chnages

model_interface/a_17_grapes_count.py

@@ -425,103 +425,111 @@ def grapes_count():
                 "b_ratio":     b_ratio,
             })
 
-    # ── STABLE two-column layout (always rendered, prevents shaking) ───
-    # Read the input image dimensions so the placeholder matches exactly.
+    # ── Read input image aspect ratio once ────────────────────────────
     _probe = cv2.imread(image_path)
-    if _probe is not None:
-        _ih, _iw = _probe.shape[:2]
-        # Columns are ~50% of page width. Use aspect-ratio CSS so the
-        # placeholder box is the same proportional height as the real image.
-        _aspect_pct = round((_ih / _iw) * 100, 2)
-    else:
-        _aspect_pct = 75.0   # safe fallback (4:3)
-
-    col_input, col_output = st.columns(2)
-
-    with col_input:
-        st.subheader(f"📷 Input — {selected_image}")
-        st.image(image_path, use_container_width=True)
-
-    with col_output:
-        st.subheader("🔍 Predicted Output")
-        # Use a single st.empty() slot — Streamlit replaces its content
-        # in-place on rerun, so the surrounding layout never shifts.
-        out_slot = st.empty()
+    _aspect_pct = round((_probe.shape[0] / _probe.shape[1]) * 100, 2) \
+                  if _probe is not None else 75.0
+
+    # ── Tabs: Detection | Results — both always exist in the DOM ──────
+    # Using tabs means the results panel is NEVER below the images, so
+    # it can never push the image columns up and cause shaking.
+    tab_detect, tab_results = st.tabs(["🔍 Detection", "📊 Results & Export"])
+
+    # ── TAB 1: Detection ──────────────────────────────────────────────
+    with tab_detect:
+        col_input, col_output = st.columns(2)
+
+        with col_input:
+            st.subheader(f"📷 Input — {selected_image}")
+            st.image(image_path, use_container_width=True)
+
+        with col_output:
+            st.subheader("🔍 Predicted Output")
+            out_slot = st.empty()
+            if st.session_state["result"] is not None:
+                out_img = st.session_state["result"]["image"]
+                if os.path.exists(out_img):
+                    out_slot.image(out_img, use_container_width=True)
+            else:
+                out_slot.markdown(
+                    f"""
+                    <div style="position:relative;width:100%;
+                                padding-top:{_aspect_pct}%;
+                                background:#1e1e1e;border-radius:8px;
+                                border:1px dashed #444;">
+                      <div style="position:absolute;inset:0;display:flex;
+                                  align-items:center;justify-content:center;
+                                  color:#888;font-size:15px;">
+                        Click <strong>▶ Run</strong> in the sidebar
+                      </div>
+                    </div>
+                    """,
+                    unsafe_allow_html=True,
+                )
+
+        # Status bar at bottom of detection tab — never causes layout shift
         if st.session_state["result"] is not None:
-            out_img = st.session_state["result"]["image"]
-            if os.path.exists(out_img):
-                out_slot.image(out_img, use_container_width=True)
+            st.success(f"✅ Detection complete — "
+                       f"{st.session_state['result']['summary']['total']} grapes found. "
+                       f"Switch to the **Results & Export** tab for full analysis.")
         else:
-            # Aspect-ratio box matches the input image height exactly.
-            out_slot.markdown(
-                f"""
-                <div style="position:relative;width:100%;
-                            padding-top:{_aspect_pct}%;
-                            background:#f0f2f6;border-radius:8px;">
-                  <div style="position:absolute;inset:0;display:flex;
-                              align-items:center;justify-content:center;
-                              color:#888;font-size:15px;">
-                    Click <strong>Run</strong> to see prediction here
-                  </div>
-                </div>
-                """,
-                unsafe_allow_html=True,
-            )
+            st.info("Press **▶ Run Prediction & Grade** in the sidebar to start.")
 
-    # ── results section ────────────────────────────────────────────────
-    if st.session_state["result"] is not None:
-        res         = st.session_state["result"]
-        score       = st.session_state["score"]
-        grade_label = st.session_state["grade_label"]
-        breakdown   = st.session_state["breakdown"]
-
-        st.success("✅ Prediction complete!")
-        st.markdown("---")
-        st.header("📊 Evaluation Results")
-
-        m1, m2, m3, m4 = st.columns(4)
-        m1.metric("Total Grapes",  res["summary"]["total"])
-        m2.metric("Green / Black",
-                  f'{res["summary"]["green"]} / {res["summary"]["black"]}')
-        m3.metric("QC Score",      f"{score} / 100")
-        m4.metric("Grade",         grade_label)
-
-        t1, t2 = st.columns(2)
-        with t1:
-            st.subheader("QC Penalty Breakdown")
-            st.dataframe(pd.DataFrame(breakdown), use_container_width=True)
-        with t2:
-            st.subheader("Detection Summary")
-            st.json(res["summary"])
-
-        st.subheader("📄 Raw Detection Data")
-        st.dataframe(res["df"], use_container_width=True)
-
-        st.markdown("---")
-        st.header("📥 Export Reports")
-        pdf_path = generate_pdf_report(
-            image_path, res, score, grade_label, breakdown, selected_template
-        )
+    # ── TAB 2: Results & Export ───────────────────────────────────────
+    with tab_results:
+        if st.session_state["result"] is None:
+            st.info("Run a prediction first to see results here.")
+        else:
+            res         = st.session_state["result"]
+            score       = st.session_state["score"]
+            grade_label = st.session_state["grade_label"]
+            breakdown   = st.session_state["breakdown"]
+
+            st.header("📊 Evaluation Results")
+            m1, m2, m3, m4 = st.columns(4)
+            m1.metric("Total Grapes",  res["summary"]["total"])
+            m2.metric("Green / Black",
+                      f'{res["summary"]["green"]} / {res["summary"]["black"]}')
+            m3.metric("QC Score",      f"{score} / 100")
+            m4.metric("Grade",         grade_label)
+
+            st.markdown("---")
+            t1, t2 = st.columns(2)
+            with t1:
+                st.subheader("QC Penalty Breakdown")
+                st.dataframe(pd.DataFrame(breakdown), use_container_width=True)
+            with t2:
+                st.subheader("Detection Summary")
+                st.json(res["summary"])
+
+            st.subheader("📄 Raw Detection Data")
+            st.dataframe(res["df"], use_container_width=True)
+
+            st.markdown("---")
+            st.header("📥 Export Reports")
+            pdf_path = generate_pdf_report(
+                image_path, res, score, grade_label, breakdown, selected_template
+            )
 
-        dl1, dl2 = st.columns(2)
-        with dl1:
-            with open(res["excel"], "rb") as f:
-                st.download_button(
-                    label="⬇ Download Excel Data",
-                    data=f,
-                    file_name="grape_analysis.xlsx",
-                    mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
-                    use_container_width=True,
-                )
-        with dl2:
-            with open(pdf_path, "rb") as f:
-                st.download_button(
-                    label="📄 Download PDF Report",
-                    data=f,
-                    file_name=f"QC_Report_{selected_template}.pdf",
-                    mime="application/pdf",
-                    use_container_width=True,
-                )
+            dl1, dl2 = st.columns(2)
+            with dl1:
+                with open(res["excel"], "rb") as f:
+                    st.download_button(
+                        label="⬇ Download Excel Data",
+                        data=f,
+                        file_name="grape_analysis.xlsx",
+                        mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
+                        use_container_width=True,
+                    )
+            with dl2:
+                with open(pdf_path, "rb") as f:
+                    st.download_button(
+                        label="📄 Download PDF Report",
+                        data=f,
+                        file_name=f"QC_Report_{selected_template}.pdf",
+                        mime="application/pdf",
+                        use_container_width=True,
+                    )
 
 
 if __name__ == "__main__":