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| import streamlit as st | |
| def render_report(): | |
| st.title("📊 Recipe Search System Report") | |
| st.markdown(""" | |
| ## Overview | |
| This report summarizes the working of the **custom BERT-based Recipe Recommendation System**, dataset characteristics, scoring algorithm, and evaluation metrics. | |
| """) | |
| st.markdown("### 🔍 Query Embedding and Similarity Calculation") | |
| st.latex(r""" | |
| \text{Similarity}(q, r_i) = \cos(\hat{q}, \hat{r}_i) = \frac{\hat{q} \cdot \hat{r}_i}{\|\hat{q}\|\|\hat{r}_i\|} | |
| """) | |
| st.markdown(""" | |
| Here, $\\hat{q}$ is the BERT embedding of the query, and $\\hat{r}_i$ is the embedding of the i-th recipe. | |
| """) | |
| st.markdown("### 🏆 Final Score Calculation") | |
| st.latex(r""" | |
| \text{Score}_i = 0.6 \times \text{Similarity}_i + 0.4 \times \text{Popularity}_i | |
| """) | |
| st.markdown("### 📊 Dataset Summary") | |
| st.markdown(""" | |
| - **Total Recipes:** 231,630 | |
| - **Average Tags per Recipe:** ~6 | |
| - **Ingredients per Recipe:** 3 to 20 | |
| - **Ratings Data:** Extracted from user interaction dataset | |
| """) | |
| st.markdown("### 🧪 Evaluation Strategy") | |
| st.markdown(""" | |
| We use a combination of: | |
| - Manual inspection | |
| - Recipe diversity analysis | |
| - Match vs rating correlation | |
| - Qualitative feedback from test queries | |
| """) | |
| st.markdown("---") | |
| st.markdown("© 2025 Your Name. All rights reserved.") | |
| # If using a layout wrapper: | |
| render_report() | |
| # LaTeX content as string | |
| latex_report = r""" | |
| \documentclass{article} | |
| \usepackage{amsmath} | |
| \usepackage{geometry} | |
| \geometry{margin=1in} | |
| \title{Recipe Recommendation System Report} | |
| \author{Saksham Lakhera} | |
| \date{\today} | |
| \begin{document} | |
| \maketitle | |
| \section*{Overview} | |
| This report summarizes the working of the \textbf{custom BERT-based Recipe Recommendation System}, dataset characteristics, scoring algorithm, and evaluation metrics. | |
| \section*{Query Embedding and Similarity Calculation} | |
| \[ | |
| \text{Similarity}(q, r_i) = \cos(\hat{q}, \hat{r}_i) = \frac{\hat{q} \cdot \hat{r}_i}{\|\hat{q}\|\|\hat{r}_i\|} | |
| \] | |
| Here, $\hat{q}$ is the BERT embedding of the query, and $\hat{r}_i$ is the embedding of the i-th recipe. | |
| \section*{Final Score Calculation} | |
| \[ | |
| \text{Score}_i = 0.6 \times \text{Similarity}_i + 0.4 \times \text{Popularity}_i | |
| \] | |
| \section*{Dataset Summary} | |
| \begin{itemize} | |
| \item \textbf{Total Recipes:} 231,630 | |
| \item \textbf{Average Tags per Recipe:} $\sim$6 | |
| \item \textbf{Ingredients per Recipe:} 3 to 20 | |
| \item \textbf{Ratings Source:} User interaction dataset | |
| \end{itemize} | |
| \section*{Evaluation Strategy} | |
| We use a combination of: | |
| \begin{itemize} | |
| \item Manual inspection | |
| \item Recipe diversity analysis | |
| \item Match vs rating correlation | |
| \item Qualitative user feedback | |
| \end{itemize} | |
| \end{document} | |
| """ | |
| # ⬇️ Download button to get the .tex file | |
| st.markdown("### 📥 Download LaTeX Report") | |
| st.download_button( | |
| label="Download LaTeX (.tex)", | |
| data=latex_report, | |
| file_name="recipe_report.tex", | |
| mime="text/plain" | |
| ) | |
| # 📤 Optional: Show the .tex content in the app | |
| with st.expander("📄 View LaTeX (.tex) File Content"): | |
| st.code(latex_report, language="latex") | |