Fixed app v2

app.py

@@ -1,51 +1,48 @@
-import openai
+from fastapi import FastAPI, HTTPException, UploadFile, File
+from llama_cpp import Llama
 
-# Set up OpenAI API key
-openai.api_key = "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"
+# Initialize FastAPI app
+app = FastAPI()
 
-# Function to read and process log files
-def read_log_file(file_path):
-    with open(file_path, 'r') as file:
-        log_data = file.read()
-    return log_data
-
-# Function to analyze log data for malicious activity using OpenAI
-def analyze_logs_for_malicious_activity(log_data):
-    # Instruction prompt to guide the model
-    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"{log_data}"
-    )
-    
-    # Send request to OpenAI API
-    response = openai.Completion.create(
-        engine="gpt-3.5-turbo",  # Ensure to use a suitable model for instructions
-        prompt=prompt,
-        max_tokens=500,
-        temperature=0.5
+# Load the Llama model
+try:
+    llm = Llama.from_pretrained(
+        repo_id="QuantFactory/Lily-Cybersecurity-7B-v0.2-GGUF",
+        filename="Lily-Cybersecurity-7B-v0.2.Q3_K_S.gguf",
     )
-    
-    # Extract response text
-    analysis = response.choices[0].text.strip()
-    return analysis
+except Exception as e:
+    raise RuntimeError(f"Failed to load model: {e}")
+
+# Define the route for security log analysis with file upload
+@app.post("/analyze_security_logs")
+async def analyze_security_logs(file: UploadFile = File(...)):
+    try:
+        # Read the content of the uploaded file
+        log_data = await file.read()
+        log_data = log_data.decode("utf-8")
 
-# Main function to execute log analysis
-def main():
-    # Path to your network log file
-    log_file_path = "log.txt"
-    
-    # Read log data
-    log_data = read_log_file(log_file_path)
-    
-    # Analyze log data
-    analysis = analyze_logs_for_malicious_activity(log_data)
-    
-    # Print or save analysis result
-    print("Analysis of Network Logs for Malicious Activity:\n")
-    print(analysis)
+        # 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"{log_data}"
+        )
+        
+        # Generate response from the model
+        response = llm.create_chat_completion(
+            messages=[
+                {
+                    "role": "user",
+                    "content": prompt
+                }
+            ]
+        )
+        
+        # Extract and return the analysis text
+        analysis_text = response["choices"][0]["message"]["content"]
+        return {"analysis": analysis_text}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
 
-# Run the main function
-if __name__ == "__main__":
-    main()
+# To run the app, use: uvicorn app:app --reload

requirements.txt

@@ -7,4 +7,5 @@ torchvision
 pydantic
 sentencepiece
 accelerate>=0.26.0
-gradio
+gradio
+git+https://github.com/abetlen/llama-cpp-python.git