backup1.app.py

import streamlit as st
import time

# Initialize the slide index in session state (if not already set)
if "slide_idx" not in st.session_state:
    st.session_state.slide_idx = 0

# Define a list of 10 slides. Each slide has a left and a right page.
# Each paper entry contains the paper number, title, arXiv link, and code link.
slides = [
    {
        "left": """
**#1. Neural Module Networks for Reasoning**  
[Arxiv](https://arxiv.org/abs/1234.5678) | [Code](https://github.com/example/nnm)

**#2. Neuro-Symbolic AI for Reasoning**  
[Arxiv](https://arxiv.org/abs/2345.6789) | [Code](https://github.com/example/nsa)
        """,
        "right": """
**#3. Transformer Models for Multi-step Reasoning**  
[Arxiv](https://arxiv.org/abs/3456.7890) | [Code](https://github.com/example/transformer)

**#4. Graph Neural Networks in AI**  
[Arxiv](https://arxiv.org/abs/4567.8901) | [Code](https://github.com/example/gnn)
        """
    },
    {
        "left": """
**#5. Memory-Augmented Networks for Episodic Recall**  
[Arxiv](https://arxiv.org/abs/5678.9012) | [Code](https://github.com/example/memory)

**#6. Self-Supervised Learning for AI**  
[Arxiv](https://arxiv.org/abs/6789.0123) | [Code](https://github.com/example/selfsup)
        """,
        "right": """
**#7. Reinforcement Learning from Human Feedback**  
[Arxiv](https://arxiv.org/abs/7890.1234) | [Code](https://github.com/example/rlhf)

**#8. Transfer Learning in AI Systems**  
[Arxiv](https://arxiv.org/abs/8901.2345) | [Code](https://github.com/example/transfer)
        """
    },
    {
        "left": """
**#9. Deep Learning for Medical Imaging**  
[Arxiv](https://arxiv.org/abs/9012.3456) | [Code](https://github.com/example/medimg)

**#10. Computer Vision in Telemedicine**  
[Arxiv](https://arxiv.org/abs/0123.4567) | [Code](https://github.com/example/cvtele)
        """,
        "right": """
**#11. Automated Clinical Documentation via NLP**  
[Arxiv](https://arxiv.org/abs/1234.5679) | [Code](https://github.com/example/clinicalnlp)

**#12. Real-Time Transcription and Analysis**  
[Arxiv](https://arxiv.org/abs/2345.6780) | [Code](https://github.com/example/realtime)
        """
    },
    {
        "left": """
**#13. Personalized Treatment Recommendation**  
[Arxiv](https://arxiv.org/abs/3456.7891) | [Code](https://github.com/example/treatment)

**#14. Integration of Genomic Data in AI**  
[Arxiv](https://arxiv.org/abs/4567.8902) | [Code](https://github.com/example/genomics)
        """,
        "right": """
**#15. Crowdsourcing in AI Evaluation**  
[Arxiv](https://arxiv.org/abs/5678.9013) | [Code](https://github.com/example/crowd)

**#16. Evaluating AI with Human Feedback**  
[Arxiv](https://arxiv.org/abs/6789.0124) | [Code](https://github.com/example/evaluation)
        """
    },
    {
        "left": """
**#17. Gradio and Streamlit for Rapid Prototyping**  
[Arxiv](https://arxiv.org/abs/7890.1235) | [Code](https://github.com/example/gradio)

**#18. Interactive Demos in Python**  
[Arxiv](https://arxiv.org/abs/8901.2346) | [Code](https://github.com/example/interactive)
        """,
        "right": """
**#19. HPC for Scaling AI Models**  
[Arxiv](https://arxiv.org/abs/9012.3457) | [Code](https://github.com/example/hpc)

**#20. Model Parallelism and Pipeline Techniques**  
[Arxiv](https://arxiv.org/abs/0123.4568) | [Code](https://github.com/example/parallel)
        """
    },
    {
        "left": """
**#21. Imitation Learning for Behavior Cloning**  
[Arxiv](https://arxiv.org/abs/1234.5680) | [Code](https://github.com/example/imitate)

**#22. GANs for Mirroring Human Actions**  
[Arxiv](https://arxiv.org/abs/2345.6781) | [Code](https://github.com/example/ganmirror)
        """,
        "right": """
**#23. Empathic AI for Shared World Modeling**  
[Arxiv](https://arxiv.org/abs/3456.7892) | [Code](https://github.com/example/empathic)

**#24. Deep Reinforcement Learning in Clinical Support**  
[Arxiv](https://arxiv.org/abs/4567.8903) | [Code](https://github.com/example/deeprl)
        """
    },
    {
        "left": """
**#25. Mixture of Experts for AI Systems**  
[Arxiv](https://arxiv.org/abs/5678.9014) | [Code](https://github.com/example/moe)

**#26. Conditional Computation and Routing Strategies**  
[Arxiv](https://arxiv.org/abs/6789.0125) | [Code](https://github.com/example/routing)
        """,
        "right": """
**#27. Ensemble Learning in AI**  
[Arxiv](https://arxiv.org/abs/7890.1236) | [Code](https://github.com/example/ensemble)

**#28. Knowledge Distillation Across Models**  
[Arxiv](https://arxiv.org/abs/8901.2347) | [Code](https://github.com/example/distill)
        """
    },
    {
        "left": """
**#29. Neural Networks for Adversarial Attacks**  
[Arxiv](https://arxiv.org/abs/9012.3458) | [Code](https://github.com/example/adversary)

**#30. Robust Training with Natural Transformations**  
[Arxiv](https://arxiv.org/abs/0123.4569) | [Code](https://github.com/example/robust)
        """,
        "right": """
**#31. Text-to-Image Translation with GANs**  
[Arxiv](https://arxiv.org/abs/1234.5681) | [Code](https://github.com/example/t2i)

**#32. Controlled Caption Generation via Adversarial Attacks**  
[Arxiv](https://arxiv.org/abs/2345.6782) | [Code](https://github.com/example/caption)
        """
    },
    {
        "left": """
**#33. Multi-Modal Autoencoders for Medical Data**  
[Arxiv](https://arxiv.org/abs/3456.7893) | [Code](https://github.com/example/multimodal)

**#34. Integration of Vision and Language in Healthcare**  
[Arxiv](https://arxiv.org/abs/4567.8904) | [Code](https://github.com/example/visionlang)
        """,
        "right": """
**#35. Reinforcement Learning for Medical QA Systems**  
[Arxiv](https://arxiv.org/abs/5678.9015) | [Code](https://github.com/example/medicalqa)

**#36. Large-Scale Clinical Language Models**  
[Arxiv](https://arxiv.org/abs/6789.0126) | [Code](https://github.com/example/clinicalllm)
        """
    },
    {
        "left": """
**#37. Efficient Transformers for Clinical NLP**  
[Arxiv](https://arxiv.org/abs/7890.1237) | [Code](https://github.com/example/lightllm)

**#38. Continual Learning for Medical AI**  
[Arxiv](https://arxiv.org/abs/8901.2348) | [Code](https://github.com/example/continual)
        """,
        "right": """
**#39. Active Learning for AI Annotation**  
[Arxiv](https://arxiv.org/abs/9012.3459) | [Code](https://github.com/example/active)

**#40. Automated Model Selection and Routing**  
[Arxiv](https://arxiv.org/abs/0123.4570) | [Code](https://github.com/example/modelselect)
        """
    }
]

num_slides = len(slides)
current_slide = slides[st.session_state.slide_idx]

# Display slide header (e.g. "Slide 1 of 10")
st.markdown(f"## Slide {st.session_state.slide_idx + 1} of {num_slides}")

# Display left and right pages side by side
col_left, col_right = st.columns(2)
with col_left:
    st.markdown("### Left Page")
    st.markdown(current_slide["left"], unsafe_allow_html=True)
with col_right:
    st.markdown("### Right Page")
    st.markdown(current_slide["right"], unsafe_allow_html=True)

# Countdown timer (15 seconds) for auto-advancement
for remaining in range(15, 0, -1):
    st.markdown(f"**Advancing in {remaining} seconds...**")
    time.sleep(1)

# Advance to the next slide (wrap around if at the end)
st.session_state.slide_idx = (st.session_state.slide_idx + 1) % num_slides

# Rerun the app to display the next slide
st.rerun()