Upload README.md

README.md

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----
-title: AcceptIN
-emoji: 🚀
-colorFrom: red
-colorTo: red
-sdk: docker
-app_port: 8501
-tags:
-- streamlit
-pinned: false
-short_description: AI-Powered Telecom Site Quality Inspector
-license: mit
----
-
-# Welcome to Streamlit!
-
-Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
-
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+---
+title: ACCEPTIN - Telecom Site Quality Classification
+emoji: 📡
+colorFrom: blue
+colorTo: purple
+sdk: streamlit
+sdk_version: 1.31.0
+app_file: app.py
+pinned: false
+license: mit
+---
+
+# 📡 ACCEPTIN - Telecom Site Quality Classification
+
+AI-powered telecom site inspection using ConvNeXt transfer learning.
+
+---
+
+## 🚀 Deploying ACCEPTIN on Hugging Face Spaces
+
+### 1. Prepare Your Project Directory
+
+Ensure your project has the following structure:
+```
+ACCEPTIN/
+├── app.py
+├── requirements.txt
+├── README.md
+├── models/
+│   └── telecom_classifier.pth
+├── utils/
+│   ├── data_utils.py
+│   └── model_utils.py
+└── ... (other files)
+```
+
+### 2. Create a New Space on Hugging Face
+1. Go to [Hugging Face Spaces](https://huggingface.co/spaces).
+2. Click **Create new Space**.
+3. Fill in:
+   - **Space name**: acceptin (or your choice)
+   - **SDK**: Streamlit
+   - **Hardware**: CPU basic (free)
+   - **Visibility**: Public or Private
+4. Click **Create Space**.
+
+### 3. Prepare Your Files
+- **requirements.txt**: List all dependencies (see below for example).
+- **README.md**: This file, with the YAML header above.
+- **Model File**: Place `telecom_classifier.pth` in a `models/` folder. If >10MB, use Git LFS.
+
+#### Example requirements.txt
+```
+streamlit==1.31.0
+torch==2.2.0
+torchvision==0.17.0
+Pillow==10.2.0
+numpy==1.26.0
+timm==0.9.12
+opencv-python-headless==4.8.1.78
+plotly==5.18.0
+pandas==2.2.0
+scikit-learn==1.4.0
+matplotlib==3.8.0
+seaborn==0.13.0
+tqdm==4.66.0
+```
+
+### 4. (If Needed) Set Up Git LFS for Large Files
+If your model file is large:
+```bash
+git lfs install
+git lfs track "*.pth"
+git add .gitattributes
+```
+
+### 5. Upload Your Files to the Space
+**A. Web Interface**
+- Go to your Space on Hugging Face.
+- Click the **Files** tab.
+- Upload all files and folders (`app.py`, `requirements.txt`, `README.md`, `models/`, `utils/`, etc.).
+
+**B. Git Method (Recommended)**
+```bash
+git clone https://huggingface.co/spaces/YOUR_USERNAME/acceptin
+cd acceptin
+# Copy your files into this directory
+# If using Git LFS:
+git lfs install
+git lfs track "*.pth"
+git add .gitattributes
+git add .
+git commit -m "Initial ACCEPTIN deployment"
+git push
+```
+
+### 6. Wait for Build & Test
+- Hugging Face will automatically build your Space.
+- Wait for the build to finish (watch the logs for errors).
+- Test your app in the browser.
+
+### 7. Troubleshooting
+- If you see errors, check the build logs.
+- Make sure all dependencies are in `requirements.txt`.
+- Ensure your model path in `app.py` matches the uploaded file location.
+
+### 8. Share Your Space
+- Once working, share your Space URL (e.g., `https://huggingface.co/spaces/YOUR_USERNAME/acceptin`).
+
+---
+
+## 🏗️ Technical Overview
+- **Model**: ConvNeXt Large (197M parameters, transfer learning from food detection)
+- **Task**: Binary classification (good/bad telecom site)
+- **App**: Streamlit web interface
+- **Data**: Images of telecom sites, labeled as good or bad
+- **Deployment**: Hugging Face Spaces (Streamlit SDK)
+
+## 📋 Features
+- Upload telecom site images for instant quality assessment
+- Visual confidence scores and inspection breakdown
+- Modern, responsive UI
+
+## 📊 Model Performance
+- **Validation Accuracy**: ~94%
+- **Model Size**: ~750MB
+
+## 📚 Data Requirements
+- Images of telecom sites (good/bad)
+- Recommended: 100+ images per class
+
+---
+
+**For more details, see the in-app documentation or contact the author.**