Fixed app v2

app.py

@@ -1,4 +1,4 @@
-from fastapi import FastAPI, HTTPException, UploadFile, File
+from fastapi import FastAPI, HTTPException
 from pydantic import BaseModel
 from llama_cpp import Llama
 
@@ -14,48 +14,39 @@ 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 with file upload
+# Define the route for security log analysis
 @app.post("/analyze_security_logs", response_model=AnalysisResponse)
-async def analyze_security_logs(file: UploadFile = File(...)):
+async def analyze_security_logs(request: LogRequest):
     try:
-        # 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)]
+        # 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}"
+        )
         
-        # 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"
+        # Generate response from the model
+        response = llm.create_chat_completion(
+            messages=[
+                {
+                    "role": "user",
+                    "content": prompt
+                }
+            ]
+        )
         
-        return AnalysisResponse(analysis=full_analysis)
+        # Extract and return the analysis text
+        analysis_text = response["choices"][0]["message"]["content"]
+        return AnalysisResponse(analysis=analysis_text)
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))