Update app.py

app.py

@@ -39,15 +39,6 @@ def _coerce_numeric(x):
     try: return float(s)
     except: return pd.NA
 
-# ---- NEW: in‑memory file helpers (no disk writes) ----
-def _csv_bytes(df: pd.DataFrame | None) -> bytes:
-    buf = io.StringIO()
-    (df if df is not None else pd.DataFrame()).to_csv(buf, index=False)
-    return buf.getvalue().encode("utf-8")
-
-def _text_bytes(txt: str | None) -> bytes:
-    return (txt or "").encode("utf-8")
-
 PARAM_PATTERNS = {
     "cost":       r"cost\s*[:=]\s*\$?\s*([\d,]+(?:\.\d+)?)",
     "salvage":    r"salvage\s*[:=]\s*\$?\s*([\d,]+(?:\.\d+)?)",
@@ -251,8 +242,7 @@ def handle_docx(file):
 
 def handle_image(img):
     if img is None:
-        # include empty bytes for download buttons
-        return "(no image)", {}, pd.DataFrame(), pd.DataFrame(), b"", b"", b"", 0.0, 0.0, 10, pd.Timestamp.now().year, {}, pd.DataFrame()
+        return "(no image)", {}, pd.DataFrame(), pd.DataFrame(), 0.0, 0.0, 10, pd.Timestamp.now().year, {}, pd.DataFrame()
 
     from PIL import Image as PILImage
     pil = img if isinstance(img, PILImage.Image) else PILImage.fromarray(img)
@@ -262,17 +252,6 @@ def handle_image(img):
     df_raw = _table_from_ocr_text(ocr_text or "")
     df_norm = _normalize_depr_columns(df_raw) if df_raw is not None else pd.DataFrame()
 
-    # numerics (helps when you edit later)
-    if not df_norm.empty:
-        for c in ["Year","Begin BV","Depreciation","Accum Dep","End BV"]:
-            if c in df_norm.columns:
-                df_norm[c] = pd.to_numeric(df_norm[c], errors="coerce")
-
-    # in‑memory artifacts
-    raw_csv_bytes  = _csv_bytes(df_raw)
-    norm_csv_bytes = _csv_bytes(df_norm)
-    txt_bytes      = _text_bytes(ocr_text or "")
-
     cost, salv, life, year = _params_tuple(params)
 
     return (
@@ -280,14 +259,13 @@ def handle_image(img):
         params,
         df_raw,                  # raw table shown in OCR tab
         df_norm,                 # normalized table shown in OCR tab
-        raw_csv_bytes,           # bytes for RAW CSV download
-        norm_csv_bytes,          # bytes for NORMALIZED CSV download
-        txt_bytes,               # bytes for OCR TEXT download
         cost, salv, life, year,  # auto-fill numbers
         params,                  # save params state
-        df_norm                  # save normalized table to last_table
+        df_norm                  # 🔹 save normalized table to last_table (same as docx)
     )
 
+
+
 def fill_from_state(p):
     p = p or {}
     return (
@@ -313,7 +291,7 @@ def check_cb(cost, salv, life, year, table_state):
     if not isinstance(table_state, pd.DataFrame) or table_state.empty:
         return pd.DataFrame(), "No student table found to check."
 
-    # re-normalize and coerce here every time
+    # 👇 Gradio returns strings → re-normalize and coerce here every time
     actual = _normalize_depr_columns(table_state)
     for c in ["Year", "Begin BV", "Depreciation", "Accum Dep", "End BV"]:
         actual[c] = pd.to_numeric(actual[c], errors="coerce")
@@ -344,16 +322,7 @@ with gr.Blocks(title="Jerry • HW Intake (Echo)") as demo:
         ocr_txt = gr.Textbox(label="Raw OCR text", lines=12)
         params_json2 = gr.JSON(label="Detected parameters")
         raw_df = gr.Dataframe(label="Raw table guess", interactive=False)
-        # make editable so you can fix numbers
-        norm_df = gr.Dataframe(label="Detected table (normalized)", interactive=True)
-
-        # ---- NEW: in‑memory downloads ----
-        raw_dl  = gr.DownloadButton(label="Download RAW CSV")
-        norm_dl = gr.DownloadButton(label="Download NORMALIZED CSV")
-        txt_dl  = gr.DownloadButton(label="Download OCR TEXT")
-
-        # re‑export after manual edits
-        export_btn = gr.DownloadButton(label="Download EDITED NORMALIZED CSV")
+        norm_df = gr.Dataframe(label="Detected table (normalized)", interactive=False)
 
     # --- Tab 3: Solve & Check ---
     with gr.Tab("Straight-Line • Solve & Check"):
@@ -393,9 +362,6 @@ with gr.Blocks(title="Jerry • HW Intake (Echo)") as demo:
             params_json2,      # json
             raw_df,            # raw table
             norm_df,           # normalized table (tab 2)
-            raw_dl,            # NEW: bytes -> RAW CSV download
-            norm_dl,           # NEW: bytes -> NORMALIZED CSV download
-            txt_dl,            # NEW: bytes -> OCR TEXT download
             in_cost, in_salv, in_life, in_year,   # autofill inputs
             last_params,       # state
             last_table,        # state
@@ -412,12 +378,6 @@ with gr.Blocks(title="Jerry • HW Intake (Echo)") as demo:
     btn_build.click(build_cb, [in_cost, in_salv, in_life, in_year], [expected_df])
     btn_check.click(check_cb, [in_cost, in_salv, in_life, in_year, last_table], [deltas_df, coach_txt])
 
-    # ---- NEW: export edited normalized CSV ----
-    def export_current(norm_df_current: pd.DataFrame):
-        return _csv_bytes(norm_df_current if isinstance(norm_df_current, pd.DataFrame) else pd.DataFrame())
-
-    export_btn.click(export_current, inputs=norm_df, outputs=export_btn)
-
     gr.Markdown("— Echo mode finished. When this looks good, we’ll plug in the SL solver + coaching.")
 
 if __name__ == "__main__":