update readme

README.md

@@ -1,13 +1,76 @@
-FashionRec Dataset download is required
+# FashionM3 Project Setup Guide
 
-.env file is required, including
+This guide explains how to set up and run the FashionM3 project, which requires the FashionRec dataset, environment configuration, and specific server startup steps.
 
+## Prerequisites
+
+- Python 3.10 or higher
+- `pip` for installing dependencies
+- A working proxy server (if needed, e.g., at `http://127.0.0.1:10809`)
+
+## Step 1: Download the FashionRec Dataset
+
+1. Download the FashionRec dataset from https://huggingface.co/datasets/Anony100/FashionRec
+2. Extract the dataset to a directory of your choice (e.g., `/path/to/FashionRec`).
+3. Note the absolute path to the dataset directory, as it will be used in the `.env` file.
+
+## Step 2: Configure the Environment File
+
+Create a `.env` file in the project root directory with the following content:
+
+```plaintext
+# Chainlit server port
 CHAINLIT_PORT=8888
 
+# Proxy configuration (update or remove if not needed)
 PROXY=http://127.0.0.1:10809
 
-OPENAI_API_KEY=
-GEMINI_API_KEY=
+# API keys (replace with your own keys)
+OPENAI_API_KEY=your_openai_api_key_here
+GEMINI_API_KEY=your_gemini_api_key_here
+
+# Path to the FashionRec dataset (update to your dataset path)
+FASHION_DATA_ROOT=/path/to/FashionRec
+
+# Directory for generated images
+GEN_IMG_DIR=./generated_images
+```
+
+### Notes:
+- Replace `your_openai_api_key_here` and `your_gemini_api_key_here` with your actual OpenAI and Google Gemini API keys.
+- Set FASHION_DATA_ROOT to the absolute path of the FashionRec dataset (e.g., /home/user/data/FashionRec).
+- Update PROXY to match your proxy server, or remove it if no proxy is used.
+
+## Step 3: Install Dependencies
+
+Install the required Python packages:
+```bash
+pip install -r requirements.txt
+```
+
+## Step 4:  Run the Application
+Follow these steps to start the FashionM3 application:
+### 1. Start the Fashion VLM MCP Server:
+Run the MCP server for the fashion vision-language model:
+```bash
+python mcp_servers/fashion_vlm/main.py
+```
+
+Ensure the server starts successfully and remains running.
+
+### 2. Start the FashionM3 Client:
+Launch the Chainlit client to interact with the Fashion Assistant:
+```bash
+chainlit run chainlit_app.py --port 8888
+```
+
+### 3. Interact with the Fashion Assistant:
+Open your browser and navigate to:
+```plaintext
+http://localhost:8888/
+```
+
+This will load the FashionM3 interface, allowing you to interact with the Fashion Assistant.
+
+
 
-FASHION_DATA_ROOT=path_to_FashionRec
-GEN_IMG_DIR="./generated_images"

mcp_servers/fashion_vlm/main.py

@@ -28,6 +28,8 @@ load_dotenv()
 FASHION_DATA_ROOT = os.getenv("FASHION_DATA_ROOT", "/mnt/d/PostDoc/fifth paper/code/FashionVLM/datasets/FashionRec")
 OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
 OPENAI_API_BASE = os.getenv("OPENAI_API_BASE")
+GEN_IMG_DIR = os.getenv("GEN_IMG_DIR")
+os.makedirs(GEN_IMG_DIR, exist_ok=True)
 openai = AsyncOpenAI(api_key=OPENAI_API_KEY, base_url=OPENAI_API_BASE)
 VALID_CATEGORIES = [
     'Pants', 'Coats', 'Cross-body bags', 'Shirts', 'Hats & caps', 'Sneakers', 'Jeans', 'Boots', 'Dresses', 'Sandals',
@@ -64,7 +66,7 @@ fashion_vlm = FashionVLM(
     temperature=0.8,
     top_k=1,
     fashion_vlm_name='Anony100/FashionVLM',
-    save_dir="/mnt/d/PostDoc/fifth paper/code/FashionM3/generated_images"
+    save_dir=GEN_IMG_DIR
 )
 
 resolution = 512