Create app.py

app.py

@@ -0,0 +1,197 @@
+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()