feat: integrate gradio

app.py

@@ -0,0 +1,372 @@
+"""
+GCP Cost Optimization MCP Server — Gradio Interface
+Wraps rohithay/dataops-mcp-server tools with a web UI + MCP SSE endpoint.
+"""
+
+import gradio as gr
+import os
+import json
+from typing import Optional
+
+# ── Import your actual server modules here ───────────────────────────────────
+# from dataops_mcp_server.bigquery import get_bigquery_costs, analyze_query_cost
+# from dataops_mcp_server.anomalies import detect_cost_anomalies
+# from dataops_mcp_server.optimize import optimize_query, create_optimization_pr
+# from dataops_mcp_server.alerts import send_cost_alert
+# from dataops_mcp_server.dbt import get_dbt_model_costs
+# from dataops_mcp_server.sla import monitor_sla_compliance
+# from dataops_mcp_server.forecast import forecast_costs
+# from dataops_mcp_server.slack import slack_post_message
+# ─────────────────────────────────────────────────────────────────────────────
+
+
+# ── Tool wrapper functions ────────────────────────────────────────────────────
+# Each function:
+#   1. Has the same name as your MCP tool
+#   2. Has a clear docstring (becomes the MCP tool description)
+#   3. Has type hints (become the MCP tool schema)
+#   4. Calls your real server module (replace the stub return)
+
+def get_bigquery_costs(
+    days: int = 7,
+    project_id: Optional[str] = None,
+    include_predictions: bool = True,
+    group_by: str = "date",
+    include_query_details: bool = False
+) -> str:
+    """
+    Retrieve comprehensive BigQuery cost analysis for specified time periods.
+
+    Args:
+        days: Number of days to analyze (1-90)
+        project_id: Specific GCP project ID (optional)
+        include_predictions: Include ML-based cost forecasting
+        group_by: Grouping dimension - date, user, dataset, or query_type
+        include_query_details: Include individual query cost breakdowns
+    """
+    # TODO: replace stub with → from dataops_mcp_server.bigquery import get_bigquery_costs as _get; return _get(...)
+    return json.dumps({"tool": "get_bigquery_costs", "days": days, "project_id": project_id, "status": "stub"}, indent=2)
+
+
+def analyze_query_cost(
+    sql: str,
+    project_id: Optional[str] = None,
+    include_optimization: bool = True,
+    optimization_model: str = "claude",
+    create_pr_if_savings: bool = False
+) -> str:
+    """
+    Predict cost of a SQL query before execution and get optimization suggestions.
+
+    Args:
+        sql: SQL query to analyze (required)
+        project_id: GCP project ID (optional)
+        include_optimization: Include AI-powered optimization suggestions
+        optimization_model: Model to use - claude or gpt-4
+        create_pr_if_savings: Create GitHub PR if savings exceed threshold
+    """
+    return json.dumps({"tool": "analyze_query_cost", "sql_preview": sql[:100], "model": optimization_model, "status": "stub"}, indent=2)
+
+
+def detect_cost_anomalies(
+    days: int = 30,
+    sensitivity: str = "medium",
+    project_id: Optional[str] = None,
+    alert_threshold: float = 0.25,
+    send_slack_alert: bool = False
+) -> str:
+    """
+    Use ML to detect cost spikes, anomalies, and early signs of overruns.
+
+    Args:
+        days: Historical days to analyze
+        sensitivity: Detection sensitivity - low, medium, or high
+        project_id: GCP project ID (optional)
+        alert_threshold: Fractional increase to trigger alert e.g. 0.25 means 25 percent
+        send_slack_alert: Send Slack alert if anomalies are found
+    """
+    return json.dumps({"tool": "detect_cost_anomalies", "days": days, "sensitivity": sensitivity, "threshold": alert_threshold, "status": "stub"}, indent=2)
+
+
+def optimize_query(
+    sql: str,
+    optimization_goals: str = "cost,performance",
+    preserve_results: bool = True,
+    include_explanation: bool = True,
+    target_savings_pct: int = 30,
+    dbt_model_path: Optional[str] = None
+) -> str:
+    """
+    LLM-powered query optimization with cost-saving recommendations.
+
+    Args:
+        sql: SQL query to optimize (required)
+        optimization_goals: Comma-separated objectives e.g. cost,performance
+        preserve_results: Ensure query results remain unchanged
+        include_explanation: Include explanation of changes made
+        target_savings_pct: Target savings percentage
+        dbt_model_path: Path to dbt model for additional context (optional)
+    """
+    return json.dumps({"tool": "optimize_query", "sql_preview": sql[:100], "goals": optimization_goals, "target_pct": target_savings_pct, "status": "stub"}, indent=2)
+
+
+def create_optimization_pr(
+    optimization_id: str,
+    repository: str = "data-platform",
+    base_branch: str = "main",
+    title_prefix: str = "🚀 Cost Optimization",
+    assign_reviewers: bool = True,
+    include_tests: bool = True
+) -> str:
+    """
+    Auto-create GitHub PRs with optimized SQL and validation tests.
+
+    Args:
+        optimization_id: Optimization analysis ID from a previous optimize_query call
+        repository: GitHub repository name
+        base_branch: Base branch for the PR
+        title_prefix: Prefix for the PR title
+        assign_reviewers: Auto-assign reviewers to the PR
+        include_tests: Generate validation tests for the PR
+    """
+    return json.dumps({"tool": "create_optimization_pr", "optimization_id": optimization_id, "repo": repository, "status": "stub"}, indent=2)
+
+
+def send_cost_alert(
+    alert_type: str,
+    cost_data: str,
+    severity: str = "medium",
+    channel: str = "#data-ops-alerts",
+    mention_users: str = "",
+    include_remediation: bool = True
+) -> str:
+    """
+    Send actionable cost alerts to Slack with rich context.
+
+    Args:
+        alert_type: Type of alert - anomaly, budget_warning, or optimization_opportunity
+        cost_data: JSON string of cost data to include in the alert
+        severity: Alert severity - low, medium, or high
+        channel: Slack channel for the alert
+        mention_users: Comma-separated Slack user IDs to mention
+        include_remediation: Include suggested fix in the alert
+    """
+    return json.dumps({"tool": "send_cost_alert", "type": alert_type, "severity": severity, "channel": channel, "status": "stub"}, indent=2)
+
+
+def get_dbt_model_costs(
+    model_path: Optional[str] = None,
+    include_dependencies: bool = True,
+    materialization_analysis: bool = True,
+    days: int = 7,
+    suggest_optimizations: bool = True
+) -> str:
+    """
+    Analyze dbt model execution costs and optimization opportunities.
+
+    Args:
+        model_path: Specific dbt model path (optional)
+        include_dependencies: Analyze downstream dependency impacts
+        materialization_analysis: Suggest materialization strategy improvements
+        days: Time period for analysis
+        suggest_optimizations: Include cost-saving suggestions
+    """
+    return json.dumps({"tool": "get_dbt_model_costs", "model_path": model_path, "days": days, "status": "stub"}, indent=2)
+
+
+def monitor_sla_compliance(
+    sla_type: str = "all",
+    time_window: str = "24h",
+    include_cost_correlation: bool = True,
+    alert_on_breach: bool = False,
+    optimization_suggestions: bool = True
+) -> str:
+    """
+    Monitor pipeline SLAs and correlate with cost-performance metrics.
+
+    Args:
+        sla_type: SLA type to monitor - latency, freshness, success_rate, or all
+        time_window: Time window for analysis e.g. 24h, 7d
+        include_cost_correlation: Link SLA metrics with cost data
+        alert_on_breach: Send alerts when SLA is breached
+        optimization_suggestions: Suggest cost-aware fixes for breaches
+    """
+    return json.dumps({"tool": "monitor_sla_compliance", "sla_type": sla_type, "window": time_window, "status": "stub"}, indent=2)
+
+
+def forecast_costs(
+    forecast_days: int = 30,
+    include_confidence_intervals: bool = True,
+    breakdown_by: str = "service",
+    scenario_analysis: bool = False,
+    budget_recommendations: bool = True
+) -> str:
+    """
+    Forecast future GCP spend using ML and scenario modeling.
+
+    Args:
+        forecast_days: Number of days to forecast
+        include_confidence_intervals: Include prediction confidence ranges
+        breakdown_by: Forecast breakdown dimension - service or project
+        scenario_analysis: Include optimistic and pessimistic scenarios
+        budget_recommendations: Suggest budget allocations based on forecast
+    """
+    return json.dumps({"tool": "forecast_costs", "days": forecast_days, "breakdown": breakdown_by, "status": "stub"}, indent=2)
+
+
+def slack_post_message(channel_id: str, text: str) -> str:
+    """
+    Post a message to a Slack channel.
+
+    Args:
+        channel_id: ID of the Slack channel
+        text: Message text to post
+    """
+    return json.dumps({"tool": "slack_post_message", "channel_id": channel_id, "text_preview": text[:100], "status": "stub"}, indent=2)
+
+
+# ── Gradio UI ─────────────────────────────────────────────────────────────────
+
+with gr.Blocks(title="GCP Cost Optimization", theme=gr.themes.Soft()) as demo:
+
+    gr.Markdown("""
+    # ☁️ GCP Cost Optimization MCP Server
+    Test tools directly in the browser · Connect any MCP client via the SSE endpoint below.
+    """)
+
+    with gr.Tab("📊 BigQuery Costs"):
+        with gr.Row():
+            bq_days = gr.Slider(1, 90, value=7, label="Days")
+            bq_project = gr.Textbox(label="Project ID (optional)")
+            bq_group_by = gr.Dropdown(["date", "user", "dataset", "query_type"], value="date", label="Group By")
+        with gr.Row():
+            bq_predictions = gr.Checkbox(value=True, label="Include Predictions")
+            bq_details = gr.Checkbox(value=False, label="Include Query Details")
+        gr.Button("Get BigQuery Costs", variant="primary").click(
+            get_bigquery_costs, [bq_days, bq_project, bq_predictions, bq_group_by, bq_details],
+            gr.Code(language="json", label="Result"))
+
+    with gr.Tab("🔍 Analyze Query Cost"):
+        aq_sql = gr.Code(language="sql", label="SQL Query", lines=8)
+        with gr.Row():
+            aq_project = gr.Textbox(label="Project ID (optional)")
+            aq_model = gr.Dropdown(["claude", "gpt-4"], value="claude", label="Model")
+        with gr.Row():
+            aq_opt = gr.Checkbox(value=True, label="Include Optimization")
+            aq_pr = gr.Checkbox(value=False, label="Create PR if Savings Found")
+        aq_out = gr.Code(language="json", label="Result")
+        gr.Button("Analyze", variant="primary").click(
+            analyze_query_cost, [aq_sql, aq_project, aq_opt, aq_model, aq_pr], aq_out)
+
+    with gr.Tab("🚨 Detect Anomalies"):
+        with gr.Row():
+            an_days = gr.Slider(1, 90, value=30, label="Historical Days")
+            an_sens = gr.Dropdown(["low", "medium", "high"], value="medium", label="Sensitivity")
+            an_thresh = gr.Slider(0.05, 1.0, value=0.25, step=0.05, label="Alert Threshold")
+        with gr.Row():
+            an_project = gr.Textbox(label="Project ID (optional)")
+            an_slack = gr.Checkbox(value=False, label="Send Slack Alert")
+        an_out = gr.Code(language="json", label="Result")
+        gr.Button("Detect Anomalies", variant="primary").click(
+            detect_cost_anomalies, [an_days, an_sens, an_project, an_thresh, an_slack], an_out)
+
+    with gr.Tab("⚡ Optimize Query"):
+        opt_sql = gr.Code(language="sql", label="SQL Query", lines=8)
+        with gr.Row():
+            opt_goals = gr.CheckboxGroup(["cost", "performance", "readability"], value=["cost", "performance"], label="Goals")
+            opt_pct = gr.Slider(10, 80, value=30, label="Target Savings %")
+        with gr.Row():
+            opt_preserve = gr.Checkbox(value=True, label="Preserve Results")
+            opt_explain = gr.Checkbox(value=True, label="Include Explanation")
+        opt_dbt = gr.Textbox(label="dbt Model Path (optional)")
+        opt_out = gr.Code(language="json", label="Result")
+        gr.Button("Optimize", variant="primary").click(
+            lambda sql, goals, preserve, explain, pct, dbt: optimize_query(sql, ",".join(goals), preserve, explain, pct, dbt),
+            [opt_sql, opt_goals, opt_preserve, opt_explain, opt_pct, opt_dbt], opt_out)
+
+    with gr.Tab("🔀 Create PR"):
+        with gr.Row():
+            pr_id = gr.Textbox(label="Optimization ID")
+            pr_repo = gr.Textbox(value="data-platform", label="Repository")
+        with gr.Row():
+            pr_branch = gr.Textbox(value="main", label="Base Branch")
+            pr_prefix = gr.Textbox(value="🚀 Cost Optimization", label="Title Prefix")
+        with gr.Row():
+            pr_reviewers = gr.Checkbox(value=True, label="Auto-assign Reviewers")
+            pr_tests = gr.Checkbox(value=True, label="Include Tests")
+        pr_out = gr.Code(language="json", label="Result")
+        gr.Button("Create PR", variant="primary").click(
+            create_optimization_pr, [pr_id, pr_repo, pr_branch, pr_prefix, pr_reviewers, pr_tests], pr_out)
+
+    with gr.Tab("🔔 Cost Alerts"):
+        with gr.Row():
+            al_type = gr.Dropdown(["anomaly", "budget_warning", "optimization_opportunity"], label="Alert Type")
+            al_sev = gr.Dropdown(["low", "medium", "high"], value="medium", label="Severity")
+        al_data = gr.Code(language="json", label="Cost Data (JSON)", value="{}", lines=4)
+        with gr.Row():
+            al_channel = gr.Textbox(value="#data-ops-alerts", label="Slack Channel")
+            al_users = gr.Textbox(label="Mention Users (comma-separated)")
+        al_rem = gr.Checkbox(value=True, label="Include Remediation")
+        al_out = gr.Code(language="json", label="Result")
+        gr.Button("Send Alert", variant="primary").click(
+            send_cost_alert, [al_type, al_data, al_sev, al_channel, al_users, al_rem], al_out)
+
+    with gr.Tab("🔧 dbt Costs"):
+        with gr.Row():
+            dbt_path = gr.Textbox(label="dbt Model Path (optional)")
+            dbt_days = gr.Slider(1, 30, value=7, label="Days")
+        with gr.Row():
+            dbt_deps = gr.Checkbox(value=True, label="Include Dependencies")
+            dbt_mat = gr.Checkbox(value=True, label="Materialization Analysis")
+            dbt_opt = gr.Checkbox(value=True, label="Suggest Optimizations")
+        dbt_out = gr.Code(language="json", label="Result")
+        gr.Button("Analyze dbt Costs", variant="primary").click(
+            get_dbt_model_costs, [dbt_path, dbt_deps, dbt_mat, dbt_days, dbt_opt], dbt_out)
+
+    with gr.Tab("📈 SLA Monitoring"):
+        with gr.Row():
+            sla_type = gr.Dropdown(["all", "latency", "freshness", "success_rate"], value="all", label="SLA Type")
+            sla_window = gr.Dropdown(["1h", "6h", "24h", "7d", "30d"], value="24h", label="Time Window")
+        with gr.Row():
+            sla_cost = gr.Checkbox(value=True, label="Cost Correlation")
+            sla_alert = gr.Checkbox(value=False, label="Alert on Breach")
+            sla_opt = gr.Checkbox(value=True, label="Optimization Suggestions")
+        sla_out = gr.Code(language="json", label="Result")
+        gr.Button("Monitor SLA", variant="primary").click(
+            monitor_sla_compliance, [sla_type, sla_window, sla_cost, sla_alert, sla_opt], sla_out)
+
+    with gr.Tab("🔮 Forecast Costs"):
+        with gr.Row():
+            fc_days = gr.Slider(7, 90, value=30, label="Forecast Days")
+            fc_breakdown = gr.Dropdown(["service", "project"], value="service", label="Breakdown By")
+        with gr.Row():
+            fc_ci = gr.Checkbox(value=True, label="Confidence Intervals")
+            fc_scenario = gr.Checkbox(value=False, label="Scenario Analysis")
+            fc_budget = gr.Checkbox(value=True, label="Budget Recommendations")
+        fc_out = gr.Code(language="json", label="Result")
+        gr.Button("Forecast", variant="primary").click(
+            forecast_costs, [fc_days, fc_ci, fc_breakdown, fc_scenario, fc_budget], fc_out)
+
+    with gr.Tab("💬 Slack"):
+        sl_channel = gr.Textbox(label="Channel ID")
+        sl_text = gr.Textbox(label="Message", lines=4)
+        sl_out = gr.Code(language="json", label="Result")
+        gr.Button("Post Message", variant="primary").click(
+            slack_post_message, [sl_channel, sl_text], sl_out)
+
+    gr.Markdown("""
+    ---
+    ### 🔌 MCP Client Config
+    ```json
+    {
+      "mcpServers": {
+        "gcp-cost-optimization": {
+          "url": "https://<your-hf-username>-gcp-cost-optimization.hf.space/gradio_api/mcp/sse"
+        }
+      }
+    }
+    ```
+    """)
+
+if __name__ == "__main__":
+    demo.launch(mcp_server=True)