Create app.py

app.py

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+from fastapi import FastAPI, Request
+from huggingface_hub import InferenceClient
+import json
+
+app = FastAPI()
+
+HF_API_KEY = os.getenv("HF_API_KEY")  # 🔥 No Hardcoded Keys!
+
+# ✅ Set up Hugging Face Inference Client
+client = InferenceClient(
+    provider="hf-inference",
+    api_key=HF_API_KEY  # Use the key from environment
+)
+
+# ✅ Tag descriptions (helps model understand each tag's meaning)
+TAGS_DESCRIPTION = """
+IPWhitelisting: Adding IP addresses into Okta Security Networks fields if blocked.
+AppLocker: Used when adding hashes to allow execution of applications (AppLocker or CrowdStrike).
+ADSecurityGroup: Removing a user from an AD Security group to stop or allow access.
+AttachmentRelease: Releasing an attachment if password protected.
+EmailWhitelisting: Allowing emails through ProofPoint or Checkpoint for delivery.
+AppAssignment: Adding a user to an Application.
+chatops:mfa-bypass: Automates MFA AD group changes or Requests from the bypass MFA.
+VM: Removing hosts from Nessus or decommissioning hosts from tracking in nessus and tenable or All requests for tenable and nessus.
+PhishingReport: When a user submits a phishing report or Email investigation or Email Analysis.
+GenericInformation: Generic tickets assigned to SecOps.
+PasswordReset: When a ticket needs Okta or AD password reset.
+KeeperAccounts: Issues with Keeper Password Manager accounts.
+MemberIssues: Investigating possible security issues with Member accounts.
+ADPassword: AD password issues, including resets.
+OktaMFAResets: Okta MFA resets performed by Security or Requests for MFA reset.
+Zscaler: Issues related to Zscaler security or Domain/URL allow requests or Domain/URL block requests.
+chatops:cs-usb: Automates individual USB whitelisting.
+USBDeviceControl: Requests for workstation USB whitelisting.
+Imperva: Issues related to Imperva and CDN security.
+"""
+
+
+# ✅ Function to summarize & assign a single tag
+def analyze_ticket(title, body):
+    messages = [
+        {"role": "system",
+         "content": "You are an AI that summarizes security tickets and assigns a single most relevant tag based on "
+                    "the title and body."},
+        {"role": "user", "content": f"Title: {title}\nBody: {body}\n\n"
+                                    "### TASK 1: Summarization\nSummarize the ticket body in 1-2 sentences.\n\n"
+                                    "### TASK 2: Tagging\nChoose the **one best tag** from this list that matches the "
+                                    "ticket:\n "
+                                    f"{TAGS_DESCRIPTION}\n\n"
+                                    "Return output in JSON format with keys: 'summary' (ticket summary) and 'tag' ("
+                                    "best matching tag)."}
+    ]
+
+    completion = client.chat.completions.create(
+        model="mistralai/Mistral-7B-Instruct-v0.3",
+        messages=messages,
+        max_tokens=200
+    )
+
+    # ✅ Parse JSON response
+    try:
+        response = json.loads(completion.choices[0].message.content.strip())
+    except json.JSONDecodeError:
+        response = {"summary": "Could not generate summary", "tag": "Unknown"}
+
+    return response
+
+
+# ✅ API Endpoint to accept input from Tines
+@app.post("/process-ticket/")
+async def process_ticket(request: Request):
+    data = await request.json()
+
+    # ✅ Extract input fields
+    ticket_id = data.get("id", "No ID")
+    title = data.get("title", "No Title")
+    body = data.get("body", "No Body")
+
+    # ✅ Get AI-generated summary & tag
+    result = analyze_ticket(title, body)
+
+    # ✅ Return response including ID
+    return {
+        "id": ticket_id,  # ✅ Keep original ID
+        "summary": result["summary"],
+        "tag": result["tag"]
+    }
+
+# def process_input_file(filename):
+#     try:
+#         with open(filename, "r", encoding="utf-8") as file:
+#             data = json.load(file)  # ✅ Load JSON file
+#
+#         title = data.get("title", "No Title")
+#         body = data.get("body", "No Body")
+#
+#         # ✅ Get AI-generated summary & tag
+#         result = analyze_ticket(title, body)
+#
+#         return result  # ✅ Returns {'summary': "...", 'tag': "..."}
+#
+#     except FileNotFoundError:
+#         print(f"❌ Error: The file '{filename}' was not found.")
+#         return {"error": "File not found"}
+#
+#     except json.JSONDecodeError:
+#         print(f"❌ Error: The file '{filename}' contains invalid JSON.")
+#         return {"error": "Invalid JSON format"}
+#
+#
+# # ✅ Load input from `input.json`
+# output = process_input_file("input.json")
+#
+# # ✅ Print the output
+# print(json.dumps(output, indent=2))