Update modules/analysis.py

modules/analysis.py

@@ -1,8 +1,65 @@
 from langchain.chains import RetrievalQA
-from langchain_community.llms import HuggingFacePipeline  # ✅ Updated import
+from langchain_community.llms import HuggingFacePipeline
 from transformers import pipeline
 from modules import parser, vectorizer
+from datetime import datetime
+import re
+
+def filter_logs_by_time(logs_text, start_time, end_time):
+    """
+    Filters log lines based on timestamp range.
+    """
+    if not start_time or not end_time:
+        return logs_text  # Skip filtering if not both are set
+
+    start = datetime.fromisoformat(str(start_time))
+    end = datetime.fromisoformat(str(end_time))
+
+    filtered_lines = []
+    for line in logs_text.splitlines():
+        match = re.match(r"(\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2})", line)
+        if match:
+            timestamp = datetime.strptime(match.group(1), "%Y-%m-%d %H:%M:%S")
+            if start <= timestamp <= end:
+                filtered_lines.append(line)
+    return "\n".join(filtered_lines)
 
 def run_analysis(uploaded_files, text_input, query, quick_action, temperature, start_time, end_time):
-    print("🔥 run_analysis called")  # Will appear in Application Logs
-    return "Function was triggered!", None, None, [("INFO", "Test log processed")]
+    logs_text = ""
+
+    # Combine uploaded + pasted logs
+    if uploaded_files:
+        logs_text += parser.parse_uploaded_files(uploaded_files)
+    if text_input:
+        logs_text += "\n" + text_input
+
+    if not logs_text.strip():
+        return "❌ No logs provided.", None, None, None
+
+    # Filter logs based on time range (if provided)
+    logs_text = filter_logs_by_time(logs_text, start_time, end_time)
+
+    # Use either typed query or quick action
+    query_text = query.strip() if query else ""
+    if not query_text and quick_action:
+        query_text = quick_action
+    if not query_text:
+        return "❌ No query or quick action selected.", None, None, None
+
+    # Process logs
+    docs = vectorizer.prepare_documents(logs_text)
+    vectordb = vectorizer.create_vectorstore(docs)
+
+    pipe = pipeline("text-generation", model="gpt2", max_length=512, temperature=temperature)
+    llm = HuggingFacePipeline(pipeline=pipe)
+
+    qa = RetrievalQA.from_chain_type(llm=llm, retriever=vectordb.as_retriever())
+    result = qa.run(query_text)
+
+    # Dummy charts and alerts for testing
+    bar_data = {"Hour": ["14:00", "15:00"], "Count": [8, 4]}
+    pie_data = {"Event Type": ["Blocked", "Scan"], "Count": [8, 4]}
+    alerts = [("CRITICAL", "8 blocked SSH attempts from 192.168.1.5"),
+              ("WARNING", "4 port scanning alerts from 10.0.0.8")]
+
+    return result, bar_data, pie_data, alerts