Add proper HF Space metadata configuration

README.md

@@ -1,55 +1,12 @@
-y---
-title: Clinical Report Generator
-emoji: 🏥
-colorFrom: purple
-colorTo: blue
+---
+title: T5
+emoji: 🤖
+colorFrom: "#003366"  # IPLC primary color
+colorTo: "#0073e6"    # IPLC secondary color
 sdk: gradio
-sdk_version: 5.13.2
+sdk_version: "5.13.2"  # Latest version
 app_file: app.py
 pinned: false
-license: mit
-short_description: Professional Clinical Report Generation using T5
 ---
 
-# Clinical Report Generator
-
-This Hugging Face Space provides a clinical report generation service using a fine-tuned T5 model. The model has been trained on a dataset of clinical documents to generate professional, structured clinical reports from input notes.
-
-## Features
-
-- Generates professional clinical reports from input notes
-- Maintains medical terminology and context
-- Removes unwanted elements like URLs
-- Provides a user-friendly interface
-
-## How to Use
-
-1. Enter your clinical notes in the input text box
-2. Click "Submit" to generate the report
-3. The generated report will appear in the output text box
-
-## Model Details
-
-- Base Model: T5-small
-- Fine-tuned on: Clinical documents and structured templates
-- Input Length: Up to 512 tokens
-- Output Length: Up to 256 tokens
-- Special Features:
-  - Beam search with 4 beams
-  - No repeat ngram size of 3
-  - Length penalty of 2.0
-  - Early stopping enabled
-  - URL filtering
-
-## Examples
-
-The interface includes example inputs to demonstrate the model's capabilities. These examples cover common clinical scenarios such as:
-- Initial patient presentations
-- Follow-up visits
-- Treatment responses
-
-## Notes
-
-- The model is designed for generating clinical reports only
-- Input should be clear and contain relevant clinical information
-- Generated reports should be reviewed by healthcare professionals
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference