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Create a web app called 'AI Phenomenology Tester' using Python backend with Streamlit. Include: - User input for selecting an HF model (e.g., Llama-2 via Transformers). - An agentic workflow: Use a simple AI agent (like LangChain or HF Agents) to plan and run tests. - Phenomenology test: Prompt the model with scenarios (e.g., 'What is consciousness?') and analyze emergent behaviors (e.g., entropy or pattern matching). - Eigenmode test: Extract activations, compute eigenvalues/eigenvectors with SciPy, visualize dominant modes (e.g., for physical signals like vibrations). - Dashboard to display results, charts (Matplotlib), and export options. - Integrate Hugging Face API for model loading. Make it deployable on HF Spaces.

b59fe0b verified 10 days ago

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Create a web app called 'AI Phenomenology Tester' using Python backend with Streamlit. Include: - User input for selecting an HF model (e.g., Llama-2 via Transformers). - An agentic workflow: Use a simple AI agent (like LangChain or HF Agents) to plan and run tests. - Phenomenology test: Prompt the model with scenarios (e.g., 'What is consciousness?') and analyze emergent behaviors (e.g., entropy or pattern matching). - Eigenmode test: Extract activations, compute eigenvalues/eigenvectors with SciPy, visualize dominant modes (e.g., for physical signals like vibrations). - Dashboard to display results, charts (Matplotlib), and export options. - Integrate Hugging Face API for model loading. Make it deployable on HF Spaces. 10 days ago
.gitattributes
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initial commit 10 days ago
README.md
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Create a web app called 'AI Phenomenology Tester' using Python backend with Streamlit. Include: - User input for selecting an HF model (e.g., Llama-2 via Transformers). - An agentic workflow: Use a simple AI agent (like LangChain or HF Agents) to plan and run tests. - Phenomenology test: Prompt the model with scenarios (e.g., 'What is consciousness?') and analyze emergent behaviors (e.g., entropy or pattern matching). - Eigenmode test: Extract activations, compute eigenvalues/eigenvectors with SciPy, visualize dominant modes (e.g., for physical signals like vibrations). - Dashboard to display results, charts (Matplotlib), and export options. - Integrate Hugging Face API for model loading. Make it deployable on HF Spaces. 10 days ago
index.html
10.5 kB

Create a web app called 'AI Phenomenology Tester' using Python backend with Streamlit. Include: - User input for selecting an HF model (e.g., Llama-2 via Transformers). - An agentic workflow: Use a simple AI agent (like LangChain or HF Agents) to plan and run tests. - Phenomenology test: Prompt the model with scenarios (e.g., 'What is consciousness?') and analyze emergent behaviors (e.g., entropy or pattern matching). - Eigenmode test: Extract activations, compute eigenvalues/eigenvectors with SciPy, visualize dominant modes (e.g., for physical signals like vibrations). - Dashboard to display results, charts (Matplotlib), and export options. - Integrate Hugging Face API for model loading. Make it deployable on HF Spaces. 10 days ago
script.js
743 Bytes

Create a web app called 'AI Phenomenology Tester' using Python backend with Streamlit. Include: - User input for selecting an HF model (e.g., Llama-2 via Transformers). - An agentic workflow: Use a simple AI agent (like LangChain or HF Agents) to plan and run tests. - Phenomenology test: Prompt the model with scenarios (e.g., 'What is consciousness?') and analyze emergent behaviors (e.g., entropy or pattern matching). - Eigenmode test: Extract activations, compute eigenvalues/eigenvectors with SciPy, visualize dominant modes (e.g., for physical signals like vibrations). - Dashboard to display results, charts (Matplotlib), and export options. - Integrate Hugging Face API for model loading. Make it deployable on HF Spaces. 10 days ago
style.css
805 Bytes

Create a web app called 'AI Phenomenology Tester' using Python backend with Streamlit. Include: - User input for selecting an HF model (e.g., Llama-2 via Transformers). - An agentic workflow: Use a simple AI agent (like LangChain or HF Agents) to plan and run tests. - Phenomenology test: Prompt the model with scenarios (e.g., 'What is consciousness?') and analyze emergent behaviors (e.g., entropy or pattern matching). - Eigenmode test: Extract activations, compute eigenvalues/eigenvectors with SciPy, visualize dominant modes (e.g., for physical signals like vibrations). - Dashboard to display results, charts (Matplotlib), and export options. - Integrate Hugging Face API for model loading. Make it deployable on HF Spaces. 10 days ago