Update README.md

README.md

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----
-title: Street Descriptions Using MuniVis Model
-emoji: 📚
-colorFrom: purple
-colorTo: blue
-sdk: gradio
-sdk_version: 5.25.2
-app_file: app.py
-pinned: false
-license: mit
-short_description: This project using the MuniVis model to describe streets.
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 🖼️ MuniVis - Multilingual Image Captioning
+
+**MuniVis** is a powerful image captioning application built using the BLIP (Bootstrapped Language-Image Pretraining) model fine-tuned on custom data. It provides image descriptions in both **English** and **Arabic**, making it suitable for multilingual applications, accessibility tools, and urban scene understanding.
+
+## 🔍 What It Does
+
+- Generates English image captions using a fine-tuned BLIP model.
+- Automatically translates captions into Arabic using MarianMT (English → Arabic).
+- Provides a simple web interface powered by [Gradio](https://gradio.app/).
+
+## 🚀 Demo
+
+Try it on [Hugging Face Spaces](https://huggingface.co/spaces/saja003/MuniVis)!
+
+## 📷 How to Use
+
+1. Upload an image.
+2. Choose your preferred language (English or Arabic).
+3. The model will analyze the image and generate a meaningful description.
+
+## 🧠 Model Information
+
+- **Image Captioning Backbone**: `BLIP (Salesforce/blip-image-captioning-base)`
+- **Translation Model**: `Helsinki-NLP/opus-mt-en-ar`
+- **Custom Fine-Tuned Model**: [`saja003/MuniVis`](https://huggingface.co/saja003/MuniVis)
+
+## 📦 Requirements
+
+This app runs on the following Python packages:
+
+```txt
+torch
+transformers
+gradio
+Pillow