Add cost estimation functionality with AI analysis for Infrastructures

cost_estimate.py

@@ -0,0 +1,92 @@
+import os
+import sys
+import json
+import subprocess
+
+from dotenv import load_dotenv
+from groq import Groq
+
+GREEN = '\033[92m'
+YELLOW = '\033[93m'
+RED = '\033[91m'
+CYAN = '\033[96m'
+RESET = '\033[0m'
+BOLD = '\033[1m'
+
+load_dotenv()
+
+GROQ_API_KEY = os.environ.get("GROQ_API_KEY")
+
+if not GROQ_API_KEY:
+    print("ERROR: No GRoQ API key found.")
+    sys.exit(1)
+
+client = Groq(api_key=GROQ_API_KEY)
+
+def run_infracost(dir):
+    print(f"\n{CYAN}Calling infracost to calculate costs...{RESET}")
+    try:
+        result = subprocess.run(["infracost", "breakdown", "--path", dir, "--format", "json"], capture_output=True, text=True, check=True)
+        return json.loads(result.stdout)
+    except FileNotFoundError:
+        print(f"{RED}ERROR: Infracost is not installed, or not found in PATH.{RESET}")
+        print("Run: 'winget install infracost' and after 'infracost auth login")
+        sys.exit(1)
+    except subprocess.CalledProcessError as e:
+        print(f"{RED}Error running Infracost:{RESET}\n{e.stderr}")
+        sys.exit(1)
+
+def finops_ai_analysis(client, infracost_data):
+    total_cost = infracost_data.get("totalMonthlyCost", "0.00")
+
+    resource_summary = []
+    for project in infracost_data.get("projects", []):
+        for resource in project.get("breakdown", {}).get("resources", []):
+            resource_summary.append({
+                "resource": resource.get("name"),
+                "montly_cost": resource.get("monthlyCost")
+            })
+
+    print(f"{YELLOW}Calling AI for analysis...{RESET}")
+
+    prompt = f"""Act as a Senior Cloud FinOps Expert for Microsoft Azure.
+        I have a Terraform infrastructure with an exact estimated total monthly cost of ${total_cost}.
+        Here is the breakdown by resource:
+        {json.dumps(resource_summary, indent=2)}
+
+        Please provide:
+        1. A concise analysis of where the budget is going.
+        2. Top 3 actionable recommendations to optimize and reduce this cost in Azure (e.g., Reserved Instances, auto-scaling, cheaper SKUs).
+
+        Keep it professional, objective and well-formatted.
+        """
+
+    response = client.chat.completions.create(
+        model="llama-3.3-70b-versatile",
+        messages=[{"role": "user", "content": prompt}],
+        temperature=0.1
+    )
+
+    return total_cost, response.choices[0].message.content
+
+def main():
+    print(f"\n{BOLD}Cost Estimate Funcionality{RESET}")
+
+    dir = input("Enter the directory path of your Terraform code [./iac_output]: ") or "./iac_output"
+
+    if not os.path.exists(dir):
+        print(f"{RED}ERROR: Directory '{dir}' does not exist or was not found.{RESET}")
+        sys.exit(1)
+
+    infracost_data = run_infracost(dir)
+    total_cost, ai_analysis = finops_ai_analysis(client, infracost_data)
+
+    print("\n" + "="*60)
+    print(f"Estimated Total Cost:{BOLD}{GREEN}{total_cost} / month{RESET}")
+    print("="*60)
+    print(f"\n{BOLD}AI analysis and saving opportunities:{RESET}\n")
+    print(ai_analysis)
+    print("="*60 + "\n")
+
+if __name__ == "__main__":
+    main()