added cases and broswer mode

.streamlit/config.toml

@@ -0,0 +1,8 @@
+[theme]
+# Default to light theme - user can switch via theme selector in the app
+base = "light"
+primaryColor = "#384166"
+backgroundColor = "#ffffff"
+secondaryBackgroundColor = "#f8f9fa"
+textColor = "#262730"
+font = "sans serif"

case-study-memory/memo_4/chat_history.json

@@ -0,0 +1,88 @@
+{
+  "timestamp": "2026-02-23T14:25:39.695263",
+  "workflow_type": "full",
+  "metadata": {
+    "workflow_type": "full",
+    "query": "This is a classification task where the target is the mutagenicity label. Preprocess the molecular representations (e.g., convert SMILES to fixed-length descriptors or fingerprints) and perform a proper train/test split. Train a set of suitable machine learning / deep learning models, evaluate classification performance on the test set using appropriate metrics and report the model performance. ",
+    "path": null,
+    "data_path": "/private/var/folders/xm/j_y2w6zd2tnch_l4t705nly80000gn/T/scider_uploads/tmpg00nt1y2/extracted",
+    "run_data": true,
+    "run_exp": true
+  },
+  "messages": [
+    {
+      "role": "assistant",
+      "content": "Hello. I can run ideation, data analysis, experiments, or a full workflow.\n\nPlease select a workflow type below to get started."
+    },
+    {
+      "role": "user",
+      "content": "Full Workflow: This is a classification task where the target is the mutagenicity label. Preprocess the molecular representations (e.g., convert SMILES to fixed-length descriptors or fingerprints) and perform a proper train/test split. Train a set of suitable machine learning / deep learning models, evaluate classification performance on the test set using appropriate metrics and report the model performance.  (Data: /private/var/folders/xm/j_y2w6zd2tnch_l4t705nly80000gn/T/scider_uploads/tmpg00nt1y2/extracted) [Data Analysis] [Experiment]"
+    },
+    {
+      "role": "assistant",
+      "content": "# Full SciEvo Workflow with Ideation Summary\n\n## Research Ideation\n\n# Research Ideation Report\n\n## Research Topic\nThis is a classification task where the target is the mutagenicity label. Preprocess the molecular representations (e.g., convert SMILES to fixed-length descriptors or fingerprints) and perform a proper train/test split. Train a set of suitable machine learning / deep learning models, evaluate classification performance on the test set using appropriate metrics and report the model performance. \n## Literature Review\n- Papers reviewed: 0\n- Papers analyzed: 0\n\n## Generated Research Ideas\n\n```json\n{\n  \"novelty_score\": 7.8,\n  \"breakdown\": {\n    \"uniqueness\": 2.8,\n    \"innovation\": 2.5,\n    \"gap_addressing\": 2.0,\n    \"potential_impact\": 2.0\n  },\n  \"feedback\": \"The proposed research ideas are strong and address several significant gaps in the standard workflow for mutagenicity prediction. The primary strength lies in moving beyond conventional molecular descriptors and fingerprints towards more advanced techniques like Graph Neural Networks (GNNs) and ensemble methods combining diverse representations. The inclusion of interpretability and uncertainty quantification further enhances the novelty and practical relevance of these ideas. However, it's important to acknowledge that GNNs for molecular property prediction and ensemble learning are established research areas. The novelty here comes from their specific application and combination within the context of mutagenicity prediction, along with the focus on interpretability and uncertainty. The identified gaps are well-articulated, particularly the limitations of current representations and validation strategies. The potential impact is high, as improved, interpretable, and robust mutagenicity prediction is crucial for drug discovery and chemical safety.\",\n  \"comparison_with_literature\": \"Given that no papers were reviewed in the previous turn, this comparison is based on the *assumption* that a standard literature search for mutagenicity prediction would reveal numerous studies using traditional descriptors (e.g., ECFP, physical-chemical properties) with standard ML models (e.g., Random Forests, SVMs). \\n\\n**Idea 1 (GNNs):** While GNNs are increasingly used for molecular property prediction across various domains, their *specific* application and detailed exploration for mutagenicity prediction, especially with a strong focus on *interpretable feature attribution*, represents a significant step beyond the standard approaches. Many existing GNN studies might focus solely on predictive performance without delving into chemical interpretability for toxicology.\\n\\n**Idea 2 (Ensemble of Diverse Representations):** The concept of ensemble learning is well-established. However, the novelty here lies in the *systematic combination of highly diverse representations* (traditional, learned embeddings from LMs, and GNN-based) specifically for mutagenicity. Many ensemble studies might focus on variations of the same representation or similar model types. This approach addresses the gap of relying on single representation types, which might be a limitation in current literature.\\n\\n**Idea 3 (Uncertainty-Aware & Active Learning):** Uncertainty quantification in ML is a growing field. Its application to mutagenicity prediction, coupled with an *active learning strategy for efficient data curation*, is a more advanced and novel contribution. While some studies might implicitly assess model robustness, explicitly developing and leveraging uncertainty estimates to guide experimental design for toxicology represents a significant gap closure compared to models that only report point predictions.\"\n}\n```\n\n## Novelty Assessment\n\n**Novelty Score: 7.80/10**\n\nThe proposed research ideas are strong and address several significant gaps in the standard workflow for mutagenicity prediction. The primary strength lies in moving beyond conventional molecular descriptors and fingerprints towards more advanced techniques like Graph Neural Networks (GNNs) and ensemble methods combining diverse representations. The inclusion of interpretability and uncertainty quantification further enhances the novelty and practical relevance of these ideas. However, it's important to acknowledge that GNNs for molecular property prediction and ensemble learning are established research areas. The novelty here comes from their specific application and combination within the context of mutagenicity prediction, along with the focus on interpretability and uncertainty. The identified gaps are well-articulated, particularly the limitations of current representations and validation strategies. The potential impact is high, as improved, interpretable, and robust mutagenicity prediction is crucial for drug discovery and chemical safety.\n\nComparison with Literature: Given that no papers were reviewed in the previous turn, this comparison is based on the *assumption* that a standard literature search for mutagenicity prediction would reveal numerous studies using traditional descriptors (e.g., ECFP, physical-chemical properties) with standard ML models (e.g., Random Forests, SVMs). \n\n**Idea 1 (GNNs):** While GNNs are increasingly used for molecular property prediction across various domains, their *specific* application and detailed exploration for mutagenicity prediction, especially with a strong focus on *interpretable feature attribution*, represents a significant step beyond the standard approaches. Many existing GNN studies might focus solely on predictive performance without delving into chemical interpretability for toxicology.\n\n**Idea 2 (Ensemble of Diverse Representations):** The concept of ensemble learning is well-established. However, the novelty here lies in the *systematic combination of highly diverse representations* (traditional, learned embeddings from LMs, and GNN-based) specifically for mutagenicity. Many ensemble studies might focus on variations of the same representation or similar model types. This approach addresses the gap of relying on single representation types, which might be a limitation in current literature.\n\n**Idea 3 (Uncertainty-Aware & Active Learning):** Uncertainty quantification in ML is a growing field. Its application to mutagenicity prediction, coupled with an *active learning strategy for efficient data curation*, is a more advanced and novel contribution. While some studies might implicitly assess model robustness, explicitly developing and leveraging uncertainty estimates to guide experimental design for toxicology represents a significant gap closure compared to models that only report point predictions.\n## Summary\nThis ideation report was generated through literature review of 0 academic papers.\nThe research ideas presented above are based on analysis of current research gaps and opportunities.\n\n\n**Novelty Score**: 7.80/10\n**Papers Reviewed**: 0\n\n\n---\n\n## Data Analysis\n\n# Data Analysis Summary\n\n## 1. Overview\n\nThis analysis focused on exploring and understanding the content of data files located within the directory: `/private/var/folders/xm/j_y2w6zd2tnch_l4t705nly80000gn/T/scider_uploads/tmpg00nt1y2/extracted`. The primary objective was to read and analyze the actual data within these files, moving beyond just directory inspection. The expectation was to identify file formats, data structures, key features, and derive meaningful insights.\n\n## 2. Data Characteristics\n\nDuring the analysis, the following data file was identified and examined:\n\n*   **File:** `Mutagenicity_N6512.csv`\n    *   **Format:** Comma-Separated Values (CSV).\n    *   **Structure:** Tabular data with distinct columns.\n    *   **Columns Identified:**\n        *   `CAS_NO`: Chemical Abstracts Service Registry Number (String).\n        *   `Source`: Data source identifier (String, e.g., CCRIS, VITIC).\n        *   `Activity`: Numerical indicator of mutagenicity (Integer, 0 or 1).\n        *   `Steroid`: Integer, consistently observed as -1.\n        *   `WDI`: Appears to be an empty column (String).\n        *   `Canonical_Smiles`: SMILES notation representing chemical structures (String).\n        *   `REFERENCE`: Bibliographic citation or reference for the data entry (String).\n    *   **Data Types:** Primarily strings and integers, with SMILES strings representing complex chemical structures.\n\nNo `metadata.json` file was found in the provided directory, which limited the scope of analysis to the available CSV file.\n\n## 3. Key Findings\n\n*   **Mutagenicity Data:** The core of the `Mutagenicity_N6512.csv` file is information pertaining to the mutagenic activity of chemical compounds. The `Activity` column serves as a crucial binary classifier (0 for non-mutagenic, 1 for mutagenic).\n*   **Chemical Identification:** Each chemical is uniquely identified by its `CAS_NO`, and its structure is represented using `Canonical_Smiles`. This allows for the potential correlation of structural features with mutagenic activity.\n*   **Diverse Data Sources:** The `Source` column indicates that the data originates from multiple databases or sources, such as CCRIS and VITIC. This suggests a consolidation of information from different chemical and toxicological information systems.\n*   **Non-Steroidal Nature Indicated:** The `Steroid` column consistently contains the value -1. This strongly suggests that the chemicals in this dataset are not steroids, or this property is marked as absent/not applicable.\n*   **Potential for Predictive Modeling:** The dataset, with its chemical identifiers, structures, and mutagenicity labels, is well-suited for developing predictive models to assess the mutagenic potential of new chemical compounds based on their structural properties.\n\n## 4. Data Quality\n\n*   **Empty `WDI` Column:** The `WDI` column appears to be entirely empty across the observed data. This column might be a remnant from a previous dataset or a placeholder that was not populated.\n*   **Consistent `Steroid` Value:** The uniform presence of `-1` in the `Steroid` column indicates a lack of variation for this feature within the dataset, simplifying its interpretation but also limiting its analytical utility.\n*   **Referential Integrity:** The `REFERENCE` column contains detailed citations. Further analysis could involve checking for redundancy or variations in citation formatting.\n*   **Data Completeness:** While the initial inspection covered the first 50 lines, a full read of the file would be necessary to confirm the consistency of these observations (e.g., emptiness of `WDI`, value of `Steroid`) across the entire dataset.\n\n## 5. Recommendations\n\n*   **Full File Read:** Perform a complete read of `Mutagenicity_N6512.csv` to:\n    *   Confirm the consistency of data quality observations (e.g., empty `WDI` column, `-1` in `Steroid` column) throughout the entire file.\n    *   Analyze the distribution of the `Activity` column to understand the class balance (proportion of mutagenic vs. non-mutagenic compounds).\n    *   Investigate the variety and frequency of entries in the `Source` and `REFERENCE` columns.\n    *   Examine the `Canonical_Smiles` strings for structural complexity and potential patterns.\n*   **Data Cleaning:** Based on the full read, decide on a strategy for the `WDI` column (e.g., remove it).\n*   **Feature Engineering:** The `Canonical_Smiles` column is rich in information. Consider using cheminformatics tools to generate molecular descriptors from SMILES strings, which can serve as features for machine learning models.\n*   **Exploratory Data Analysis (EDA):** Conduct more in-depth EDA, potentially including visualizations, to explore relationships between `Canonical_Smiles` (or derived features) and `Activity`.\n*   **Contextual Information:** If possible, seek external information about the sources (CCRIS, VITIC) mentioned in the `Source` column to better understand the context and potential biases of the data.\n\n\n---\n\n## Experiment Results\n\n# Experiment Workflow Failed\n\nError: ExperimentAgent failed: litellm.ServiceUnavailableError: GeminiException - {\n  \"error\": {\n    \"code\": 503,\n    \"message\": \"This model is currently experiencing high demand. Spikes in demand are usually temporary. Please try again later.\",\n    \"status\": \"UNAVAILABLE\"\n  }\n}\n\n\n\n---\n\n## Workflow Metadata\n\n- **Research Topic**: This is a classification task where the target is the mutagenicity label. Preprocess the molecular representations (e.g., convert SMILES to fixed-length descriptors or fingerprints) and perform a proper train/test split. Train a set of suitable machine learning / deep learning models, evaluate classification performance on the test set using appropriate metrics and report the model performance. \n- **Research Domain**: Not specified\n- **Workspace**: /private/var/folders/xm/j_y2w6zd2tnch_l4t705nly80000gn/T/scider_uploads/tmpg00nt1y2\n- **Final Status**: failed\n",
+      "intermediate_state": [
+        {
+          "node_name": "ideation/literature_search",
+          "output": "Found 0 papers for query: 'This is a classification task where the target is the mutagenicity label. Preprocess the molecular representations (e.g., convert SMILES to fixed-length descriptors or fingerprints) and perform a proper train/test split. Train a set of suitable machine learning / deep learning models, evaluate classification performance on the test set using appropriate metrics and report the model performance. '\n\n"
+        },
+        {
+          "node_name": "ideation/generate_ideas",
+          "output": "## Research Title: Enhancing Mutagenicity Prediction through Advanced Molecular Representations and Ensemble Learning\n\n### Literature Review Summary\n- Number of papers reviewed: 0 (No papers were provided for review in the previous turn. Therefore, this summary is based on the absence of prior literature.)\n- Key themes identified: Mutagenicity prediction, molecular representation, machine learning, deep learning, classification, train/test split, evaluation metrics.\n- Current state of research: The user has outlined a standard machine learning workflow for predicting mutagenicity. This typically involves preprocessing molecular structures (SMILES to descriptors/fingerprints), splitting data, training models, and evaluating performance. However, without specific papers, the current state of advanced techniques, benchmark datasets, and state-of-the-art performance is unknown.\n\n### Research Gaps Identified\n1.  **Limited Exploration of Advanced Molecular Representations**: The prompt mentions converting SMILES to fixed-length descriptors or fingerprints. While common, these methods might not fully capture the complex structural nuances and electronic properties that contribute to mutagenicity. There's a gap in exploring more sophisticated, data-driven, and context-aware molecular representations, such as graph-based embeddings (e.g., graph convolutional networks, graph attention networks) or learned molecular fingerprints that dynamically adapt to the predictive task.\n2.  **Potential for Overfitting and Lack of Robustness**: Standard train/test splits, while necessary, can sometimes lead to models that perform well on a specific split but fail to generalize to unseen data from different sources or with slightly different distributions. There's a gap in robust validation strategies that go beyond simple splits, such as cross-validation across diverse datasets, external validation, and techniques that assess model uncertainty and reliability.\n3.  **Suboptimal Model Generalization and Interpretability**: Many machine learning models, especially deep learning ones, can be \"black boxes.\" While achieving high accuracy is important, understanding *why* a molecule is predicted as mutagenic or not is crucial for drug design and safety assessment. There's a gap in developing interpretable models or post-hoc interpretability methods that can provide insights into the molecular features driving mutagenicity predictions.\n4.  **Underutilization of Ensemble Methods for Improved Accuracy and Robustness**: While training \"a set of suitable models\" is mentioned, a systematic approach to ensemble learning, combining diverse models (e.g., different architectures, different feature sets) to leverage their collective strength, might not be fully exploited. This could lead to more accurate and robust predictions.\n\n### Proposed Research Ideas\n\n```json\n[\n  {\n    \"title\": \"Graph Neural Networks for Enhanced Mutagenicity Prediction with Interpretable Feature Attribution\",\n    \"description\": \"This research proposes the use of Graph Neural Networks (GNNs) for predicting mutagenicity. GNNs can directly learn from the molecular graph structure, capturing topological and chemical environment information more effectively than traditional fixed-length descriptors. The research will explore different GNN architectures (e.g., GCN, GAT, MPNN) and their suitability for mutagenicity prediction. Furthermore, it will integrate interpretability techniques, such as Graph Attention mechanisms or GNNExplainer, to identify specific substructures or atoms within molecules that are most predictive of mutagenicity, thereby providing actionable insights.\",\n    \"rationale\": \"GNNs offer a more powerful and flexible way to represent molecules compared to traditional fingerprints. Their ability to learn context-aware embeddings can lead to improved predictive accuracy. Incorporating interpretability addresses the 'black box' nature of deep learning models, making the predictions more trustworthy and useful for domain experts.\",\n    \"potential_impact\": \"Significant improvement in mutagenicity prediction accuracy, development of interpretable models for toxicology, and identification of novel structural alerts for mutagenicity.\",\n    \"related_papers\": []\n  },\n  {\n    \"title\": \"Ensemble of Diverse Molecular Representations for Robust Mutagenicity Classification\",\n    \"description\": \"This project will investigate the power of ensemble learning by combining predictions from models trained on diverse molecular representations. Instead of relying on a single representation type, we will generate and evaluate an ensemble of models that utilize: 1) traditional molecular descriptors (e.g., RDKit descriptors), 2) molecular fingerprints (e.g., ECFP), 3) learned embeddings from pre-trained molecular language models (e.g., MolBERT, MolGPT), and 4) graph-based representations (as explored in Idea 1). Different ensemble strategies (e.g., majority voting, weighted averaging, stacking) will be explored to maximize predictive performance and generalization.\",\n    \"rationale\": \"Combining diverse feature spaces and model types in an ensemble is a well-established strategy for improving robustness and accuracy. Different representations capture different aspects of molecular information, and an ensemble can leverage these complementary strengths to provide a more comprehensive and reliable prediction. This approach is likely to be more resilient to dataset biases.\",\n    \"potential_impact\": \"Higher predictive accuracy and improved generalization for mutagenicity prediction, creating a more reliable tool for early-stage toxicity screening in drug discovery and chemical safety assessment.\",\n    \"related_papers\": []\n  },\n  {\n    \"title\": \"Uncertainty-Aware Mutagenicity Prediction and Active Learning for Efficient Data Curation\",\n    \"description\": \"This research will focus on developing uncertainty-aware models for mutagenicity prediction. Utilizing Bayesian deep learning methods or ensemble variance, the models will not only predict mutagenicity but also quantify the confidence in their predictions. This uncertainty estimation can then be used to drive an active learning strategy. The system will prioritize the testing of new, uncharacterized molecules for which the model is most uncertain, thereby enabling more efficient and cost-effective data curation and experimental validation. A proper train/test split will be maintained, with uncertainty assessed on a held-out validation set and used for guiding the selection of samples for *future* experimental testing.\",\n    \"rationale\": \"In toxicology, understanding prediction uncertainty is as important as the prediction itself. High uncertainty indicates regions of chemical space where current models are weak, highlighting the need for more data or expert review. Active learning based on uncertainty can significantly reduce the number of costly experiments required to build high-performing predictive models.\",\n    \"potential_impact\": \"Development of more reliable mutagenicity prediction systems that provide confidence estimates, a framework for intelligent experimental design and data acquisition in toxicology, and a more efficient path to building robust predictive models.\",\n    \"related_papers\": []\n  }\n]\n```\n\n### Next Steps\n-   **Detailed Literature Search**: Conduct a deep dive into recent literature on:\n    -   Graph Neural Networks for molecular property prediction (specifically toxicology).\n    -   Ensemble learning strategies applied to cheminformatics.\n    -   Uncertainty quantification in machine learning for chemical safety.\n    -   State-of-the-art molecular representations and their application in QSAR.\n    -   Publicly available mutagenicity datasets (e.g., Ames test datasets like Ames Mutagenicity dataset, Tox21).\n-   **Dataset Acquisition and Preprocessing**: Identify and obtain suitable benchmark mutagenicity datasets. Implement robust preprocessing pipelines for SMILES to various molecular representations (descriptors, fingerprints, molecular graphs).\n-   **Model Implementation and Baseline Establishment**: Implement baseline classification models (e.g., Random Forest, SVM, simple MLP) using standard representations. This will serve as a benchmark for evaluating the proposed advanced methods.\n-   **Experiment Design**: Plan systematic experiments for each proposed idea, including defining appropriate evaluation metrics (e.g., AUC-ROC, AUC-PR, accuracy, F1-score, MCC), cross-validation strategies, and interpretability analysis protocols."
+        },
+        {
+          "node_name": "ideation/novelty_check",
+          "output": "```json\n{\n  \"novelty_score\": 7.8,\n  \"breakdown\": {\n    \"uniqueness\": 2.8,\n    \"innovation\": 2.5,\n    \"gap_addressing\": 2.0,\n    \"potential_impact\": 2.0\n  },\n  \"feedback\": \"The proposed research ideas are strong and address several significant gaps in the standard workflow for mutagenicity prediction. The primary strength lies in moving beyond conventional molecular descriptors and fingerprints towards more advanced techniques like Graph Neural Networks (GNNs) and ensemble methods combining diverse representations. The inclusion of interpretability and uncertainty quantification further enhances the novelty and practical relevance of these ideas. However, it's important to acknowledge that GNNs for molecular property prediction and ensemble learning are established research areas. The novelty here comes from their specific application and combination within the context of mutagenicity prediction, along with the focus on interpretability and uncertainty. The identified gaps are well-articulated, particularly the limitations of current representations and validation strategies. The potential impact is high, as improved, interpretable, and robust mutagenicity prediction is crucial for drug discovery and chemical safety.\",\n  \"comparison_with_literature\": \"Given that no papers were reviewed in the previous turn, this comparison is based on the *assumption* that a standard literature search for mutagenicity prediction would reveal numerous studies using traditional descriptors (e.g., ECFP, physical-chemical properties) with standard ML models (e.g., Random Forests, SVMs). \\n\\n**Idea 1 (GNNs):** While GNNs are increasingly used for molecular property prediction across various domains, their *specific* application and detailed exploration for mutagenicity prediction, especially with a strong focus on *interpretable feature attribution*, represents a significant step beyond the standard approaches. Many existing GNN studies might focus solely on predictive performance without delving into chemical interpretability for toxicology.\\n\\n**Idea 2 (Ensemble of Diverse Representations):** The concept of ensemble learning is well-established. However, the novelty here lies in the *systematic combination of highly diverse representations* (traditional, learned embeddings from LMs, and GNN-based) specifically for mutagenicity. Many ensemble studies might focus on variations of the same representation or similar model types. This approach addresses the gap of relying on single representation types, which might be a limitation in current literature.\\n\\n**Idea 3 (Uncertainty-Aware & Active Learning):** Uncertainty quantification in ML is a growing field. Its application to mutagenicity prediction, coupled with an *active learning strategy for efficient data curation*, is a more advanced and novel contribution. While some studies might implicitly assess model robustness, explicitly developing and leveraging uncertainty estimates to guide experimental design for toxicology represents a significant gap closure compared to models that only report point predictions.\"\n}\n```"
+        },
+        {
+          "node_name": "ideation/ideation_report",
+          "output": "# Research Ideation Report\n\n## Research Topic\nThis is a classification task where the target is the mutagenicity label. Preprocess the molecular representations (e.g., convert SMILES to fixed-length descriptors or fingerprints) and perform a proper train/test split. Train a set of suitable machine learning / deep learning models, evaluate classification performance on the test set using appropriate metrics and report the model performance. \n## Literature Review\n- Papers reviewed: 0\n- Papers analyzed: 0\n\n## Generated Research Ideas\n\n```json\n{\n  \"novelty_score\": 7.8,\n  \"breakdown\": {\n    \"uniqueness\": 2.8,\n    \"innovation\": 2.5,\n    \"gap_addressing\": 2.0,\n    \"potential_impact\": 2.0\n  },\n  \"feedback\": \"The proposed research ideas are strong and address several significant gaps in the standard workflow for mutagenicity prediction. The primary strength lies in moving beyond conventional molecular descriptors and fingerprints towards more advanced techniques like Graph Neural Networks (GNNs) and ensemble methods combining diverse representations. The inclusion of interpretability and uncertainty quantification further enhances the novelty and practical relevance of these ideas. However, it's important to acknowledge that GNNs for molecular property prediction and ensemble learning are established research areas. The novelty here comes from their specific application and combination within the context of mutagenicity prediction, along with the focus on interpretability and uncertainty. The identified gaps are well-articulated, particularly the limitations of current representations and validation strategies. The potential impact is high, as improved, interpretable, and robust mutagenicity prediction is crucial for drug discovery and chemical safety.\",\n  \"comparison_with_literature\": \"Given that no papers were reviewed in the previous turn, this comparison is based on the *assumption* that a standard literature search for mutagenicity prediction would reveal numerous studies using traditional descriptors (e.g., ECFP, physical-chemical properties) with standard ML models (e.g., Random Forests, SVMs). \\n\\n**Idea 1 (GNNs):** While GNNs are increasingly used for molecular property prediction across various domains, their *specific* application and detailed exploration for mutagenicity prediction, especially with a strong focus on *interpretable feature attribution*, represents a significant step beyond the standard approaches. Many existing GNN studies might focus solely on predictive performance without delving into chemical interpretability for toxicology.\\n\\n**Idea 2 (Ensemble of Diverse Representations):** The concept of ensemble learning is well-established. However, the novelty here lies in the *systematic combination of highly diverse representations* (traditional, learned embeddings from LMs, and GNN-based) specifically for mutagenicity. Many ensemble studies might focus on variations of the same representation or similar model types. This approach addresses the gap of relying on single representation types, which might be a limitation in current literature.\\n\\n**Idea 3 (Uncertainty-Aware & Active Learning):** Uncertainty quantification in ML is a growing field. Its application to mutagenicity prediction, coupled with an *active learning strategy for efficient data curation*, is a more advanced and novel contribution. While some studies might implicitly assess model robustness, explicitly developing and leveraging uncertainty estimates to guide experimental design for toxicology represents a significant gap closure compared to models that only report point predictions.\"\n}\n```\n\n## Novelty Assessment\n\n**Novelty Score: 7.80/10**\n\nThe proposed research ideas are strong and address several significant gaps in the standard workflow for mutagenicity prediction. The primary strength lies in moving beyond conventional molecular descriptors and fingerprints towards more advanced techniques like Graph Neural Networks (GNNs) and ensemble methods combining diverse representations. The inclusion of interpretability and uncertainty quantification further enhances the novelty and practical relevance of these ideas. However, it's important to acknowledge that GNNs for molecular property prediction and ensemble learning are established research areas. The novelty here comes from their specific application and combination within the context of mutagenicity prediction, along with the focus on interpretability and uncertainty. The identified gaps are well-articulated, particularly the limitations of current representations and validation strategies. The potential impact is high, as improved, interpretable, and robust mutagenicity prediction is crucial for drug discovery and chemical safety.\n\nComparison with Literature: Given that no papers were reviewed in the previous turn, this comparison is based on the *assumption* that a standard literature search for mutagenicity prediction would reveal numerous studies using traditional descriptors (e.g., ECFP, physical-chemical properties) with standard ML models (e.g., Random Forests, SVMs). \n\n**Idea 1 (GNNs):** While GNNs are increasingly used for molecular property prediction across various domains, their *specific* application and detailed exploration for mutagenicity prediction, especially with a strong focus on *interpretable feature attribution*, represents a significant step beyond the standard approaches. Many existing GNN studies might focus solely on predictive performance without delving into chemical interpretability for toxicology.\n\n**Idea 2 (Ensemble of Diverse Representations):** The concept of ensemble learning is well-established. However, the novelty here lies in the *systematic combination of highly diverse representations* (traditional, learned embeddings from LMs, and GNN-based) specifically for mutagenicity. Many ensemble studies might focus on variations of the same representation or similar model types. This approach addresses the gap of relying on single representation types, which might be a limitation in current literature.\n\n**Idea 3 (Uncertainty-Aware & Active Learning):** Uncertainty quantification in ML is a growing field. Its application to mutagenicity prediction, coupled with an *active learning strategy for efficient data curation*, is a more advanced and novel contribution. While some studies might implicitly assess model robustness, explicitly developing and leveraging uncertainty estimates to guide experimental design for toxicology represents a significant gap closure compared to models that only report point predictions.\n## Summary\nThis ideation report was generated through literature review of 0 academic papers.\nThe research ideas presented above are based on analysis of current research gaps and opportunities.\n"
+        },
+        {
+          "node_name": "data/paper_subagent",
+          "output": "Paper subagent completed. Found 10 papers, 0 datasets, 7 metrics.\n\nSummary: # Overview\n\n## Description\n\nThis research task focuses on the direct, content-centric analysis of data files provided within a specified directory. The primary objective is to move beyond mere file system inspection and delve into the actual information contained within these files. This involves reading and interpreting the data, understanding its structure, identifying key features, and extracting meaningful insights. The task emphasizes the practical application of data analysis techniques to raw data, requiring participants to engage directly with the content of files, regardless of their format (e.g., CSV, JSON, plain text). The goal is to foster a deep understanding of the data's inherent characteristics and to derive actionable intelligence from it.\n\nThe importance of this task lies in its foundational nature for many data-driven research and application areas. Before sophisticated modeling or complex analyses can be performed, a thorough understanding of the underlying data is paramount. This often begins with the ability to efficiently read, parse, and analyze various data file formats. The challenge is to extract meaningful information from potentially diverse and unstructured or semi-structured data sources without relying on external information or pre-existing knowledge about the dataset's domain. This hands-on approach to data exploration is crucial for tasks such as feature engineering, data cleaning, exploratory data analysis, and hypothesis generation.\n\n### Context\n\nThe field of data analysis and data mining is continuously evolving, driven by the ever-increasing volume and variety of data generated across numerous domains. From scientific research to business intelligence and social sciences, the ability to extract valuable information from raw data is a critical skill. This task situates itself within this broader context by emphasizing the fundamental step of data exploration and comprehension. Papers like \"Plague Dot Text: Text mining and annotation of outbreak reports of the Third Plague Pandemic (1894-1952)\" (Casey et al., 2020) highlight the necessity of analyzing textual data for scientific discovery, while \"MixedTrails: Bayesian hypothesis comparison on heterogeneous sequential data\" (Becker et al., 2016) points to the importance of analyzing sequential and heterogeneous data formats.\n\nCurrent approaches to data analysis often involve sophisticated algorithms and machine learning models. However, the success of these advanced techniques hinges on the quality and interpretability of the input data. This task addresses the crucial precursor to such analyses: understanding the data itself. The challenge lies in dealing with diverse file formats (CSV, JSON, text, etc.) and extracting relevant information efficiently. This is particularly relevant in fields like scientific data mining, as exemplified by \"Scientific Data Mining in Astronomy\" (Borne, 2009), where vast amounts of raw observational data require meticulous analysis. The ability to directly interact with and analyze the content of provided files, as mandated by this task, is a core competency for any data scientist or researcher.\n\n## Evaluation\n\nThis task does not have pre-defined quantitative evaluation metrics in the traditional sense, as it focuses on qualitative analysis and insight generation based on the provided data files. However, the depth, accuracy, and relevance of the insights derived from the data will be the primary basis for evaluation. Based on the related literature, the following metrics are commonly used in data mining and analysis tasks, which could be adapted to assess the thoroughness of the data analysis performed here:\n\n*   **Metric name**: Accuracy\n    *   **Description**: Measures the overall correctness of findings or classifications derived from the data. In this context, it would refer to the accuracy of interpretations and conclusions drawn from the data content.\n    *   **Formula**: Not applicable for qualitative analysis; relies on human judgment of correctness.\n    *   **Interpretation**: Higher accuracy indicates more correct interpretations.\n    *   **Usage**: Used to evaluate the correctness of derived patterns or classifications.\n\n*   **Metric name**: Precision and Recall (adapted)\n    *   **Description**: While typically used for classification, these can be adapted to measure the completeness and focus of the analysis. Precision would refer to the relevance and focus of the insights (avoiding irrelevant information), and Recall would refer to the comprehensiveness of the analysis (covering all significant aspects of the data).\n    *   **Formula**: Not directly applicable in a quantitative way.\n    *   **Interpretation**: High precision means insights are focused and relevant. High recall means all important aspects of the data have been analyzed.\n    *   **Usage**: To assess the quality and thoroughness of the data interpretation.\n\n*   **Metric name**: Insightfulness/Novelty\n    *   **Description**: Assesses the depth and originality of the conclusions drawn from the data. This goes beyond surface-level observations to uncover non-obvious patterns or relationships.\n    *   **Formula**: Not applicable.\n    *   **Interpretation**: More insightful and novel findings are highly valued.\n    *   **Usage**: Subjective evaluation of the quality and impact of the analysis.\n\n*   **Metric name**: Completeness of Data Understanding\n    *   **Description**: Evaluates how thoroughly the structure, format, data types, and content of each file have been understood and described.\n    *   **Formula**: Not applicable.\n    *   **Interpretation**: A comprehensive understanding of all data aspects indicates good performance.\n    *   **Usage**: To ensure all critical aspects of the data have been explored.\n\nOther metrics found in related literature, such as **Mean Square Error (MSE)**, **R-square**, **Adjusted R-square**, **Condition Number**, **Root Mean Square Error (RMSE)**, and **F-value**, are more relevant for quantitative modeling tasks and predictive analytics, which are subsequent steps after the initial data exploration and analysis emphasized in this task.\n\n## Dataset Description\n\nNo specific datasets were found during the search. The task operates on a provided directory containing data files.\n\n*   **Dataset Name**: N/A (Operates on provided directory)\n*   **Source**: Provided directory: `/private/var/folders/xm/j_y2w6zd2tnch_l4t705nly80000gn/T/scider_uploads/tmpg00nt1y2/extracted`\n*   **Description**: The directory is expected to contain various data files, potentially including but not limited to CSV, JSON, TXT, and other common data formats. The content of these files is unknown prior to analysis.\n*   **Size**: Variable, depending on the contents of the provided directory.\n*   **Domain**: General data analysis; specific domain will depend on the file contents.\n*   **Format**: Mixed (e.g., CSV, JSON, TXT, etc.).\n*   **Access**: Via the provided file path.\n*   **License**: Not applicable.\n*   **Preprocessing**: Users are expected to perform necessary reading and parsing steps based on the detected file formats.\n\n## Key Papers and References\n\nThe following papers provide context and highlight the importance of data mining, text mining, and analyzing diverse data types, which are relevant to the fundamental skills required for this task:\n\n*   **Title**: Plague Dot Text: Text mining and annotation of outbreak reports of the Third Plague Pandemic (1894-1952)\n    *   **Authors**: Arlene Casey, Mike Bennett, Richard Tobin, Claire Grover, Iona Walker\n    *   **Published**: 2020-02-04T17:16:36Z\n    *   **Main Contribution**: Demonstrates the application of text mining to historical outbreak reports to extract information that might not be captured in statistical data alone.\n    *   **URL**: https://arxiv.org/abs/2002.01415v3\n    *   **Relevance**: Underscores the value of analyzing unstructured text data for uncovering insights, a skill directly applicable to analyzing text files within the provided directory.\n\n*   **Title**: Text Data Mining from the Author's Perspective: Whose Text, Whose Mining, and to Whose Benefit?\n    *   **Authors**: Christine L. Borgman\n    *   **Published**: 2018-03-12T22:11:40Z\n    *   **Main Contribution**: Discusses the broader implications and ethical considerations of text data mining, emphasizing the perspective of data creators.\n    *   **URL**: https://arxiv.org/abs/1803.04552v1\n    *   **Relevance**: Provides a high-level perspective on data mining practices and their context, relevant for understanding the broader implications of data analysis.\n\n*   **Title**: MixedTrails: Bayesian hypothesis comparison on heterogeneous sequential data\n    *   **Authors**: Martin Becker, Florian Lemmerich, Philipp Singer, Markus Strohmaier, Andreas Hotho\n    *   **Published**: 2016-12-21T13:15:25Z\n    *   **Main Contribution**: Proposes a method for analyzing heterogeneous sequential data, highlighting the challenges and importance of dealing with diverse data types.\n    *   **URL**: https://arxiv.org/abs/1612.07612v2\n    *   **Relevance**: Illustrates the complexity and importance of analyzing data that comes in various formats and sequences, a skill needed for this task.\n\n*   **Title**: Scientific Data Mining in Astronomy\n    *   **Authors**: Kirk Borne\n    *   **Published**: 2009-11-03T05:34:54Z\n    *   **Main Contribution**: Explores the application of data mining techniques in astronomy, highlighting data mining as a fundamental tool for evidence-based discovery.\n    *   **URL**: https://arxiv.org/abs/0911.0505v1\n    *   **Relevance**: Shows how data mining is applied to large, raw datasets in scientific contexts, reinforcing the importance of the fundamental data analysis skills required by this task.\n\n*   **Title**: A Performance Study of Data Mining Techniques: Multiple Linear Regression vs. Factor Analysis\n    *   **Authors**: Abhishek Taneja, R. K. Chauhan\n    *   **Published**: 2011-08-26T07:08:13Z\n    *   **Main Contribution**: Compares the performance of different data mining techniques for data analysis.\n    *   **URL**: https://arxiv.org/abs/1108.5592v1\n    *   **Relevance**: Discusses various data mining techniques and performance metrics, relevant for understanding how to evaluate the outcomes of data analysis.\n\n## Recommended Approach\n\n*   **Common Methodologies**:\n    *   **File Listing and Inspection**: Start by using available tools to list all files in the provided directory to get an overview of the data sources.\n    *   **Format Identification**: For each file, determine its format (e.g., by file extension or by inspecting the header/first few lines).\n    *   **Data Reading and Parsing**: Use appropriate libraries or built-in functions to read and parse the data based on its identified format (e.g., `pandas` for CSV, `json` library for JSON, standard file I/O for text files).\n    *   **Exploratory Data Analysis (EDA)**:\n        *   **For CSV files**: Examine column names, data types, check for missing values, calculate descriptive statistics (mean, median, standard deviation), and visualize distributions.\n        *   **For JSON files**: Analyze the structure (nested objects, arrays), identify key-value pairs, and understand the hierarchy of the data.\n        *   **For Text files**: Identify patterns, extract key phrases or entities, analyze word frequencies, and potentially perform sentiment analysis or topic modeling if appropriate.\n    *   **Insight Generation**: Synthesize observations from the analysis of individual files to provide a comprehensive understanding of the data contained within the directory.\n\n*   **Best Practices**:\n    *   **Prioritize Content over Metadata**: Always focus on the actual data values and structure, not just file names or sizes.\n    *   **Handle Diverse Formats Gracefully**: Be prepared to work with multiple data formats and implement robust parsing strategies.\n    *   **Systematic Approach**: Analyze files systematically, documenting findings for each file before attempting to draw overarching conclusions.\n    *   **Iterative Exploration**: Data exploration is often iterative. Revisit files or aspects of the data as new insights emerge.\n    *   **Clear Documentation**: Document the process, observations, and conclusions clearly and concisely.\n\n*   **Potential Challenges**:\n    *   **Unknown Data Formats**: Encountering unfamiliar or corrupted file formats.\n    *   **Large File Sizes**: Processing very large files that may require memory-efficient techniques.\n    *   **Data Heterogeneity**: Dealing with files that have vastly different structures and types of information.\n    *   **Ambiguity in Text Data**: Extracting meaningful information from free-form text can be challenging due to ambiguity and natural language variations.\n    *   **Lack of Schema Information**: In cases of JSON or text files without explicit schemas, inferring structure can be difficult.\n\n*   **Recommended Tools/Frameworks**:\n    *   **Python**: A versatile language with extensive libraries for data manipulation and analysis.\n        *   `pandas`: For reading and analyzing tabular data (CSV).\n        *   `json`: For parsing JSON files.\n        *   `os`: For interacting with the file system (listing files).\n        *   `re` (regular expressions): For pattern matching in text files.\n    *   **Command-line tools**: `ls` (to list files), `head` / `tail` (to inspect file beginnings/ends), `file` (to determine file types).\n\n*   **Implementation Tips**:\n    *   **Start with `list_files`**: Always begin by understanding what files are present.\n    *   **Inspect First Lines**: Use `read_head` or similar to quickly gauge the content and format of each file before committing to reading the entire file.\n    *   **Error Handling**: Implement robust error handling for file reading and parsing to gracefully manage unexpected data issues.\n    *   **Visualize (if applicable)**: If the data lends itself to visualization (e.g., numerical data in CSVs), consider creating simple plots to identify trends or outliers."
+        },
+        {
+          "node_name": "data/planner",
+          "output": "```json\n{\n  \"steps\": [\n    \"List files in the directory /private/var/folders/xm/j_y2w6zd2tnch_l4t705nly80000gn/T/scider_uploads/tmpg00nt1y2/extracted to see what data files are available\",\n    \"Read the first 50 lines of the file 'data.csv' using the read_head tool to understand its structure and content\",\n    \"Analyze the content of 'data.csv' to identify column names, data types, and sample values\",\n    \"Read the first 50 lines of the file 'metadata.json' using the read_head tool to understand its structure and content\",\n    \"Analyze the content of 'metadata.json' to identify its schema, key-value pairs, and data types\",\n    \"Provide a summary of the data characteristics and structure found in 'data.csv' and 'metadata.json', including potential relationships between them\"\n  ]\n}\n```"
+        },
+        {
+          "node_name": "data/llm_chat",
+          "output": "I will start by listing the files in the provided directory."
+        },
+        {
+          "node_name": "data/tool_calling",
+          "output": "Tool: list_files\nResult: [Path: /private/var/folders/xm/j_y2w6zd2tnch_l4t705nly80000gn/T/scider_uploads/tmpg00nt1y2/extracted]\n[Total entries: 2]\n[Current offset: 0]\n[Limit: 50]\n[Returned entries: 2]\n[Has more: False]\n---\ntotal 3280\n-rw-r--r-- 1 harrylu_mac staff 1676551 Feb 23 14:07 Mutagenicity_N6512.csv\ndrwxr-xr-x 3 harrylu_mac staff      96 Feb 23 14:07 __MACOSX"
+        },
+        {
+          "node_name": "data/llm_chat",
+          "output": "The directory contains two entries: `Mutagenicity_N6512.csv` and `__MACOSX`. The `__MACOSX` directory is likely a system directory and may not contain relevant data files. I will proceed to inspect the `Mutagenicity_N6512.csv` file."
+        },
+        {
+          "node_name": "data/tool_calling",
+          "output": "Tool: read_head\nResult: === /private/var/folders/xm/j_y2w6zd2tnch_l4t705nly80000gn/T/scider_uploads/tmpg00nt1y2/extracted/Mutagenicity_N6512.csv ===\nCAS_NO,Source,Activity,Steroid,WDI,Canonical_Smiles,REFERENCE\n2475-33-4,VITIC,0,-1,,O=C1c2ccccc2C(=O)c3c1ccc4c3[nH]c5c6C(=O)c7ccccc7C(=O)c6c8[nH]c9c%10C(=O)c%11ccccc%11C(=O)c%10ccc9c8c45,\"JUDSON, PN, COOKE, PA, DOERRER, NG, GREENE, N, HANZLIK, RP, HARDY, C, HARTMANN, A, HINCHLIFFE, D, HOLDER, J, MUELLER, L, STEGER-HARTMANN, T, ROTHFUSS, A, SMITH, M, THOMAS, K, VESSEY, JD AND ZEIGER E. TOWARDS THE CREATION OF AN INTERNATIONAL TOXICOLOGY INFORMATION CENTRE. TOXICOLOGY 213(1-2):117-28, 2005\"\n820-75-7,CCRIS,1,-1,,NNC(=O)CNC(=O)\\C=N\\#N,\"MCCANN,J, CHOI,E, YAMASAKI,E AND AMES,BN, DETECTION OF CARCINOGENS ASMUTAGENS IN THE SALMONELLA/MICROSOME TEST: ASSAY OF 300 CHEMICALS, PROC. NATL.ACAD. SCI. USA 72(12):5135-5139, 1975\"\n2435-76-9,CCRIS,1,-1,,O=C1NC(=O)\\C(=N/#N)\\C=N1,\"HIRAMOTO,K, KATO,T AND KIKUGAWA,K, MECHANISMS OF THE DNA BREAKINGACTIVITY OF MUTAGENIC 5-DIAZOURACIL, M"
+        },
+        {
+          "node_name": "data/llm_chat",
+          "output": "The file `Mutagenicity_N6512.csv` appears to be a CSV file containing data related to mutagenicity.\n\nHere's an analysis of the first few lines:\n\n**File Format**: CSV\n\n**Columns**:\n*   `CAS_NO`: Chemical Abstracts Service Registry Number, a unique identifier for chemical substances.\n*   `Source`: The source of the data (e.g., CCRIS, VITIC).\n*   `Activity`: A numerical value indicating mutagenicity activity (0 or 1).\n*   `Steroid`: A binary indicator, likely (-1) suggesting it's not a steroid or absence of information.\n*   `WDI`: Seems to be a placeholder or not used.\n*   `Canonical_Smiles`: Simplified Molecular Input Line Entry System (SMILES) notation, a way to represent chemical structures in a string format.\n*   `REFERENCE`: Citation or reference for the data.\n\n**Data Types**:\n*   `CAS_NO`: String\n*   `Source`: String\n*   `Activity`: Integer (0 or 1)\n*   `Steroid`: Integer (-1)\n*   `WDI`: String (appears empty)\n*   `Canonical_Smiles`: String\n*   `REFERENCE`: String\n\n**Sample Values**:\n*   The `CAS_NO` column contains unique identifiers for chemicals.\n*   The `Source` column shows different databases or sources of information.\n*   The `Activity` column indicates whether a chemical is mutagenic (1) or not (0).\n*   `Canonical_Smiles` provides a structural representation of the chemicals.\n*   `REFERENCE` column contains long strings, likely bibliographic citations.\n\n**Additional Information**:\n*   The `__MACOSX` directory is a hidden system folder created by macOS and usually doesn't contain user data. I will ignore it.\n*   There is no `metadata.json` file in the directory as per the plan.\n\nI have analyzed the `Mutagenicity_N6512.csv` file. Since there is no `metadata.json` file, I cannot proceed with the original plan to analyze it. I will summarize the findings for the CSV file.\n\n**Summary of `Mutagenicity_N6512.csv`**:\n\nThe `Mutagenicity_N6512.csv` file is a dataset containing information about chemical substances and their mutagenicity. It includes a unique identifier for each chemical (`CAS_NO`), the data source (`Source`), a binary indicator of mutagenicity (`Activity`), and a string representation of the chemical's structure (`Canonical_Smiles`). The `Steroid` column seems to indicate that these are not steroids, and the `WDI` column is empty. Each record is associated with a `REFERENCE` which provides citation details. The `Activity` column is key for understanding mutagenic properties, with 1 indicating mutagenic and 0 indicating non-mutagenic.The directory `/private/var/folders/xm/j_y2w6zd2tnch_l4t705nly80000gn/T/scider_uploads/tmpg00nt1y2/extracted` contains two entries: `Mutagenicity_N6512.csv` and `__MACOSX`.\n\nI have analyzed the `Mutagenicity_N6512.csv` file.\n\n**Analysis of `Mutagenicity_N6512.csv`**:\n\n*   **File Format**: CSV\n*   **Content**: The file contains data related to chemical mutagenicity.\n*   **Columns**:\n    *   `CAS_NO`: Chemical Abstracts Service Registry Number (unique identifier for chemicals).\n    *   `Source`: Data source (e.g., CCRIS, VITIC).\n    *   `Activity`: Binary indicator of mutagenicity (0 for non-mutagenic, 1 for mutagenic).\n    *   `Steroid`: Appears to be -1, possibly indicating the absence of steroid properties.\n    *   `WDI`: This column appears to be empty in the provided sample.\n    *   `Canonical_Smiles`: SMILES (Simplified Molecular Input Line Entry System) strings representing the chemical structures.\n    *   `REFERENCE`: Citations or references for the data entries.\n*   **Data Types**:\n    *   `CAS_NO`: String\n    *   `Source`: String\n    *   `Activity`: Integer\n    *   `Steroid`: Integer\n    *   `WDI`: String (empty)\n    *   `Canonical_Smiles`: String\n    *   `REFERENCE`: String\n*   **Data Quality**: The `WDI` column seems unused. The `Steroid` column consistently shows -1, suggesting it might not be relevant for this dataset or indicates a specific category.\n*   **Potential Use**: This dataset could be used to train models that predict mutagenicity based on chemical structures or other properties.\n\nThe `__MACOSX` directory is a system directory created by macOS and is not relevant for data analysis.\n\nThere was no `metadata.json` file found in the directory. Therefore, I cannot proceed with analyzing it."
+        },
+        {
+          "node_name": "data/critic",
+          "output": "Here's a summary of the criticism:\n\n1.  **Limited File Inspection**: Only the first 50 lines of `Mutagenicity_N6512.csv` were read, potentially missing important data beyond this initial sample.\n2.  **Assumption without Verification**: The `__MACOSX` directory was dismissed without a brief confirmation of its contents or irrelevance.\n3.  **Plan Deviation without Adaptation**: The absence of `metadata.json` was noted, but the original plan was not revised, and no alternative methods for gathering metadata were explored.\n4.  **Superficial Data Analysis**: The analysis of `Mutagenicity_N6512.csv` could be more in-depth. Key areas for deeper analysis include:\n    *   Distribution of the `Activity` column.\n    *   Variety and frequency of `Source` entries.\n    *   Implications of the consistent `-1` in the `Steroid` column.\n    *   Confirmation of the `WDI` column's emptiness across the entire file.\n    *   Characteristics of `Canonical_Smiles` (e.g., complexity, uniqueness).\n    *   Redundancy or uniqueness of `REFERENCE` entries.\n5.  **Missed Synthesis Opportunity**: The absence of `metadata.json` prevented the planned synthesis of data characteristics and potential relationships. While this was due to missing data, the agent could have speculated on potential metadata or suggested ways to infer it."
+        },
+        {
+          "node_name": "data/finalize",
+          "output": "Finalization complete. Plans completed: 1, Remaining: 5"
+        },
+        {
+          "node_name": "data/generate_summary",
+          "output": "# Data Analysis Summary\n\n## 1. Overview\n\nThis analysis focused on exploring and understanding the content of data files located within the directory: `/private/var/folders/xm/j_y2w6zd2tnch_l4t705nly80000gn/T/scider_uploads/tmpg00nt1y2/extracted`. The primary objective was to read and analyze the actual data within these files, moving beyond just directory inspection. The expectation was to identify file formats, data structures, key features, and derive meaningful insights.\n\n## 2. Data Characteristics\n\nDuring the analysis, the following data file was identified and examined:\n\n*   **File:** `Mutagenicity_N6512.csv`\n    *   **Format:** Comma-Separated Values (CSV).\n    *   **Structure:** Tabular data with distinct columns.\n    *   **Columns Identified:**\n        *   `CAS_NO`: Chemical Abstracts Service Registry Number (String).\n        *   `Source`: Data source identifier (String, e.g., CCRIS, VITIC).\n        *   `Activity`: Numerical indicator of mutagenicity (Integer, 0 or 1).\n        *   `Steroid`: Integer, consistently observed as -1.\n        *   `WDI`: Appears to be an empty column (String).\n        *   `Canonical_Smiles`: SMILES notation representing chemical structures (String).\n        *   `REFERENCE`: Bibliographic citation or reference for the data entry (String).\n    *   **Data Types:** Primarily strings and integers, with SMILES strings representing complex chemical structures.\n\nNo `metadata.json` file was found in the provided directory, which limited the scope of analysis to the available CSV file.\n\n## 3. Key Findings\n\n*   **Mutagenicity Data:** The core of the `Mutagenicity_N6512.csv` file is information pertaining to the mutagenic activity of chemical compounds. The `Activity` column serves as a crucial binary classifier (0 for non-mutagenic, 1 for mutagenic).\n*   **Chemical Identification:** Each chemical is uniquely identified by its `CAS_NO`, and its structure is represented using `Canonical_Smiles`. This allows for the potential correlation of structural features with mutagenic activity.\n*   **Diverse Data Sources:** The `Source` column indicates that the data originates from multiple databases or sources, such as CCRIS and VITIC. This suggests a consolidation of information from different chemical and toxicological information systems.\n*   **Non-Steroidal Nature Indicated:** The `Steroid` column consistently contains the value -1. This strongly suggests that the chemicals in this dataset are not steroids, or this property is marked as absent/not applicable.\n*   **Potential for Predictive Modeling:** The dataset, with its chemical identifiers, structures, and mutagenicity labels, is well-suited for developing predictive models to assess the mutagenic potential of new chemical compounds based on their structural properties.\n\n## 4. Data Quality\n\n*   **Empty `WDI` Column:** The `WDI` column appears to be entirely empty across the observed data. This column might be a remnant from a previous dataset or a placeholder that was not populated.\n*   **Consistent `Steroid` Value:** The uniform presence of `-1` in the `Steroid` column indicates a lack of variation for this feature within the dataset, simplifying its interpretation but also limiting its analytical utility.\n*   **Referential Integrity:** The `REFERENCE` column contains detailed citations. Further analysis could involve checking for redundancy or variations in citation formatting.\n*   **Data Completeness:** While the initial inspection covered the first 50 lines, a full read of the file would be necessary to confirm the consistency of these observations (e.g., emptiness of `WDI`, value of `Steroid`) across the entire dataset.\n\n## 5. Recommendations\n\n*   **Full File Read:** Perform a complete read of `Mutagenicity_N6512.csv` to:\n    *   Confirm the consistency of data quality observations (e.g., empty `WDI` column, `-1` in `Steroid` column) throughout the entire file.\n    *   Analyze the distribution of the `Activity` column to understand the class balance (proportion of mutagenic vs. non-mutagenic compounds).\n    *   Investigate the variety and frequency of entries in the `Source` and `REFERENCE` columns.\n    *   Examine the `Canonical_Smiles` strings for structural complexity and potential patterns.\n*   **Data Cleaning:** Based on the full read, decide on a strategy for the `WDI` column (e.g., remove it).\n*   **Feature Engineering:** The `Canonical_Smiles` column is rich in information. Consider using cheminformatics tools to generate molecular descriptors from SMILES strings, which can serve as features for machine learning models.\n*   **Exploratory Data Analysis (EDA):** Conduct more in-depth EDA, potentially including visualizations, to explore relationships between `Canonical_Smiles` (or derived features) and `Activity`.\n*   **Contextual Information:** If possible, seek external information about the sources (CCRIS, VITIC) mentioned in the `Source` column to better understand the context and potential biases of the data."
+        },
+        {
+          "node_name": "data/prepare_for_talk_mode",
+          "output": "Memory consolidation skipped"
+        }
+      ]
+    }
+  ]
+}

streamlit-client/app.py

@@ -28,23 +28,56 @@ from scievo.workflows.full_workflow_with_ideation import FullWorkflowWithIdeatio
 
 st.set_page_config(page_title="SciDER Chat", layout="centered")
 
-st.markdown(
-    """
+# Theme selection - must be set before first render
+if "theme" not in st.session_state:
+    st.session_state.theme = "light"
+
+
+def get_theme_css(theme: str) -> str:
+    """Return CSS for the selected theme."""
+    if theme == "dark":
+        return """
+        <style>
+        [data-testid="stApp"], .stApp { background-color: #0e1117 !important; }
+        [data-testid="stAppViewContainer"] { background-color: #0e1117 !important; }
+        [data-testid="stVerticalBlock"], [data-testid="block-container"] { background-color: #0e1117 !important; }
+        [data-testid="stChatMessage"] { background-color: #1e1e1e !important; border: 1px solid #2d2d2d; }
+        h1, h2, h3, h4, h5, h6 { color: #fafafa !important; }
+        [data-testid="stChatMessage"] h1, [data-testid="stChatMessage"] h2,
+        [data-testid="stChatMessage"] h3, [data-testid="stChatMessage"] h4,
+        [data-testid="stChatMessage"] h5, [data-testid="stChatMessage"] h6 { color: inherit !important; }
+        [data-testid="stSidebar"] { background-color: #0e1117 !important; }
+        [data-testid="stExpander"], .stForm { background-color: #0e1117 !important; }
+        .stMarkdown, p, span, label, [data-testid="stMarkdown"], [data-testid="stCaptionContainer"],
+        [data-testid="stVerticalBlock"] { color: #e8e8e8 !important; }
+        [data-testid="stChatMessage"] .stMarkdown, [data-testid="stChatMessage"] p,
+        [data-testid="stChatMessage"] span { color: #e8e8e8 !important; }
+        input, textarea { color: #e8e8e8 !important; background-color: #1e1e1e !important; }
+        </style>
+        """
+    # Light theme (default)
+    return """
     <style>
-    /* App headers: title, subheader, form section titles - exclude chat message content */
-    h1, h2, h3, h4, h5, h6 {
-        color: #384166 !important;
-    }
-    /* Exclude LLM-generated content inside chat messages */
+    [data-testid="stApp"], .stApp { background-color: #ffffff !important; }
+    [data-testid="stAppViewContainer"] { background-color: #ffffff !important; }
+    [data-testid="stVerticalBlock"], [data-testid="block-container"] { background-color: #ffffff !important; }
+    [data-testid="stChatMessage"] { background-color: #f8f9fa !important; border: 1px solid #e9ecef; }
+    h1, h2, h3, h4, h5, h6 { color: #384166 !important; }
     [data-testid="stChatMessage"] h1, [data-testid="stChatMessage"] h2,
     [data-testid="stChatMessage"] h3, [data-testid="stChatMessage"] h4,
-    [data-testid="stChatMessage"] h5, [data-testid="stChatMessage"] h6 {
-        color: inherit !important;
-    }
+    [data-testid="stChatMessage"] h5, [data-testid="stChatMessage"] h6 { color: inherit !important; }
+    [data-testid="stSidebar"] { background-color: #ffffff !important; }
+    [data-testid="stExpander"], .stForm { background-color: #ffffff !important; }
+    .stMarkdown, p, span, label, [data-testid="stMarkdown"], [data-testid="stCaptionContainer"],
+    [data-testid="stVerticalBlock"] { color: #262730 !important; }
+    [data-testid="stChatMessage"] .stMarkdown, [data-testid="stChatMessage"] p,
+    [data-testid="stChatMessage"] span { color: #262730 !important; }
+    input, textarea { color: #262730 !important; background-color: #ffffff !important; }
     </style>
-    """,
-    unsafe_allow_html=True,
-)
+    """
+
+
+st.markdown(get_theme_css(st.session_state.theme), unsafe_allow_html=True)
 
 
 def register_all_models(user_api_key=None, user_model=None):
@@ -308,6 +341,52 @@ def cleanup_uploaded_data():
         st.session_state.workspace_path = st.session_state.default_workspace_path
 
 
+def get_case_study_memory_paths() -> list[Path]:
+    """Return list of case-study-memory directories to search (cwd and project root)."""
+    paths = []
+    cwd = Path.cwd()
+    app_dir = Path(__file__).parent
+    project_root = app_dir.parent
+    for p in [cwd / "case-study-memory", project_root / "case-study-memory", app_dir / "case-study-memory"]:
+        if p.exists() and p.is_dir() and p not in paths:
+            paths.append(p)
+    return paths
+
+
+def list_available_chat_files() -> list[tuple[Path, dict]]:
+    """List all chat_history.json files in case-study-memory, return (path, metadata_for_display)."""
+    results = []
+    seen = set()
+    for base in get_case_study_memory_paths():
+        dirs = [d for d in base.iterdir() if d.is_dir() and d.name.startswith("memo_")]
+        dirs.sort(key=lambda x: x.stat().st_mtime if x.exists() else 0, reverse=True)
+        for memo_dir in dirs:
+            chat_file = memo_dir / "chat_history.json"
+            if chat_file.exists():
+                try:
+                    with open(chat_file, encoding="utf-8") as f:
+                        data = json.load(f)
+                    ts = data.get("timestamp", "")[:19].replace("T", " ")
+                    wf = data.get("workflow_type", "unknown")
+                    meta = data.get("metadata", {})
+                    q = meta.get("query") or ""
+                    query = (q[:57] + "...") if len(q) > 60 else q
+                    key = str(chat_file.resolve())
+                    if key not in seen:
+                        seen.add(key)
+                        results.append((chat_file, {"label": f"{memo_dir.name} | {ts} | {wf} | {query}", "timestamp": ts, "workflow_type": wf}))
+                except Exception:
+                    results.append((chat_file, {"label": f"{memo_dir.name} | (parse error)", "timestamp": "", "workflow_type": "unknown"}))
+    return results
+
+
+def load_chat_from_file(chat_path: Path) -> list[dict]:
+    """Load messages from chat_history.json. Returns list of message dicts."""
+    with open(chat_path, encoding="utf-8") as f:
+        data = json.load(f)
+    return data.get("messages", [])
+
+
 def get_next_memo_number(memory_dir: Path) -> int:
     if not memory_dir.exists():
         return 1
@@ -362,12 +441,59 @@ if "default_model" not in st.session_state:
     st.session_state.default_model = os.getenv(
         "SCIEVO_DEFAULT_MODEL", "gemini/gemini-2.5-flash-lite"
     )
+if "view_mode" not in st.session_state:
+    st.session_state.view_mode = "live"  # "live" | "case_study"
+
+# Case Study mode: no API key required, load from case-study-memory, same UI as live
+if st.session_state.view_mode == "case_study":
+    # Same header as live mode
+    col_title, col_theme, col_reset = st.columns([4, 1, 1])
+    with col_title:
+        st.title("SciDER Research Assistant")
+    with col_theme:
+        theme_options = {"☀️ Light": "light", "🌙 Dark": "dark"}
+        current_label = "☀️ Light" if st.session_state.theme == "light" else "🌙 Dark"
+        new_label = st.selectbox("Theme", options=list(theme_options.keys()), index=list(theme_options.keys()).index(current_label), key="theme_selector_casestudy", label_visibility="collapsed", help="Select light or dark theme")
+        new_theme = theme_options[new_label]
+        if new_theme != st.session_state.theme:
+            st.session_state.theme = new_theme
+            st.rerun()
+    with col_reset:
+        if st.button("← Live", help="Back to live chat", key="back_to_live"):
+            st.session_state.view_mode = "live"
+            st.rerun()
+
+    available = list_available_chat_files()
+    if not available:
+        st.info("No case studies found in case-study-memory. Run a workflow first to save conversations.")
+        st.stop()
+
+    options = [m["label"] for _, m in available]
+    paths = [p for p, _ in available]
+    idx = st.selectbox("Select case study", range(len(options)), format_func=lambda i: options[i], key="casestudy_select")
+    selected_path = paths[idx]
+    loaded = load_chat_from_file(selected_path)
+    st.divider()
 
+    for m in loaded:
+        with st.chat_message(m["role"]):
+            st.markdown(m["content"])
+            if m.get("intermediate_state"):
+                render_intermediate_state(m["intermediate_state"])
+    st.stop()
+
+# Live mode: require API key
 if not st.session_state.api_key:
     st.title("SciDER Research Assistant")
     st.warning("API Key Required")
     st.markdown("Please provide API keys to use the SciDER Research Assistant.")
 
+    st.divider()
+    if st.button("📂 Case Study", help="Browse saved chat history (no API key required)", key="casestudy_from_login"):
+        st.session_state.view_mode = "case_study"
+        st.rerun()
+    st.divider()
+
     # Model provider selection
     col1, col2 = st.columns(2)
     with col1:
@@ -414,9 +540,24 @@ if not st.session_state.api_key:
             st.error("Please enter a valid API key for the selected model provider")
     st.stop()
 
-col_title, col_reset = st.columns([5, 1])
+col_title, col_theme, col_reset = st.columns([4, 1, 1])
 with col_title:
     st.title("SciDER Research Assistant")
+with col_theme:
+    theme_options = {"☀️ Light": "light", "🌙 Dark": "dark"}
+    current_label = "☀️ Light" if st.session_state.theme == "light" else "🌙 Dark"
+    new_label = st.selectbox(
+        "Theme",
+        options=list(theme_options.keys()),
+        index=list(theme_options.keys()).index(current_label),
+        key="theme_selector",
+        label_visibility="collapsed",
+        help="Select light or dark theme",
+    )
+    new_theme = theme_options[new_label]
+    if new_theme != st.session_state.theme:
+        st.session_state.theme = new_theme
+        st.rerun()
 with col_reset:
     if st.button("🔄 Reset", help="Clear all chat history", type="secondary"):
         cleanup_uploaded_data()