---
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**.

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## 📊 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]

![image](https://cdn-uploads.huggingface.co/production/uploads/690f0f64c48c7a4a0ad3d46e/9LUy7xV4D0k4ZoPNRbFiw.png)

> 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]

![image](https://cdn-uploads.huggingface.co/production/uploads/690f0f64c48c7a4a0ad3d46e/YFr2h26u8dJ9haA6W1oGO.png)

>  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]

![image](https://cdn-uploads.huggingface.co/production/uploads/690f0f64c48c7a4a0ad3d46e/sVFgclZIaPq274Zm4ehL0.png)

>  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]

![image](https://cdn-uploads.huggingface.co/production/uploads/690f0f64c48c7a4a0ad3d46e/ERv_LZOBh7oD94_CXtCES.png)

> 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.

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## 🧠 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.

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## 📂 Dataset & Tech Stack
- **Data:** 10,000 synthetic records & 1,000 indexed images.
- **Framework:** Gradio
- **Libraries:** Sentence-Transformers, Scikit-learn, Pandas, Seaborn.