update README

README.md

@@ -1,13 +1,30 @@
 ---
-title: Clip Eval
-emoji: 👀
-colorFrom: pink
-colorTo: blue
+title: CLIP Model Evaluation
+emoji: 📊🚀
+colorFrom: blue
+colorTo: green
 sdk: gradio
-sdk_version: 5.31.0
+sdk_version: 3.45.0 
 app_file: app.py
 pinned: false
-license: apache-2.0
+license: apache-2.0 
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 📊 CLIP Model Evaluation Space
+
+This Space provides an interactive interface to evaluate the performance of various CLIP (Contrastive Language-Image Pre-Training) models on standard image-text retrieval benchmarks.
+
+It calculates Recall@K (R@1, R@5, R@10) metrics for both:
+*   **Image Retrieval (Text-to-Image):** Given a text query, how well does the model retrieve the correct image?
+*   **Text Retrieval (Image-to-Text):** Given an image query, how well does the model retrieve the correct text description?
+
+## 🚀 How to Use
+
+1.  **Select a CLIP Model:** Choose a pre-trained CLIP model from the dropdown menu.
+2.  **Select a Dataset:** Choose the dataset you want to evaluate on (e.g., "mscoco", "flickr").
+3.  **Number of Samples:** Specify the number of image-text pairs from the dataset to use for the evaluation. Using fewer samples will be faster but less representative.
+4.  **Click "Evaluate Model":** The evaluation will run, and the Recall@K metrics will be displayed.
+
+## 🛠️ Under the Hood
+
+This Space uses the `evaluate` library from Hugging Face and a custom metric script (`clipmodel_eval.py`) to perform the CLIP model evaluations. The models and datasets are loaded from the Hugging Face Hub.

app.py

@@ -1,7 +1,7 @@
 import gradio as gr
 import evaluate
 
-clip_metric = evaluate.load("d-matrix/dmx_clip_eval")
+clip_metric = evaluate.load("d-matrix/clip_eval")
 print("Successfully loaded CLIP evaluation metric")
 
 AVAILABLE_MODELS = [