Initial commit: CMM Report Analyzer

FastAPI app that parses CMM inspection reports (PDF/Excel/CSV),
computes SPC metrics (Cp/Cpk/Pp/Ppk, control limits, Shapiro-Wilk),
generates interactive Plotly charts, and provides AI-powered quality
summaries via Azure OpenAI with graceful fallback.

Includes 21 passing tests covering parsers, SPC calculations, and
API endpoints.

app/analysis/charts.py

@@ -0,0 +1,86 @@
+from __future__ import annotations
+
+import plotly.graph_objects as go
+
+from app.analysis.spc import SPCResult
+
+
+def histogram(result: SPCResult) -> dict:
+    """Distribution histogram with spec limits overlay."""
+    fig = go.Figure()
+    fig.add_trace(go.Histogram(x=result.values, name="Measurements", nbinsx=20))
+    fig.add_vline(x=result.usl, line_dash="dash", line_color="red",
+                  annotation_text="USL")
+    fig.add_vline(x=result.lsl, line_dash="dash", line_color="red",
+                  annotation_text="LSL")
+    fig.add_vline(x=result.nominal, line_dash="dot", line_color="green",
+                  annotation_text="Nominal")
+    fig.update_layout(
+        title=f"Distribution – {result.feature_name}",
+        xaxis_title="Value", yaxis_title="Count",
+        template="plotly_white", height=350,
+    )
+    return fig.to_plotly_json()
+
+
+def control_chart(result: SPCResult) -> dict:
+    """Individual values control chart (I-chart)."""
+    x_axis = list(range(1, result.n + 1))
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(
+        x=x_axis, y=result.values, mode="lines+markers", name="Value",
+    ))
+    fig.add_hline(y=result.mean, line_color="green", annotation_text="Mean")
+    fig.add_hline(y=result.ucl, line_dash="dash", line_color="red",
+                  annotation_text="UCL")
+    fig.add_hline(y=result.lcl, line_dash="dash", line_color="red",
+                  annotation_text="LCL")
+    fig.add_hline(y=result.usl, line_dash="dot", line_color="orange",
+                  annotation_text="USL")
+    fig.add_hline(y=result.lsl, line_dash="dot", line_color="orange",
+                  annotation_text="LSL")
+    fig.update_layout(
+        title=f"Control Chart – {result.feature_name}",
+        xaxis_title="Sample #", yaxis_title="Value",
+        template="plotly_white", height=350,
+    )
+    return fig.to_plotly_json()
+
+
+def capability_bar(results: list[SPCResult]) -> dict:
+    """Capability index bar chart comparing all features."""
+    names = [r.feature_name for r in results]
+    cpk_vals = [r.cpk if r.cpk is not None else 0.0 for r in results]
+    ppk_vals = [r.ppk if r.ppk is not None else 0.0 for r in results]
+
+    colors = ["#2ecc71" if v >= 1.33 else "#f39c12" if v >= 1.0 else "#e74c3c"
+              for v in cpk_vals]
+
+    fig = go.Figure()
+    fig.add_trace(go.Bar(x=names, y=cpk_vals, name="Cpk", marker_color=colors))
+    fig.add_trace(go.Bar(x=names, y=ppk_vals, name="Ppk", marker_color="rgba(52,152,219,0.6)"))
+    fig.add_hline(y=1.33, line_dash="dash", line_color="green",
+                  annotation_text="Cpk=1.33")
+    fig.add_hline(y=1.0, line_dash="dot", line_color="orange",
+                  annotation_text="Cpk=1.0")
+    fig.update_layout(
+        title="Process Capability Summary",
+        xaxis_title="Feature", yaxis_title="Index",
+        barmode="group", template="plotly_white", height=400,
+    )
+    return fig.to_plotly_json()
+
+
+def generate_charts(results: list[SPCResult]) -> dict:
+    """Generate all charts for a set of SPC results."""
+    charts: dict[str, list[dict] | dict] = {
+        "histograms": [],
+        "control_charts": [],
+    }
+    for r in results:
+        if r.n >= 2:
+            charts["histograms"].append(histogram(r))
+            charts["control_charts"].append(control_chart(r))
+    if results:
+        charts["capability_bar"] = capability_bar(results)
+    return charts