Upload 4 files

Dockerfile

@@ -0,0 +1,17 @@
+FROM python:3.11.8-slim
+
+WORKDIR /app
+
+COPY requirements.txt .
+
+RUN pip install --upgrade pip && \
+    pip install --no-cache-dir -r requirements.txt
+
+COPY src /app/src
+COPY images /app/images
+COPY .env /app/.env
+
+
+EXPOSE 8080
+
+CMD ["python", "-m", "src.main"]

requirements.txt

@@ -0,0 +1,97 @@
+accelerate==0.28.0
+aiohttp==3.9.3
+aiosignal==1.2.0
+annotated-types==0.6.0
+anyio==4.2.0
+asttokens==2.0.5
+attrdict==2.0.1
+attrs==23.1.0
+Bottleneck==1.3.7
+Brotli==1.0.9
+certifi==2024.2.2
+charset-normalizer==2.0.4
+click==8.1.7
+comm==0.2.1
+datasets==2.12.0
+debugpy==1.6.7
+decorator==5.1.1
+dill==0.3.6
+distro==1.8.0
+einops==0.7.0
+executing==0.8.3
+faiss-cpu
+filelock==3.13.1
+Flask==2.2.2
+frozenlist==1.4.0
+fsspec==2023.10.0
+geographiclib==2.0
+geopy==2.4.1
+h11==0.14.0
+httpcore==1.0.2
+httpx==0.27.0
+huggingface-hub==0.20.3
+idna==3.4
+ipykernel==6.25.0
+ipython==8.20.0
+itsdangerous==2.0.1
+jedi==0.18.1
+Jinja2==3.1.3
+jupyter_client==8.6.0
+jupyter_core==5.5.0
+MarkupSafe==2.1.3
+matplotlib-inline==0.1.6
+mpmath==1.3.0
+multidict==6.0.4
+multiprocess==0.70.14
+nest-asyncio==1.6.0
+networkx==3.1
+numexpr==2.8.7
+numpy==1.26.4
+openai==1.16.2
+packaging==23.2
+pandas==2.2.1
+parso==0.8.3
+pexpect==4.8.0
+Pillow==10.0.1
+pip==23.3.1
+platformdirs==3.10.0
+prompt-toolkit==3.0.43
+ptyprocess==0.7.0
+pure-eval==0.2.2
+pyarrow==14.0.2
+Pygments==2.15.1
+pymongo==3.12.0
+PySocks==1.7.1
+python-dateutil==2.8.2
+python-dotenv==1.0.1
+pytz==2023.3.post1
+PyYAML==6.0.1
+pyzmq==25.1.2
+regex==2023.10.3
+replicate==0.25.1
+requests==2.31.0
+responses==0.13.3
+safetensors==0.4.2
+sentencepiece==0.2.0
+setuptools==68.2.2
+six==1.16.0
+sniffio==1.3.0
+stack-data==0.2.0
+sympy==1.12
+timm==0.9.16
+tokenizers==0.15.1
+torch==2.2.2
+torchaudio==2.2.2
+torchvision==0.17.2
+tornado==6.3.3
+tqdm==4.65.0
+traitlets==5.7.1
+transformers==4.39.3
+triton==2.2.0
+typing_extensions==4.9.0
+tzdata==2023.3
+urllib3==2.1.0
+wcwidth==0.2.5
+Werkzeug==2.3.8
+wheel==0.41.2
+yarl==1.9.3

src/main.py

@@ -0,0 +1,91 @@
+import os
+
+from dotenv import load_dotenv
+from flask import Flask, request, jsonify
+from openai import OpenAI
+from pymongo import MongoClient
+
+from src.utils import allowed_file, save_file, get_image_embeddings, \
+    get_clothing_type, save_data_to_db, fetch_weather, get_gender_by_username, \
+    prompt_gpt, get_outfit
+
+
+UPLOAD_FOLDER = 'images'
+
+app = Flask(__name__)
+app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
+
+load_dotenv()
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+os.environ["REPLICATE_API_TOKEN"] = os.getenv("REPLICATE_API_TOKEN")
+
+MONGO_URI = "mongodb+srv://moda:[email protected]/?retryWrites=true&w=majority&appName=ClusterModa"
+client = MongoClient(MONGO_URI)
+db = client.moda
+
+myclient = OpenAI(api_key=OPENAI_API_KEY)
+
+
+
+@app.route("/register", methods=['POST'])
+def register_user():
+    collection = db.users
+    username = request.json.get('username')
+    gender = request.json.get("gender")
+
+    existing_user = collection.find_one({'_id': username})
+    if existing_user:
+        return jsonify({'message': 'Username already exists'}), 400
+    
+    new_user = {'_id': username, 
+                'gender': gender,
+                "closet": {}}
+    result = collection.insert_one(new_user)
+    
+    return jsonify({'message': 'User registered successfully', 'user_id': str(result.inserted_id)}), 201
+
+
+@app.route('/upload', methods=['POST'])
+def upload_file():
+    if 'file' not in request.files:
+        return jsonify({'message': 'No file part'}), 400
+    file = request.files['file']
+    if file.filename == '':
+        return jsonify({'message': 'No selected file'}), 400
+    if not allowed_file(file.filename):
+        return jsonify({'message': 'File extension not allowed'}), 400
+    if 'username' not in request.form:
+        return jsonify({'message': 'No username provided'}), 400
+    
+    file_path = save_file(file, app)
+
+    image = open(file_path, "rb")
+    clothing_type = get_clothing_type(image)
+    image_embeddings = get_image_embeddings(image)
+    data = {
+        "username": request.form["username"],
+        "image_path": file_path,
+        "type": clothing_type,
+        "image_embeddings": image_embeddings
+    }
+    save_data_to_db(data, db)
+    
+    return jsonify({'message': 'File uploaded successfully'}), 200
+
+
+@app.route("/recommend", methods=["POST"])
+def recommend_outfit():
+    """Takes as input a username, a context, latitude, and longitude"""
+    username = request.json.get('username')
+    context = request.json.get('context')
+    temperature = fetch_weather(float(request.json.get("latitude")), float(request.json.get("longitude")))
+    gender = get_gender_by_username(username, db)
+
+    outfit_description = prompt_gpt(myclient, gender, context, temperature)
+    outfit = get_outfit(outfit_description, username, db)
+
+    return jsonify({'outfit': outfit, 'message': "Recommendation Successful"}), 200
+
+
+if __name__ == "__main__":
+    app.run(debug=True)

src/utils.py

@@ -0,0 +1,247 @@
+import requests
+import os
+
+import faiss
+import numpy as np
+import replicate
+from dotenv import load_dotenv
+from geopy.geocoders import Nominatim
+from werkzeug.utils import secure_filename
+
+
+CLOTHES_TYPES = ["tops", "bottoms", "shoes", "outerwear"]
+load_dotenv()
+WEATHER_API_KEY = os.getenv("WEATHER_API_KEY")
+
+
+def allowed_file(filename):
+    """Checks if uploaded file is allowed"""
+    ALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg'}
+    return '.' in filename and \
+           filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
+
+def save_file(file, app):
+    """Makes filename unique and saves it"""
+    filename = secure_filename(file.filename)
+    name, extension = os.path.splitext(filename)
+    counter = 1
+    while os.path.exists(os.path.join(app.config['UPLOAD_FOLDER'], filename)):
+        filename = f"{name}_{counter}{extension}"
+        counter += 1
+
+    file_path = os.path.join(app.config['UPLOAD_FOLDER'], filename)
+    file.save(file_path)
+
+    return file_path
+
+
+
+def get_image_embeddings(image):
+    output = replicate.run(
+        "daanelson/imagebind:0383f62e173dc821ec52663ed22a076d9c970549c209666ac3db181618b7a304",
+        input={
+            "input": image,
+            "modality": "vision"
+        }
+    )
+
+    return output
+
+def get_text_embeddings(text):
+    output = replicate.run(
+        "daanelson/imagebind:0383f62e173dc821ec52663ed22a076d9c970549c209666ac3db181618b7a304",
+        input={
+            "text_input": text,
+            "modality": "text"
+        }
+    )
+
+    return output
+
+def get_clothing_type(image):
+    output = replicate.run(
+        "yorickvp/llava-v1.6-34b:41ecfbfb261e6c1adf3ad896c9066ca98346996d7c4045c5bc944a79d430f174",
+        input={
+            "image": image,
+            "prompt": f"What is this piece of clothing? Please select ONLY ONE CHOICE: {CLOTHES_TYPES}. \
+                If you are in doubt, just pick ONE OF THEM.\
+                \nIf you think it's outerwear but it doesn't have a zipper or buttons, it should \
+                be considered as: 'tops'. Keep in mind that you shouldn't write anything except one \
+                of the 4 choices, and no other text."
+        }
+    )
+
+    return "".join(output).lower()
+
+def get_user_closet_length(query, collection):
+    user_doc = collection.find_one(query)
+    if user_doc:
+        closet = user_doc.get('closet', {})
+        return len(closet)
+    
+    raise ValueError("User not found")
+
+def save_data_to_db(data:dict, db):
+    collection = db.users
+    query = {'_id': data["username"]}
+    new_item = {
+        'path': data["image_path"],
+        'type': data["type"],
+        'embedding': data["image_embeddings"]
+    }
+
+    closet_length = get_user_closet_length(query, collection)
+    new_item_key = f"item{closet_length + 1}"
+    collection.update_one(
+        {"_id": data["username"]},
+        {"$set": {f"closet.{new_item_key}": new_item}}
+    )
+
+
+
+def get_city_from_coord(latitude, longitude):
+    geolocator = Nominatim(user_agent="city_name_app")
+    location = geolocator.reverse((latitude, longitude), exactly_one=True)
+    address = location.address if location else None
+    if address:
+        city = address.split(",")[-3]
+        return city.strip()
+
+    return None
+
+def fetch_weather(latitude, longitude):
+    city_name = get_city_from_coord(latitude, longitude)
+    if not city_name:
+        return None
+    
+    url = f"http://api.openweathermap.org/data/2.5/weather?q={city_name}&appid={WEATHER_API_KEY}&units=metric"
+    response = requests.get(url)
+    data = response.json()
+
+    return data["main"]["feels_like"]
+
+def get_gender_by_username(username:str, db):
+    collection = db.users
+    document = collection.find_one({"_id": username})
+    if not document:
+        return None
+    
+    return document.get("gender")
+
+
+def prompt_gpt(client, gender, context, temperature):
+    """given some context, returns a dictionary describing what you should wear in your outfit."""
+    outfit = {}
+
+    history = [{"role": "system", "content": "You are a fashion expert who is dedicated to picking outfits for a user. \
+                You will receive context from the user that will help you choose an outfit. \
+                An outfit contains four items: top, bottom, shoes, outwear. \
+                The outwear is optional and depends on the weather, if it is not needed, \
+                only write `none` and nothing else. You will need to provide a description for each item one by one.\
+                I want the description to include: color, style, fit, and material. Make sure the different items \
+                go well toghether in terms of style, color and other factors. the answer should be in this format: \
+                'description': "}]
+    
+    prompt = {"role": "user", "content": f"-Context: `{context}`,\n-Gender: `{gender}`\
+              \n-Temperature: `{temperature}` degrees celsius.\
+              \nGiven this context, generate a description for what I should wear as a top:"}
+    
+    history.append(prompt)
+    response = client.chat.completions.create(
+        model="gpt-3.5-turbo",
+        messages= history
+    )
+    content = response.choices[0].message.content
+    history.append({"role": "assistant", "content": content})
+    outfit["tops"] = content[15:]
+
+
+    prompt = {"role": "user", "content": f"Given the previous answers, generate a description for what \
+              I should wear as a bottom:"}
+    
+    history.append(prompt)
+    response = client.chat.completions.create(
+        model="gpt-3.5-turbo",
+        messages= history
+    )
+    content = response.choices[0].message.content
+    history.append({"role": "assistant", "content": content})
+    outfit["bottoms"] = content[15:]
+
+
+    prompt = {"role": "user", "content": f"Given the previous answers, generate a description for what \
+              I should wear as shoes:"}
+    
+    history.append(prompt)
+    response = client.chat.completions.create(
+        model="gpt-3.5-turbo",
+        messages= history
+    )
+    content = response.choices[0].message.content
+    history.append({"role": "assistant", "content": content})
+    outfit["shoes"] = content[15:]
+
+
+    prompt = {"role": "user", "content": f"Given the previous answers, and given the temperature, \
+              generate a description for what I should wear as outwear. If the temperature I gave you \
+              ({temperature}) is above 25, write none."}
+    
+    history.append(prompt)
+    response = client.chat.completions.create(
+        model="gpt-3.5-turbo",
+        messages= history
+    )
+    content = response.choices[0].message.content
+    history.append({"role": "assistant", "content": content})
+    if not "none" in content.lower():
+        outfit["outerwear"] = content[15:]
+        
+
+    return outfit
+
+
+def get_items_by_type(db, username, item_type:str):
+    collection = db.users
+    document = collection.find_one({"_id": username})
+    if not document:
+        return None
+    closet = document.get("closet", {})
+    items = [closet[key] for key in closet if closet[key].get("type") == item_type]
+
+    return items
+
+def get_items_embeddings(items):
+    embeddings = []
+    for item in items:
+        embedding = item.get("embedding")
+        embeddings.append(embedding)
+
+    return embeddings
+
+def get_most_similar_embedding(embedding, embeddings_list):
+    embedding = np.asarray(embedding, dtype=np.float32)
+    embeddings_list = np.asarray(embeddings_list, dtype=np.float32)
+
+    d = embedding.shape[0]
+    index = faiss.IndexFlatL2(d)
+    index.add(embeddings_list)
+    _, most_similar_index = index.search(np.expand_dims(embedding, axis=0), 1)
+
+    return most_similar_index[0][0]
+
+def get_outfit(outfit_description, username, db):
+    outfit = []
+
+    for item_type, description in outfit_description.items():
+        description_embedding = get_text_embeddings(description)
+
+        items = get_items_by_type(db, username, item_type)
+        items_embeddings= get_items_embeddings(items)
+        outfit_item_index = get_most_similar_embedding(description_embedding, items_embeddings)
+        outfit_item_path = items[outfit_item_index]["path"]
+        if item_type == "outerwear":
+            outfit.insert(0, outfit_item_path)
+        else:
+            outfit.append(outfit_item_path)
+
+    return outfit