Create app.py

app.py

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+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
+
+
+