Fixed app v2

app.py

@@ -1,4 +1,4 @@
-from fastapi import FastAPI, HTTPException
+from fastapi import FastAPI, HTTPException, UploadFile, File
 from pydantic import BaseModel
 from llama_cpp import Llama
 
@@ -14,39 +14,48 @@ try:
 except Exception as e:
     raise RuntimeError(f"Failed to load model: {e}")
 
-# Define request model for log data
-class LogRequest(BaseModel):
-    log_data: str
-
 # Define response model
 class AnalysisResponse(BaseModel):
     analysis: str
 
-# Define the route for security log analysis
+# Define the route for security log analysis with file upload
 @app.post("/analyze_security_logs", response_model=AnalysisResponse)
-async def analyze_security_logs(request: LogRequest):
+async def analyze_security_logs(file: UploadFile = File(...)):
     try:
-        # Security-focused prompt
-        prompt = (
-            "Analyze the following network log data for any indicators of malicious activity, "
-            "such as unusual IP addresses, unauthorized access attempts, data exfiltration, or anomalies. "
-            "Provide details on potential threats, IPs involved, and suggest actions if any threats are detected.\n\n"
-            f"{request.log_data}"
-        )
+        # Read the content of the uploaded file
+        log_data = await file.read()
+        log_data = log_data.decode("utf-8")
+        
+        # Set maximum tokens for each chunk
+        max_chunk_size = 512  # Adjust this based on model's token limit
+        log_chunks = [log_data[i:i+max_chunk_size] for i in range(0, len(log_data), max_chunk_size)]
         
-        # Generate response from the model
-        response = llm.create_chat_completion(
-            messages=[
-                {
-                    "role": "user",
-                    "content": prompt
-                }
-            ]
-        )
+        # Accumulate the results
+        full_analysis = ""
+        for chunk in log_chunks:
+            # Security-focused prompt with each chunk
+            prompt = (
+                "Analyze the following network log data for any indicators of malicious activity, "
+                "such as unusual IP addresses, unauthorized access attempts, data exfiltration, or anomalies. "
+                "Provide details on potential threats, IPs involved, and suggest actions if any threats are detected.\n\n"
+                f"{chunk}"
+            )
+            
+            # Generate response from the model
+            response = llm.create_chat_completion(
+                messages=[
+                    {
+                        "role": "user",
+                        "content": prompt
+                    }
+                ]
+            )
+            
+            # Extract and accumulate analysis text
+            analysis_text = response["choices"][0]["message"]["content"]
+            full_analysis += analysis_text + "\n"
         
-        # Extract and return the analysis text
-        analysis_text = response["choices"][0]["message"]["content"]
-        return AnalysisResponse(analysis=analysis_text)
+        return AnalysisResponse(analysis=full_analysis)
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))