Added Streamlit app code and requirements file

HF_embed_mitre_streamlit.py

@@ -0,0 +1,102 @@
+import streamlit as st
+import requests
+import csv
+from io import StringIO
+
+# Required NetFlow schema
+required_columns = [
+    'Flow duration', 'Source port', 'Destination port',
+    'Total forward packets', 'Total backward packets',
+    'Avg forward segment size', 'Avg backward segment size'
+]
+
+# Streamlit UI
+st.title("NetFlow Log Comparison Tool")
+st.write("Compare your NetFlow logs against Sigma rules or MITRE ATT&CK patterns using RAG.")
+
+# Instructions for data upload
+st.markdown("""
+**Instructions:**
+- Upload a CSV file with your NetFlow log data.
+- Ensure that the file contains **all the required columns** listed below.
+- You can upload **up to 5 rows** for analysis.
+""")
+
+# Display required schema for users
+st.write("### Required NetFlow Schema:")
+st.write(", ".join(required_columns))
+
+# Step 1: File Upload
+uploaded_file = st.file_uploader("Upload your NetFlow log sequence CSV file", type="csv")
+
+# Step 2: User Token Input
+hugging_face_api_token = st.text_input("Enter your Hugging Face API Token", type="password")
+if not hugging_face_api_token:
+    st.warning("Please provide a Hugging Face API Token to proceed.")
+
+# Step 3: Run Comparison if File Uploaded and Token Provided
+if uploaded_file and hugging_face_api_token:
+    # Read and display the file using CSV module
+    csv_file = StringIO(uploaded_file.getvalue().decode("utf-8"))
+    reader = csv.DictReader(csv_file)
+    csv_data = list(reader)
+
+    # Display a few rows to the user
+    st.write("Uploaded File:")
+    for i, row in enumerate(csv_data[:5]):
+        st.write(row)
+
+    # Check if the file has the required schema
+    if all(col in reader.fieldnames for col in required_columns):
+        if len(csv_data) <= 5:
+            st.success("File contains all required columns and meets the row limit of 5.")
+            
+            # Prepare data for Hugging Face API call
+            input_texts = [f"{row}" for row in csv_data]  # Convert each row to a string for comparison
+            
+            # Step 4: Call Hugging Face API
+            HUGGING_FACE_API_URL = "https://api-inference.huggingface.co/models/sentence-transformers/all-distilroberta-v1"
+            headers = {"Authorization": f"Bearer {hugging_face_api_token}"}
+
+            try:
+                # Perform inference using Hugging Face API
+                response = requests.post(HUGGING_FACE_API_URL, headers=headers, json={"inputs": input_texts})
+                response.raise_for_status()
+
+                # Display the results
+                st.write("### Comparison Results")
+                comparison_results = response.json()
+                st.write(comparison_results)
+
+            except requests.exceptions.RequestException as e:
+                st.error(f"Error calling Hugging Face API: {str(e)}")
+
+        else:
+            st.error(f"File exceeds the row limit of 5. Your file contains {len(csv_data)} rows.")
+    else:
+        missing_columns = [col for col in required_columns if col not in reader.fieldnames]
+        st.error(f"Missing columns: {', '.join(missing_columns)}")
+
+# Step 5: Survey Link
+st.write("### Feedback Survey")
+st.write("We value your feedback. [Fill out our survey](https://docs.google.com/forms/d/1-P_7Uv5OphSWhTyoPuO0jjUQnYg_Hv5oVGBkhbg-H8g/prefill)")  # Replace with your survey link
+
+# Footer
+st.markdown("---")
+st.write("This free site is maintained by DeepTempo.")
+st.image(".streamlit/Final DeepTempo logo.png", width=300)  # Adjust the path and width as needed
+st.write("[Visit DeepTempo.ai](https://deeptempo.ai)")
+st.write("[Check out the underlying code on GitHub](https://github.com/deepsecoss)")
+
+# CSS to change link color to white
+st.markdown(
+    """
+    <style>
+    a {
+        color: white !important;
+        text-decoration: underline; /* Optional: to keep the link recognizable */
+    }
+    </style>
+    """,
+    unsafe_allow_html=True
+)

requirements.txt

@@ -0,0 +1,3 @@
+streamlit==1.25.0
+boto3
+requests