going to use chatgpt code and see if it works

flow.txt

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+Create a gradio which acts a basic voice assient capable of executing task 
+make the applicatio in python and deplyoble on hugging face spaces
+use the transformers api from hugging face where every possible 
+
+Start by using fasterwhisper to convert speach into text; do this in chunks
+detect if the user stops talking and use senteinet anaylsis to figure if the user stopped to think or asked a question 
+if the user aksed a question feed it to chat gpt-oss-120b 
+give chat gpt acees to a bunch of get info; get booking dates; create; booking; take order 
+get a resoponce from chat gpt and play and conver it audio using Higgs Audio V2 [https://huggingface.co/bosonai/higgs-audio-v2-generation-3B-base]
+feed this audio responce back to gradio app; make sure all the models are avible and ready at the same time for real time conversation
+
+
+Creating a Voice Assistant with Gradio for Hugging Face Spaces
+
+This explanation details how to build a sophisticated voice assistant using Gradio that can be deployed on Hugging Face Spaces. The assistant will provide real-time interaction capabilities with speech-to-text conversion, intelligent conversation analysis, and natural-sounding responses.
+
+## System Architecture
+
+The voice assistant consists of several key components working together:
+
+1. **Speech-to-Text (STT)** - Using Faster Whisper for efficient and accurate transcription
+2. **Conversation Analysis** - Sentiment analysis to detect user intent and conversation flow
+3. **Natural Language Processing** - GPT model integration for intelligent responses
+4. **Text-to-Speech (TTS)** - Higgs Audio V2 for high-quality voice synthesis
+5. **User Interface** - Gradio for a clean, accessible web interface
+
+## Detailed Implementation Steps
+
+### 1. Speech-to-Text Processing
+
+The system will use Faster Whisper, an optimized version of OpenAI's Whisper model, to convert user speech to text:
+
+- Implement chunk-based audio processing to handle continuous speech
+- Configure the model to process audio in near real-time (16kHz sampling rate)
+- Optimize for latency by using a smaller model variant for initial deployment
+- Implement adaptive silence detection to identify when a user has finished speaking
+- Batch process audio frames to balance accuracy and responsiveness
+
+
+### 2. Conversation Flow Analysis
+
+The system will use sentiment analysis and conversational cues to determine user intent:
+
+- Implement a pause detection algorithm that analyzes audio for natural breaks
+- Use transformer-based sentiment analysis to distinguish between:
+  - Questions requiring information
+  - Thinking pauses (user contemplating)
+  - Statements or commands
+- Track conversation context to improve understanding of follow-up queries
+- Calculate confidence scores for detected intents to handle ambiguous cases
+
+```
+
+### 3. GPT Model Integration
+
+The system will use a powerful language model to generate contextually relevant responses:
+
+- Integrate with Hugging Face's `gpt-oss-120b` or equivalent open-source large language model
+- Provide the model with specialized system prompts for different tasks:
+  - Information retrieval (get_info)
+  - Booking management (get_booking_dates, create_booking)
+  - Order processing (take_order)
+- Implement context management to maintain conversation history
+- Use function calling capabilities to execute specific tasks based on user requests
+- Apply rate limiting and token optimization for efficient resource usage
+
+
+### 4. Text-to-Speech Synthesis
+
+The system will convert text responses to natural-sounding speech:
+
+- Integrate Higgs Audio V2 model from Hugging Face for high-quality voice synthesis
+- Configure voice parameters (pitch, speed, style) for natural conversation
+- Implement streaming audio playback to minimize perceived latency
+- Cache common responses to improve performance
+- Add prosody and emphasis based on sentiment and content type
+
+
+
+### 5. Gradio Interface Implementation
+
+The system will use Gradio to create an intuitive, accessible user interface:
+
+- Design a clean interface with audio input and output components
+- Implement WebRTC for low-latency audio streaming
+- Add visual feedback indicators for system status (listening, processing, speaking)
+- Include text display of transcriptions and responses for accessibility
+- Provide controls for adjusting voice parameters and conversation settings
+- Ensure responsive design for both desktop and mobile use
+
+## Hugging Face Spaces Deployment
+
+To deploy the application on Hugging Face Spaces:
+
+1. Create a `requirements.txt` file with all necessary dependencies:
+   - gradio
+   - transformers
+   - torch
+   - faster-whisper
+   - numpy
+   - scipy
+   - ffmpeg-python
+
+3. Implement model caching:
+   - Use Hugging Face's model caching mechanisms to improve loading times
+   - Implement progressive loading to make the interface available quickly
+
+4. Create a `README.md` with clear usage instructions and capabilities
+
+5. Add a Spaces SDK configuration file to specify resource requirements