Simplify UI: Remove About tab, add links to main section

app.py

@@ -162,6 +162,16 @@ def create_interface():
         - 🔒 **Private Infrastructure**: Secure and controlled deployment
         """)
         
+        gr.Markdown("""
+        ### 🔗 Links
+        - **Organization**: [RinggAI on Hugging Face](https://huggingface.co/RinggAI)
+        - **TTS Space**: [Ringg TTS V0](https://huggingface.co/spaces/RinggAI/Ringg-TTS-v0.0)
+        
+        ---
+        
+        Made with ❤️ by RinggAI Team
+        """)
+        
         # API Status indicator
         with gr.Row():
             with gr.Column(scale=4):
@@ -205,70 +215,6 @@ def create_interface():
             - Speak naturally at a moderate pace
             - For file upload, ensure audio quality is good (16kHz or higher recommended)
             - Model handles code-switching between English and Hindi
-            
-            ### 🔧 Backend Configuration
-            This Space connects to a private API endpoint for transcription. 
-            The API endpoint can be configured via the `STT_API_ENDPOINT` secret in Space Settings.
-            """)
-        
-        with gr.Tab("ℹ️ About"):
-            gr.Markdown("""
-            ## About Ringg STT V0
-            
-            Ringg STT V0 is a state-of-the-art speech-to-text system for English and Hindi languages.
-            
-            ### 🎯 Model Details
-            - **Model**: Ringg STT V0
-            - **Languages**: English (EN) & Hindi (HI)
-            - **Sample Rate**: 16kHz
-            - **Performance**: 2nd place among top bilingual ASR models
-            - **Framework**: PyTorch-based deep learning
-            
-            ### 🏗️ Architecture
-            
-            This Space uses a **frontend-backend architecture**:
-            
-            ```
-            User → HF Space (Frontend) → API Endpoint → Private Server (Model) → Response
-            ```
-            
-            - **Frontend**: This Hugging Face Space (Gradio UI)
-            - **Backend**: Private inference server with the actual model
-            - **Connection**: Secure API calls via ngrok/tunnel
-            
-            ### 🚀 Key Features
-            - **Bilingual Recognition**: Native support for English and Hindi
-            - **Real-time Streaming**: Low-latency transcription
-            - **High Accuracy**: Optimized for conversational speech
-            - **Flexible Input**: Supports microphone streaming and file upload
-            - **Private Infrastructure**: Model runs on your own infrastructure
-            
-            ### 📊 Use Cases
-            - Voice assistants and chatbots
-            - Meeting transcription
-            - Content creation and subtitling
-            - Accessibility applications
-            - Voice search and commands
-            
-            ### 🔧 Technical Specifications
-            - **Audio Processing**: 16kHz mono, PCM16
-            - **Latency**: ~2-3 seconds for streaming
-            - **API Protocol**: REST API with base64-encoded audio
-            - **Supported Formats**: WAV, MP3, FLAC, M4A, OGG, OPUS
-            
-            ### 📝 Limitations
-            - Requires active backend API endpoint
-            - Works best with clear audio and minimal background noise
-            - Accuracy may vary with accents and dialects
-            - API latency depends on network and backend performance
-            
-            ### 🔗 Links
-            - **Organization**: [RinggAI on Hugging Face](https://huggingface.co/RinggAI)
-            - **TTS Space**: [Ringg TTS V0](https://huggingface.co/spaces/RinggAI/Ringg-TTS-v0.0)
-            
-            ---
-            
-            Made with ❤️ by RinggAI Team
             """)
     
     return demo