Revert config and metadata for TemplateA redeploy

Dockerfile

@@ -23,7 +23,7 @@ COPY requirements.txt /app/requirements.txt
 RUN pip3 install --no-cache-dir -r /app/requirements.txt huggingface_hub
 
 # App code (TemplateA subtree root)
-COPY app.py /app/app.py
+COPY streamlit_app.py /app/streamlit_app.py
 COPY modules/ /app/modules/
 
 # Canonical code (you rsync these into TemplateA before subtree push)
@@ -45,4 +45,4 @@ RUN echo "[browser]\ngatherUsageStats = false" > /tmp/.streamlit/config.toml
 # Path where your downloader stores the model
 ENV MODEL_PATH=/tmp/models/TinyLlama-1.1B-Chat-v1.0.Q4_K_M.gguf
 
-ENTRYPOINT ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+ENTRYPOINT ["streamlit", "run", "streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]

README.md

@@ -1,5 +1,5 @@
 ---
-title: testv01
+title: TemplateA
 emoji: 🚀
 colorFrom: red
 colorTo: red
@@ -11,6 +11,6 @@ secrets:
 tags:
 - streamlit
 pinned: false
-short_description: Docker/Streamlit template A test env
+short_description: Docker/Streamlit template A baseline environment
 license: apache-2.0
 ---

app.py

@@ -1,102 +0,0 @@
-import os
-import subprocess
-import sys
-import streamlit as st
-
-# Ensure /app (root) is in path so /app/utils/ is importable as utils.*
-sys.path.append(os.path.dirname(os.path.abspath(__file__)))
-# Ensure /app is in sys.path so we can import utils.* from anywhere
-sys.path.insert(0, os.path.abspath(os.path.dirname(__file__)))
-# Environment setup
-os.environ["MODEL_PATH"] = "/tmp/models/tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf"
-os.environ["STREAMLIT_HOME"] = "/tmp/.streamlit"
-os.environ["XDG_CONFIG_HOME"] = "/tmp/.streamlit"
-os.environ["BROWSER_GATHER_USAGE_STATS"] = "false"
-os.environ["HF_HUB_CACHE"] = "/tmp/hf_cache"
-
-# Create required directories
-os.makedirs("/tmp/.streamlit", exist_ok=True)
-os.makedirs("/tmp/hf_cache", exist_ok=True)
-os.makedirs("/tmp/models", exist_ok=True)
-
-# Runtime model download if needed
-
-MODEL_PATH = "/tmp/models/TinyLlama-1.1B-Chat-v1.0.Q4_K_M.gguf"
-if not os.path.exists(MODEL_PATH):
-    st.warning("Model not found. Downloading...")
-    try:
-        subprocess.run(["python3", "model/download_model.py"], check=True, capture_output=True)
-        st.success("Model downloaded successfully.")
-    except subprocess.CalledProcessError as e:
-        st.error("Model download failed. Check HF_TOKEN or permissions.")
-        st.text(f"Exit code: {e.returncode}")
-        st.text(f"Command: {e.cmd}")
-        st.text(f"Output: {e.output if hasattr(e, 'output') else 'N/A'}")
-        st.stop()
-
-
-# Add local subdirectories to Python path
-sys.path.append(os.path.join(os.path.dirname(__file__), "modules"))
-sys.path.append(os.path.join(os.path.dirname(__file__), "model"))
-sys.path.append(os.path.join(os.path.dirname(__file__), "utils"))
-
-# Lab imports
-from modules import (
-    prompt_injection_2025v1,
-    insecure_output_handling_2025v1,
-    training_data_poisoning_2025v1,
-    sensitive_information_disclosure_2025v1
-)
-
-import sys
-print("✅ prompt_injection_2025v1 loaded", file=sys.stderr)
-print("✅ insecure_output_handling_2025v1 loaded", file=sys.stderr)
-print("✅ training_data_poisoning_2025v1 loaded", file=sys.stderr)
-print("✅ sensitive_information_disclosure_2025v1 loaded", file=sys.stderr)
-
-# Streamlit UI setup
-st.set_page_config(
-    page_title="LLM Security Labs",
-    layout="wide",
-    initial_sidebar_state="expanded"
-)
-
-# Map Streamlit URL paths to lab modules
-query_params = st.experimental_get_query_params()
-lab_key = query_params.get("lab", [None])[0]
-
-lab_map = {
-    "prompt-injection": prompt_injection_2025v1,
-    "insecure-output-handling": insecure_output_handling_2025v1,
-    "training-data-poisoning": training_data_poisoning_2025v1,
-    "sensitive-information-disclosure": sensitive_information_disclosure_2025v1
-}
-
-# Routing
-if lab_key in lab_map:
-    st.title(f"🧪 LLM Security Lab – {lab_key.replace('-', ' ').title()} (2025v1)")
-    lab_map[lab_key].run()
-else:
-    st.title("🧪 LLM Security Labs – OWASP-Inspired Threat Scenarios")
-    st.markdown("""
-    This is the landing page for the LLM security labs. Each lab demonstrates a known class of risk aligned with the evolving OWASP LLM Top 10.
-
-    Access a lab directly via one of the following URLs:
-
-    #- `/app?lab=prompt-injection`
-    #- `/app?lab=insecure-output-handling`
-    #- `/app?lab=training-data-poisoning`
-    #- `/app?lab=sensitive-information-disclosure`
-                
-    - [Prompt Injection](?lab=prompt-injection)
-    - [Insecure Output Handling (coming soon)](#)
-    - [Training Data Poisoning (coming soon)](#)
-    - [Sensitive Information Disclosure (coming soon)](#)
-
-    Each lab includes:
-    - **Realistic model interaction**
-    - **Risk scoring and feedback**
-    - **Detailed logging**
-    - **Optional RAG integration** where applicable
-    """)
-    st.markdown("Built using Zephyr-7B + llama.cpp")