f"Error processing audio: {str(e)}")
+            return Response(
+                content=f"Error processing audio: {str(e)}",
+                media_type="text/plain",
+                status_code=500
+            )