Update app.py

app.py

@@ -5,30 +5,21 @@ import numpy as np
 import soundfile as sf
 from transformers import (
     AutoProcessor, AutoModelForSpeechSeq2Seq,
-    AutoModelForCausalLM, AutoTokenizer,
-    pipeline, SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
+    AutoModelForCausalLM, AutoTokenizer
 )
-from datasets import load_dataset
 import logging
 from typing import Optional, Dict, Any
 import time
 from pathlib import Path
 
 from kokoro import KPipeline
-from IPython.display import display, Audio
-
-
 import gradio as gr
-import asyncio
-import os
 
 
 # Set up logging
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
-
-
 system_prompt_0 = """You are a highly trained U.S. Tax Assistant AI, designed to help individuals and small businesses understand, plan, and file their taxes according to federal and state tax laws. You explain complex tax concepts in simple, accurate, and actionable terms, using IRS guidelines, up-to-date tax code knowledge, and best practices for compliance and savings. You act as an explainer, educator, and assistant—not a certified tax preparer or legal advisor."""
 
 
@@ -39,31 +30,19 @@ class AsyncAIConversation:
         self.llm_tokenizer = None
         self.llm_model = None
         self.tts_synthesizer = None
-        self.speaker_embedding = None
         self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
         logger.info(f"Using device: {self.device}")
 
     async def initialize_models(self):
-        """Initialize all models asynchronously"""
         logger.info("Initializing models...")
-
-        # Initialize STT model
         await self._init_stt_model()
-
-        # Initialize LLM model
         await self._init_llm_model()
-
-        # Initialize TTS model
         await self._init_tts_model()
-
         logger.info("All models initialized successfully!")
 
     async def _init_stt_model(self):
-        """Initialize Speech-to-Text model"""
-        logger.info("Loading STT model...")
         try:
             stt_model_id = "unsloth/whisper-small"
-            #unsloth/whisper-large-v3-turbo
             self.stt_processor = AutoProcessor.from_pretrained(stt_model_id)
             self.stt_model = AutoModelForSpeechSeq2Seq.from_pretrained(stt_model_id)
             self.stt_model.to(self.device)
@@ -73,11 +52,8 @@ class AsyncAIConversation:
             raise
 
     async def _init_llm_model(self):
-        """Initialize Large Language Model"""
-        logger.info("Loading LLM model...")
         try:
             model_name = "unsloth/Qwen3-0.6B"
-            #unsloth/Qwen3-0.6B-unsloth-bnb-4bit
             self.llm_tokenizer = AutoTokenizer.from_pretrained(model_name)
             self.llm_model = AutoModelForCausalLM.from_pretrained(
                 model_name,
@@ -90,10 +66,7 @@ class AsyncAIConversation:
             raise
 
     async def _init_tts_model(self):
-        """Initialize Text-to-Speech model"""
-        logger.info("Loading TTS model...")
         try:
-            # Initialize Kokoro TTS pipeline
             self.tts_synthesizer = KPipeline(lang_code='a')
             logger.info("TTS model loaded successfully")
         except Exception as e:
@@ -101,56 +74,34 @@ class AsyncAIConversation:
             raise
 
     async def speech_to_text(self, audio_file_path: str) -> str:
-        """Convert speech to text asynchronously"""
-        logger.info(f"Processing audio file: {audio_file_path}")
-
         try:
-            # Load audio in a separate thread to avoid blocking
             def load_audio():
                 return librosa.load(audio_file_path, sr=16000)
 
             loop = asyncio.get_event_loop()
             speech_array, sampling_rate = await loop.run_in_executor(None, load_audio)
 
-            # Convert to tensor
-            speech_array_pt = torch.from_numpy(speech_array).unsqueeze(0).to(self.device)
-
-            # Process input features
             input_features = self.stt_processor(
                 speech_array,
                 sampling_rate=sampling_rate,
                 return_tensors="pt"
             ).input_features.to(self.device)
 
-            # Generate predictions
             with torch.no_grad():
                 predicted_ids = self.stt_model.generate(input_features)
 
-            # Decode predictions
             transcription = self.stt_processor.batch_decode(predicted_ids, skip_special_tokens=True)
-
-            result = transcription[0] if transcription else ""
-            logger.info(f"STT result: {result}")
-            return result
-
+            return transcription[0] if transcription else ""
         except Exception as e:
             logger.error(f"Error in speech_to_text: {e}")
             return ""
 
     async def process_with_llm(self, text: str, system_prompt: Optional[str] = None) -> Dict[str, str]:
-        """Process text with LLM and return both thinking and content"""
-        logger.info(f"Processing text with LLM: {text[:50]}...")
-
         try:
-            # Prepare messages
-            messages = [
-                {"role": "user", "content": text}
-            ]
-
+            messages = [{"role": "user", "content": text}]
             if system_prompt:
                 messages.insert(0, {"role": "system", "content": system_prompt})
 
-            # Apply chat template
             formatted_text = self.llm_tokenizer.apply_chat_template(
                 messages,
                 tokenize=False,
@@ -158,10 +109,8 @@ class AsyncAIConversation:
                 enable_thinking=False
             )
 
-            # Tokenize
             model_inputs = self.llm_tokenizer([formatted_text], return_tensors="pt").to(self.llm_model.device)
 
-            # Generate response
             with torch.no_grad():
                 generated_ids = self.llm_model.generate(
                     **model_inputs,
@@ -171,12 +120,9 @@ class AsyncAIConversation:
                     pad_token_id=self.llm_tokenizer.eos_token_id
                 )
 
-            # Extract new tokens
             output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
 
-            # Parse thinking content
             try:
-                # Find the end of thinking token (</think>)
                 index = len(output_ids) - output_ids[::-1].index(151668)
             except ValueError:
                 index = 0
@@ -184,31 +130,21 @@ class AsyncAIConversation:
             thinking_content = self.llm_tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
             content = self.llm_tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
 
-            result = {
+            return {
                 "thinking": thinking_content,
                 "response": content
             }
 
-            logger.info(f"LLM response generated: {content[:50]}...")
-            return result
-
         except Exception as e:
             logger.error(f"Error in process_with_llm: {e}")
             return {"thinking": "", "response": "Sorry, I encountered an error processing your request."}
 
     async def text_to_speech(self, text: str, output_path: str = "response.wav") -> str:
-        """Convert text to speech asynchronously"""
-        logger.info(f"Converting text to speech: {text[:50]}...")
-
         try:
-            # Generate speech in a separate thread to avoid blocking
             def generate_speech():
-                # Generate audio using Kokoro TTS
                 generator = self.tts_synthesizer(text, voice='af_heart')
-                
-                # Get the first generated audio chunk
                 for i, (gs, ps, audio) in enumerate(generator):
-                    if i == 0:  # Use the first chunk
+                    if i == 0:
                         return audio
                 return None
 
@@ -218,106 +154,47 @@ class AsyncAIConversation:
             if audio_data is None:
                 raise ValueError("Failed to generate audio")
 
-            # Save audio file with Kokoro's default sample rate (24000 Hz)
             sf.write(output_path, audio_data, samplerate=24000)
-
-            logger.info(f"Audio saved to: {output_path}")
             return output_path
-
         except Exception as e:
             logger.error(f"Error in text_to_speech: {e}")
             return ""
 
     async def process_conversation(self, audio_file_path: str, system_prompt: Optional[str] = None) -> Dict[str, Any]:
-        """Complete conversation pipeline: STT -> LLM -> TTS"""
-        start_time = time.time()
-        logger.info("Starting conversation processing...")
-
         try:
-            # Step 1: Speech to Text
-            stt_start = time.time()
             transcribed_text = await self.speech_to_text(audio_file_path)
-            stt_time = time.time() - stt_start
-
             if not transcribed_text:
                 return {"error": "Failed to transcribe audio"}
 
-            # Step 2: Process with LLM
-            llm_start = time.time()
             llm_result = await self.process_with_llm(transcribed_text, system_prompt)
-            llm_time = time.time() - llm_start
-
-            # Step 3: Text to Speech
-            tts_start = time.time()
             audio_output_path = await self.text_to_speech(llm_result["response"])
-            tts_time = time.time() - tts_start
 
-            total_time = time.time() - start_time
-
-            result = {
+            return {
                 "input_audio": audio_file_path,
                 "transcribed_text": transcribed_text,
                 "thinking": llm_result["thinking"],
                 "response_text": llm_result["response"],
                 "output_audio": audio_output_path,
-                "processing_times": {
-                    "stt": stt_time,
-                    "llm": llm_time,
-                    "tts": tts_time,
-                    "total": total_time
-                }
             }
-
-            logger.info(f"Conversation processed successfully in {total_time:.2f} seconds")
-            return result
-
         except Exception as e:
             logger.error(f"Error in process_conversation: {e}")
             return {"error": str(e)}
 
-    async def batch_process(self, audio_files: list, system_prompt: Optional[str] = None) -> list:
-        """Process multiple audio files concurrently"""
-        logger.info(f"Processing {len(audio_files)} audio files...")
-
-        # Create tasks for concurrent processing
-        tasks = [
-            self.process_conversation(audio_file, system_prompt)
-            for audio_file in audio_files
-        ]
-
-        # Process all files concurrently
-        results = await asyncio.gather(*tasks, return_exceptions=True)
-
-        logger.info("Batch processing completed")
-        return results
+# ---------------------------- GLOBAL CONVERSATION OBJECT ----------------------------
+ai_conversation = AsyncAIConversation()
 
-
-ai_conversation = None
-# Usage example and demo functions
+# ---------------------------- DEMO INITIALIZATION ----------------------------
 async def demo_conversation():
-    """Demonstration of the conversation system"""
-    # Initialize the conversation system
-    ai_conversation = AsyncAIConversation()
-
-    # Initialize all models
     await ai_conversation.initialize_models()
 
-
-
-# Create the async function wrapper for Gradio
+# ---------------------------- GRADIO WRAPPER ----------------------------
 async def process_audio_gradio(audio_file, system_prompt_input):
-
-    """Processes audio file and system prompt for Gradio interface."""
     if audio_file is None:
         return "Please upload an audio file.", "", "", None
 
-    # Gradio provides the file path
-    audio_path = audio_file
-
-    # Process the conversation using the initialized ai_conversation instance
     try:
         result = await ai_conversation.process_conversation(
-            audio_file_path=audio_path,
+            audio_file_path=audio_file,
             system_prompt=system_prompt_input
         )
 
@@ -328,12 +205,12 @@ async def process_audio_gradio(audio_file, system_prompt_input):
                 f"Transcribed: {result['transcribed_text']}\nThinking: {result['thinking']}",
                 result['response_text'],
                 result['output_audio'],
-                result['processing_times']
+                None
             )
     except Exception as e:
-        return f"An unexpected error occurred: {e}", "", "", None
+        return f"Unexpected error: {e}", "", "", None
 
-# Define the Gradio interface
+# ---------------------------- GRADIO INTERFACE ----------------------------
 with gr.Blocks() as demo:
     gr.Markdown("# Asynchronous AI Conversation System")
     gr.Markdown("Upload an audio file and provide a system prompt to get a response.")
@@ -350,21 +227,16 @@ with gr.Blocks() as demo:
         response_audio_output = gr.Audio(label="AI Response Audio", interactive=False)
         processing_times_output = gr.JSON(label="Processing Times")
 
-    # Link button click to the async function
     process_button.click(
         fn=process_audio_gradio,
         inputs=[audio_input, system_prompt_input],
         outputs=[status_output, response_text_output, response_audio_output, processing_times_output]
     )
 
-
+# ---------------------------- MAIN LAUNCH ----------------------------
 if __name__ == "__main__":
-
     def initiate():
         asyncio.run(demo_conversation())
 
     initiate()
-
-    # Gradio launch itself runs an event loop.
-    # Ensure ai_conversation is initialized in the notebook before this cell is run.
     demo.launch(debug=False, share=True)