Update README.md

README.md

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-title: Experimental Aether Framework
-emoji: 👀
-colorFrom: gray
-colorTo: purple
-sdk: gradio
-sdk_version: 5.45.0
-app_file: app.py
-pinned: false
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-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+🌟 AetherScore — AI Agent Evaluation Framework
+
+AetherScore is a modular evaluation framework for benchmarking AI agents. It provides a structured way to measure, compare, and visualize performance across multiple criteria, ensuring transparency and explainability in agent evaluation.
+
+✨ Key Capabilities
+
+Single Evaluation: Assess individual responses against prompts with rich visual feedback.
+
+Batch Evaluation: Upload JSON/JSONL datasets for large-scale evaluation in one click.
+
+Agent Comparison: Directly compare multiple agents’ performance across all metrics.
+
+Explainability Dashboard: Deep dive into evaluation reasoning and decision breakdowns.
+
+Comprehensive Metrics:
+
+Instruction Following
+
+Hallucination Score
+
+Assumption Control
+
+Coherence
+
+Accuracy
+
+LLM Judge Score
+
+Overall Weighted Score
+
+Visual Insights: Spider charts, bar plots, heatmaps, performance trends, and leaderboards.
+
+📂 Project Structure
+.
+├── app.py                  # Main Gradio interface
+├── evaluator_module.py     # Evaluation pipeline and scoring logic
+├── visualizer.py           # Plotly-based visualization utilities
+├── report_gen.py           # Batch and comparison report generation
+├── evaluation_storage.py   # In-memory cache for evaluations
+├── requirements.txt        # Python dependencies
+└── README.md               # Documentation
+
+⚙️ Setup & Installation
+
+Clone the repository and install dependencies:
+
+git clone https://huggingface.co/spaces/Supastrikas-004/Experimental-Aether-Framework
+cd Experimental-Aether-Framework
+pip install -r requirements.txt
+
+
+Dependencies include:->Refer Requirements.txt
+
+▶️ Usage
+Launch locally
+python app.py
+
+
+The Gradio app will open in your browser.
+
+Modes Available
+
+🎯 Single Evaluation — Input prompt + agent response → scores + charts.
+
+📦 Batch Evaluation — Upload JSON/JSONL with prompts/responses → aggregate results, heatmaps, and leaderboards.
+
+⚔️ Agent Comparison — Integrating LLM to it in current phase.
+
+🔍 Explainability — Future Work