Update README.md

README.md

@@ -14,7 +14,7 @@ license: mit
 
 # Accent Analyzer
 
-This is a Streamlit-based web application that analyzes the English accent in spoken videos. Users can provide a public video URL (MP4), receive a transcription of the speech, and ask follow-up questions based on the transcript using Gemma3.
+This is a Streamlit-based web application that analyzes the English accent in spoken videos. Users can provide a public video URL (MP4), receive a transcription of the speech, and ask follow-up questions based on the transcript using Gemma3:1b.
 
 ## What It Does
 
@@ -31,7 +31,7 @@ This is a Streamlit-based web application that analyzes the English accent in sp
 - **Streamlit** — UI  
 - **OpenAI Whisper (medium)**: For speech-to-text transcription.
 - **Jzuluaga/accent-id-commonaccent_xlsr-en-english**: For English accent classification.
-- **Gemma3 via Ollama**: For generating answers to follow-up questions using context from the transcript.
+- **Gemma3:1b via Ollama**: For generating answers to follow-up questions using context from the transcript.
 - **Docker** — containerized for deployment  
 - **Hugging Face Spaces** — for hosting with CPU  
 
@@ -42,6 +42,8 @@ This is a Streamlit-based web application that analyzes the English accent in sp
 ```
 accent-analyzer/
 ├── Dockerfile                  # Container setup
+├── start.sh                    # Serving Ollama and app setup
+├── README.md                   # Instruction about the app
 ├── requirements.txt            # Python dependencies
 ├── streamlit_app.py            # Main UI app
 └── src/
@@ -109,7 +111,7 @@ langgraph>=0.0.20
 
 ## Notes
 
-- Gemma3 is accessed via **Ollama** inside Docker — ensure it pulls on build.
+- Gemma3:1b is accessed via **Ollama** inside Docker — ensure it pulls on build.
 - `custome_interface.py` is required by the accent model — it’s automatically downloaded in Dockerfile.
 - Video URLs must be **direct links** to `.mp4` files.
 
@@ -138,7 +140,7 @@ This project uses the following models, frameworks, and tools:
 - [SpeechBrain](https://speechbrain.readthedocs.io/): Toolkit used for building and fine-tuning speech processing models.
 - [Accent-ID CommonAccent](https://huggingface.co/Jzuluaga/accent-id-commonaccent_xlsr-en-english): Fine-tuned wav2vec2 model hosted on Hugging Face for English accent classification.
 - [CustomEncoderWav2vec2Classifier](https://huggingface.co/Jzuluaga/accent-id-commonaccent_xlsr-en-english/blob/main/custom_interface.py): Custom interface used to load and run the accent model.
-- [Gemma3](https://ollama.com/library/gemma3) via [Ollama](https://ollama.com): Large language model used for natural language follow-up based on transcripts.
+- [Gemma3:1b](https://ollama.com/library/gemma3:1b) via [Ollama](https://ollama.com): Large language model used for natural language follow-up based on transcripts.
 - [Streamlit](https://streamlit.io): Python framework for building web applications.
 - [Hugging Face Spaces](https://huggingface.co/spaces): Platform used for deploying this application on GPU infrastructure.