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| title: Bite Wise Final | |
| emoji: 📚 | |
| colorFrom: purple | |
| colorTo: green | |
| sdk: gradio | |
| sdk_version: 6.3.0 | |
| app_file: app.py | |
| pinned: false | |
| license: apache-2.0 | |
| # 🥗 BiteWise: Discover Your Culinary Twin | |
| **BiteWise** is a boutique AI recommendation engine. It bridges the gap between personal lifestyle and culinary cravings, utilizing a massive dataset of **10,000 curated text records** and **1,000 high-quality images**. | |
| --- | |
| ## 📊 Exploratory Data Analysis (EDA) | |
| Our EDA process ensured data integrity and revealed the behavioral patterns of our community. | |
| ### 1. Quality Control: Rating Distribution | |
| > ![Ratings Distribution] | |
|  | |
| > We performed outlier removal to ensure recommendations are based on authentic, high-quality reviews. The distribution shows a reliable baseline for our ranking algorithm. | |
| ### 2. Community Demographics: Age Range | |
| > ![Age Distribution] | |
|  | |
| > The "Culinary Twin" logic accounts for age diversity. By analyzing this distribution, we ensure that vibes and restaurant contexts match the user's life stage. | |
| ### 3. Personal Aesthetics: Fashion Styles | |
| > ![Fashion Styles] | |
|  | |
| > This visualization highlights the diversity of our users' styles. Identifying these clusters is key to our 35% persona-based weighting. | |
| ### 4. The Core Logic: Style vs. Food Preference | |
| > ![Correlation Heatmap] | |
|  | |
| > This Behavioral Heatmap is the "Brain" of BiteWise. It proves the strong correlation between lifestyle (Fashion) and taste (Food Vibe), enabling high-precision hybrid matching. | |
| --- | |
| ## 🧠 The AI Engine: Semantic Hybrid Search | |
| BiteWise doesn't just look for keywords; it understands culinary context through vector embeddings. | |
| ### The Pipeline: | |
| 1. **Model:** We utilize the **`all-MiniLM-L6-v2`** Sentence Transformer from Hugging Face. | |
| 2. **Embeddings:** Text descriptions and user personas are converted into **384-dimensional vectors**. | |
| 3. **Hybrid Scoring:** - **65% Weight:** Semantic match between user craving and dish description. | |
| - **35% Weight:** Persona similarity (Age, City, Style) between the user and the "Culinary Twin." | |
| 4. **Distance Calculation:** We use **Cosine Similarity** to find the top 3 closest matches in the vector space. | |
| --- | |
| ## 📂 Dataset & Tech Stack | |
| - **Data:** 10,000 synthetic records & 1,000 indexed images. | |
| - **Framework:** Gradio | |
| - **Libraries:** Sentence-Transformers, Scikit-learn, Pandas, Seaborn. |