app.py

import streamlit as st

# Page title
st.title("🩺🔍 Search Results")

# Date and title
st.markdown("**Date:** 08 Dec 2023")
st.markdown("**Title:** Machine-learned molecular mechanics force field for the simulation of protein-ligand systems and beyond")
st.markdown("[**Abstract Link**](https://arxiv.org/abs/2307.07085)")
st.markdown("[**PDF Link**](https://arxiv.org/pdf/2307.07085)")
st.write("---")

# Sample table
search_data = [
    {"Date": "08 Dec 2023", "Title": "Machine-learned molecular mechanics force field for the simulation of protein-ligand systems and beyond", "Abstract Link": "https://arxiv.org/abs/2307.07085", "PDF Link": "https://arxiv.org/pdf/2307.07085"},
    {"Date": "11 Apr 2023", "Title": "Design, Integration, and Field Evaluation of a Robotic Blossom Thinning System for Tree Fruit Crops", "Abstract Link": "https://arxiv.org/abs/2304.04919", "PDF Link": "https://arxiv.org/pdf/2304.04919"},
    # Add more rows as needed...
]

# Display table in Streamlit
st.write("### 📅 Summary of Search Results")
st.table(search_data)

st.markdown('''


Discovery of Espaloma-0.3 (Hero's Journey)

Ordinary World: Traditional force fields struggle with flexibility and extensibility.
Call to Adventure: Researchers propose a new approach using graph neural networks.
Refusal of the Call: Skeptics doubt the new method's feasibility without extensive computational resources.
Meeting the Mentor: Collaboration with experts in quantum chemistry and machine learning.
Crossing the Threshold: Initial tests show promising results, validating the concept.
Tests, Allies, and Enemies: The method faces challenges with specific molecular systems but gains support.
Approach to the Inmost Cave: Intensive training on a diverse dataset.
Ordeal: Tackling edge cases and ensuring stability in simulations.
Reward: The model achieves impressive accuracy and robustness.
The Road Back: Publication and refinement for real-world applications.
Resurrection: Acceptance and adoption in the wider scientific community.
Return with the Elixir: A new, powerful tool for drug discovery and molecular simulations.
Robotic Blossom Thinning (Rags to Riches)

Initial Wholeness: Apple orchards rely heavily on manual labor.
Fall from Grace: Inefficiency and cost concerns rise.
Journey: Researchers develop a robotic solution for blossom thinning.
Personal Resolve: Field tests reveal the robot's potential.
Self-discovery: Optimizing the end-effector's performance.
Major Victory: Significant reduction in labor and cost.
False Defeat: Encountering technical issues during deployment.
Final Victory: Successful large-scale adoption of the robotic system.
Climax: Recognition of the system’s effectiveness and efficiency.
Happily Ever After: Sustainable and cost-effective orchard management.
Graph-Neural-Network Approach for Force Fields (Quest)

Goals: Develop accurate and extendible force fields for large organic molecules.
Challenges: Accurately modeling complex interactions.
Journey: Combining physics-driven potentials with neural network models.
Teamwork: Collaboration between physicists, chemists, and data scientists.
Trials: Extensive testing on different molecular sizes.
Transformation: The approach proves to be robust and extendible.
Setbacks: Refining the model for diverse chemical domains.
Redemption: Improved predictions for new molecular systems.
Success: Establishing a new standard for force field development.
Homecoming: Adoption in scientific research and industry applications.
''')


# Streamlit app

import streamlit as st

st.title("🩺🔍 Search Results")

# Add Stories
st.header("Discovery of Espaloma-0.3 (Hero's Journey) 🧙‍♂️")
st.markdown("1. **Ordinary World:** Traditional force fields struggle with flexibility and extensibility.")
st.markdown("2. **Call to Adventure:** Researchers propose a new approach using graph neural networks.")
# Continue story steps...

st.header("Robotic Blossom Thinning (Rags to Riches) 🛠️")
st.markdown("1. **Initial Wholeness:** Apple orchards rely heavily on manual labor.")
# Continue story steps...

st.header("Graph-Neural-Network Approach for Force Fields (Quest) 🕵️‍♂️")
st.markdown("1. **Goals:** Develop accurate and extendible force fields for large organic molecules.")
# Continue story steps...

# Display Search Results with a table
st.write("### 📅 Summary of Search Results")
search_data = [
    {"Date": "08 Dec 2023", "Title": "Machine-learned molecular mechanics force field for the simulation of protein-ligand systems and beyond", "Abstract Link": "https://arxiv.org/abs/2307.07085", "PDF Link": "https://arxiv.org/pdf/2307.07085"},
    {"Date": "11 Apr 2023", "Title": "Design, Integration, and Field Evaluation of a Robotic Blossom Thinning System for Tree Fruit Crops", "Abstract Link": "https