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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")
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