updated readme

README.md

@@ -1,45 +1,11 @@
 ---
-title: Accessibility Bug Prediction with ALBERT
-emoji: 🐞
+title: Albert Latest 96
+emoji: 🐨
 colorFrom: blue
 colorTo: pink
 sdk: docker
-pinned: true
+pinned: false
 short_description: An AI-powered model to classify bug reports as accessibility-related or not, with Jira integration.
 ---
 
-# Accessibility Bug Prediction Using ALBERT 🐞
-
-This project leverages the **ALBERT (A Lite BERT)** model to classify software bug reports into two categories: 
-1. Accessibility-related bugs.
-2. Non-accessibility bugs.
-
-It also includes a **custom Jira plugin** to integrate the AI model into the bug-tracking workflow, making it easier for development teams to identify and prioritize accessibility issues.
-
-## Key Features ✨
-- **State-of-the-Art NLP**: Utilizes the ALBERT transformer model, fine-tuned for high accuracy on bug report classification tasks.
-- **Custom Dataset**: The model was trained from scratch on a dataset collected by the research team.
-- **Jira Plugin Integration**: Seamlessly integrates the classification system into Jira to enhance accessibility compliance workflows.
-- **Research Collaboration**: Developed under the guidance of **Professor Wajdi Aljedaani**, a UX and Human-Centered AI researcher.
-
-## How It Works 🚀
-1. **Input**: Provide a textual description of a bug report.
-2. **Prediction**: The ALBERT model analyzes the text and classifies the bug as either accessibility-related or not.
-3. **Output**: Use the results directly or integrate them into Jira for workflow optimization.
-
-## Applications 🛠️
-- **Software Development**: Identify accessibility bugs to ensure compliance with standards like WCAG.
-- **Quality Assurance**: Optimize testing and prioritization for accessibility-related issues.
-- **Research in UX and AI**: Leverage insights for designing inclusive and accessible systems.
-
-## Deployment 🌐
-The model is hosted on **Hugging Face Spaces**, providing an interactive and user-friendly web interface.
-
-[Try the Model on Hugging Face](https://huggingface.co/spaces/shivamjadhav/albert_latest_96)
-
-## About the Research 🤝
-This project was developed as part of a research initiative at **UNT** under **Professor Wajdi Aljedaani**'s guidance. It emphasizes the intersection of AI, UX, and accessibility to drive impactful solutions for software development.
-
----
-
-Check out the configuration reference at [Hugging Face Docs](https://huggingface.co/docs/hub/spaces-config-reference).
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -18,4 +18,4 @@ async def predict(issue: str):
     }
 @app.get("/")
 async def root():
-    return {"message": "Welcome to the API. Use /predict to get predictions."}
+    return {"message": "Welcome to the API. Use /predict to get predictions."}