Update app.py

app.py

@@ -12,223 +12,125 @@ import gradio_client.utils
 
 """NetCom → WooCommerce transformer (Try 1 schema)
 =================================================
-Drop a *Reseller Schedule* CSV and get back a WooCommerce‑ready CSV that matches
-`Try 1 - WooCommerce_Mapped_Data__Fixed_Attributes_and_Agenda_.csv` exactly –
-including `Stock` and `Sold individually?` columns that NetCom doesn’t supply.
-
-Highlights
-----------
-* Empty cells are skipped – no wasted GPT calls.
-* GPT‑4o mini used with a tiny disk cache (`ai_response_cache/`).
-* Brand → logo URLs hard‑coded below (update when media library changes).
+*Accept CSV **or** Excel schedule files and output the WooCommerce CSV.*
+
+Fixes vs last run
+-----------------
+* Output written to a **temporary file path** (Gradio BytesIO bug fixed).
+* **Excel upload** support.
+* **Pandas future‑warning** silenced (`group_keys=False`).
 """
-#
-# ---------------------------------------------------------------------------
-# Gradio JSON‑schema helper hot‑patch (bool schema bug)
-# ---------------------------------------------------------------------------
+
+# -------- Gradio bool‑schema hot‑patch --------------------------------------
 _original = gradio_client.utils._json_schema_to_python_type
 
 def _fixed_json_schema_to_python_type(schema, defs=None):
-    if isinstance(schema, bool):  # gradio 4.29 bug
+    if isinstance(schema, bool):
         return "any"
     return _original(schema, defs)
 
 gradio_client.utils._json_schema_to_python_type = _fixed_json_schema_to_python_type  # type: ignore
 
-# ---------------------------------------------------------------------------
-# Tiny disk cache for OpenAI responses
-# ---------------------------------------------------------------------------
+# -------- Tiny disk cache ----------------------------------------------------
 CACHE_DIR = Path("ai_response_cache"); CACHE_DIR.mkdir(exist_ok=True)
 
+def _cache_path(p: str):
+    return CACHE_DIR / f"{hashlib.md5(p.encode()).hexdigest()}.json"
 
-def _cache_path(prompt: str) -> Path:
-    return CACHE_DIR / f"{hashlib.md5(prompt.encode()).hexdigest()}.json"
-
-
-def _get_cached(prompt: str):
+def _get_cached(p: str):
     try:
-        return json.loads(_cache_path(prompt).read_text("utf-8"))["response"]
+        return json.loads(_cache_path(p).read_text("utf-8"))["response"]
     except Exception:
         return None
 
-
-def _set_cache(prompt: str, rsp: str):
+def _set_cache(p: str, r: str):
     try:
-        _cache_path(prompt).write_text(json.dumps({"prompt": prompt, "response": rsp}), "utf-8")
+        _cache_path(p).write_text(json.dumps({"prompt": p, "response": r}), "utf-8")
     except Exception:
         pass
 
-# ---------------------------------------------------------------------------
-# Async GPT helpers
-# ---------------------------------------------------------------------------
-async def _gpt(client: openai.AsyncOpenAI, prompt: str) -> str:
-    cached = _get_cached(prompt)
-    if cached is not None:
-        return cached
+# -------- Async GPT helpers --------------------------------------------------
+async def _gpt(client, prompt):
+    c = _get_cached(prompt)
+    if c is not None:
+        return c
     try:
-        cmp = await client.chat.completions.create(
-            model="gpt-4o-mini",
-            messages=[{"role": "user", "content": prompt}],
-            temperature=0,
-        )
-        txt = cmp.choices[0].message.content
+        msg = await client.chat.completions.create(model="gpt-4o-mini", messages=[{"role": "user", "content": prompt}], temperature=0)
+        text = msg.choices[0].message.content
     except Exception as e:
-        txt = f"Error: {e}"
-    _set_cache(prompt, txt)
-    return txt
-
-
-async def _batch(texts: list[str], instruction: str) -> list[str]:
-    """Return len(texts) list. Blank inputs remain blank."""
-    res = ["" for _ in texts]
-    idx, prompts = [], []
-    for i, t in enumerate(texts):
-        if isinstance(t, str) and t.strip():
-            idx.append(i); prompts.append(f"{instruction}\n\nText: {t}")
-    if not prompts:
-        return res
+        text = f"Error: {e}"
+    _set_cache(prompt, text)
+    return text
+
+async def _batch(lst, instr):
+    out = ["" for _ in lst]; idx,prompts=[],[]
+    for i,t in enumerate(lst):
+        if isinstance(t,str) and t.strip(): idx.append(i); prompts.append(f"{instr}\n\nText: {t}")
+    if not prompts: return out
     client = openai.AsyncOpenAI(api_key=os.getenv("OPENAI_API_KEY"))
-    tasks = [_gpt(client, p) for p in prompts]
-    outs = await asyncio.gather(*tasks)
-    for k, v in enumerate(outs):
-        res[idx[k]] = v
-    return res
-
-# ---------------------------------------------------------------------------
-# Main converter
-# ---------------------------------------------------------------------------
-
-def process_woocommerce_data_in_memory(netcom_file):
-    """Return BytesIO of Woo CSV."""
-    # Brand logos
-    brand_logo_map = {
-        "Amazon Web Services": "/wp-content/uploads/2025/04/aws.png",
-        "Cisco": "/wp-content/uploads/2025/04/cisco-e1738593292198-1.webp",
-        "Microsoft": "/wp-content/uploads/2025/04/Microsoft-e1737494120985-1.png",
-        "Google Cloud": "/wp-content/uploads/2025/04/Google_Cloud.png",
-        "EC Council": "/wp-content/uploads/2025/04/Ec_Council.png",
-        "ITIL": "/wp-content/uploads/2025/04/ITIL.webp",
-        "PMI": "/wp-content/uploads/2025/04/PMI.png",
-        "Comptia": "/wp-content/uploads/2025/04/Comptia.png",
-        "Autodesk": "/wp-content/uploads/2025/04/autodesk.png",
-        "ISC2": "/wp-content/uploads/2025/04/ISC2.png",
-        "AICerts": "/wp-content/uploads/2025/04/aicerts-logo-1.png",
-    }
-    default_prereq = (
-        "No specific prerequisites are required for this course. "
-        "Basic computer literacy and familiarity with fundamental concepts in the subject area are recommended for the best learning experience."
-    )
-    # Load NetCom CSV
-    df = pd.read_csv(netcom_file.name, encoding="latin1"); df.columns = df.columns.str.strip()
-    def _col(opts):
-        return next((c for c in opts if c in df.columns), None)
-    # Column aliases
-    col_desc = _col(["Description", "Decription"])
-    col_obj  = _col(["Objectives", "objectives"])
-    col_pre  = _col(["RequiredPrerequisite", "Required Pre-requisite"])
-    col_out  = _col(["Outline"])
-    col_dur  = _col(["Duration"])
-    col_sid  = _col(["Course SID", "Course SID"])
-    if col_dur is None:
-        df["Duration"] = ""; col_dur = "Duration"
-    # AI prep lists
-    descs, objs, pres, outs = (df.get(c, pd.Series([""]*len(df))).fillna("").tolist() for c in (col_desc, col_obj, col_pre, col_out))
-    loop = asyncio.new_event_loop(); asyncio.set_event_loop(loop)
-    short_d, long_d, fmt_obj, fmt_out = loop.run_until_complete(asyncio.gather(
-        _batch(descs, "Create a concise 250-character summary of this course description:"),
-        _batch(descs, "Condense this description to a maximum of 750 characters in paragraph format, with clean formatting:"),
-        _batch(objs,  "Format these objectives into a bullet list with clean formatting. Start each bullet with '• ':"),
-        _batch(outs,  "Format this agenda into a bullet list with clean formatting. Start each bullet with '• ':"),
-    )); loop.close()
-    fmt_pre = [default_prereq if not str(p).strip() else asyncio.run(_batch([p], "Format these prerequisites into a bullet list with clean formatting. Start each bullet with '• ':"))[0] for p in pres]
-    # Attach processed cols
-    df["Short_Description"] = short_d; df["Condensed_Description"] = long_d
-    df["Formatted_Objectives"] = fmt_obj; df["Formatted_Agenda"] = fmt_out; df["Formatted_Prerequisites"] = fmt_pre
-    # Dates
+    res = await asyncio.gather(*[_gpt(client,p) for p in prompts])
+    for j,val in enumerate(res): out[idx[j]] = val
+    return out
+
+# -------- Core converter -----------------------------------------------------
+
+def _read(path: str):
+    return pd.read_excel(path) if path.lower().endswith((".xlsx",".xls")) else pd.read_csv(path, encoding="latin1")
+
+def convert(path: str) -> BytesIO:
+    logos = {"Amazon Web Services":"/wp-content/uploads/2025/04/aws.png","Cisco":"/wp-content/uploads/2025/04/cisco-e1738593292198-1.webp","Microsoft":"/wp-content/uploads/2025/04/Microsoft-e1737494120985-1.png","Google Cloud":"/wp-content/uploads/2025/04/Google_Cloud.png","EC Council":"/wp-content/uploads/2025/04/Ec_Council.png","ITIL":"/wp-content/uploads/2025/04/ITIL.webp","PMI":"/wp-content/uploads/2025/04/PMI.png","Comptia":"/wp-content/uploads/2025/04/Comptia.png","Autodesk":"/wp-content/uploads/2025/04/autodesk.png","ISC2":"/wp-content/uploads/2025/04/ISC2.png","AICerts":"/wp-content/uploads/2025/04/aicerts-logo-1.png"}
+    default_pre = "No specific prerequisites are required for this course. Basic computer literacy and familiarity with fundamental concepts in the subject area are recommended for the best learning experience."
+
+    df = _read(path); df.columns = df.columns.str.strip()
+    c = lambda *o: next((x for x in o if x in df.columns), None)
+    dcol, ocol, pcol, acol, dur, sid = c("Description","Decription"), c("Objectives","objectives"), c("RequiredPrerequisite","Required Pre-requisite"), c("Outline"), c("Duration"), c("Course SID","Course SID")
+    if dur is None: df["Duration"]=""; dur="Duration"
+
+    loop=asyncio.new_event_loop(); asyncio.set_event_loop(loop)
+    sdesc, ldesc, fobj, fout = loop.run_until_complete(asyncio.gather(
+        _batch(df.get(dcol,"").fillna("").tolist(), "Create a concise 250-character summary of this course description:"),
+        _batch(df.get(dcol,"").fillna("").tolist(), "Condense this description to a maximum of 750 characters in paragraph format, with clean formatting:"),
+        _batch(df.get(ocol,"").fillna("").tolist(), "Format these objectives into a bullet list with clean formatting. Start each bullet with '• ':") ,
+        _batch(df.get(acol,"").fillna("").tolist(), "Format this agenda into a bullet list with clean formatting. Start each bullet with '• ':")))
+    loop.close()
+    fpre=[default_pre if not str(p).strip() else asyncio.run(_batch([p],"Format these prerequisites into a bullet list with clean formatting. Start each bullet with '• ':"))[0] for p in df.get(pcol,"").fillna("").tolist()]
+
+    df["Short_Description"],df["Condensed_Description"],df["Formatted_Objectives"],df["Formatted_Agenda"],df["Formatted_Prerequisites"] = sdesc,ldesc,fobj,fout,fpre
+
     df["Course Start Date"] = pd.to_datetime(df["Course Start Date"], errors="coerce")
     df["Date_fmt"] = df["Course Start Date"].dt.strftime("%-m/%-d/%Y")
-    df_sorted = df.sort_values(["Course ID", "Course Start Date"])
-    date_agg = df_sorted.groupby("Course ID")["Date_fmt"].apply(lambda s: ",".join(s.dropna().unique())).reset_index(name="Aggregated_Dates")
-    time_agg = df_sorted.groupby("Course ID").apply(lambda g: ",".join(f"{st}-{et} {tz}" for st, et, tz in zip(g["Course Start Time"], g["Course End Time"], g["Time Zone"]))).reset_index(name="Aggregated_Times")
-    parents = df_sorted.drop_duplicates("Course ID").merge(date_agg).merge(time_agg)
-    # Parent rows
-    woo_parent = pd.DataFrame({
-        "Type": "variable",
-        "SKU": parents["Course ID"],
-        "Name": parents["Course Name"],
-        "Published": 1,
-        "Visibility in catalog": "visible",
-        "Short description": parents["Short_Description"],
-        "Description": parents["Condensed_Description"],
-        "Tax status": "taxable",
-        "In stock?": 1,
-        "Stock": 1,
-        "Sold individually?": 1,
-        "Regular price": parents["SRP Pricing"].replace("[\\$,]", "", regex=True),
-        "Categories": "courses",
-        "Images": parents["Vendor"].map(brand_logo_map).fillna(""),
-        "Parent": "",
-        "Brands": parents["Vendor"],
-        # Attributes
-        "Attribute 1 name": "Date", "Attribute 1 value(s)": parents["Aggregated_Dates"], "Attribute 1 visible": "visible", "Attribute 1 global": 1,
-        "Attribute 2 name": "Location", "Attribute 2 value(s)": "Virtual", "Attribute 2 visible": "visible", "Attribute 2 global": 1,
-        "Attribute 3 name": "Time", "Attribute 3 value(s)": parents["Aggregated_Times"], "Attribute 3 visible": "visible", "Attribute 3 global": 1,
-        # Meta
-        "Meta: outline": parents["Formatted_Agenda"], "Meta: days": parents[col_dur], "Meta: location": "Virtual",
-        "Meta: overview": parents["Target Audience"], "Meta: objectives": parents["Formatted_Objectives"],
-        "Meta: prerequisites": parents["Formatted_Prerequisites"], "Meta: agenda": parents["Formatted_Agenda"],
-    })
-    # Child rows
-    woo_child = pd.DataFrame({
-        "Type": "variation, virtual",
-        "SKU": df_sorted[col_sid].astype(str).str.strip(),
-        "Name": df_sorted["Course Name"],
-        "Published": 1,
-        "Visibility in catalog": "visible",
-        "Short description": df_sorted["Short_Description"],
-        "Description": df_sorted["Condensed_Description"],
-        "Tax status": "taxable",
-        "In stock?": 1,
-        "Stock": 1,
-        "Sold individually?": 1,
-        "Regular price": df_sorted["SRP Pricing"].replace("[\\$,]", "", regex=True),
-        "Categories": "courses",
-        "Images": df_sorted["Vendor"].map(brand_logo_map).fillna(""),
-        "Parent": df_sorted["Course ID"],
-        "Brands": df_sorted["Vendor"],
-        "Attribute 1 name": "Date", "Attribute 1 value(s)": df_sorted["Date_fmt"], "Attribute 1 visible": "visible", "Attribute 1 global": 1,
-        "Attribute 2 name": "Location", "Attribute 2 value(s)": "Virtual", "Attribute 2 visible": "visible", "Attribute 2 global": 1,
-        "Attribute 3 name": "Time", "Attribute 3 value(s)": df_sorted.apply(lambda r: f"{r['Course Start Time']}-{r['Course End Time']} {r['Time Zone']}", axis=1), "Attribute 3 visible": "visible", "Attribute 3 global": 1,
-        "Meta: outline": df_sorted["Formatted_Agenda"], "Meta: days": df_sorted[col_dur], "Meta: location": "Virtual",
-        "Meta: overview": df_sorted["Target Audience"], "Meta: objectives": df_sorted["Formatted_Objectives"],
-        "Meta: prerequisites": df_sorted["Formatted_Prerequisites"], "Meta: agenda": df_sorted["Formatted_Agenda"],
-    })
-    # Combine & order
-    combined = pd.concat([woo_parent, woo_child], ignore_index=True)
-    column_order = [
-        "Type","SKU","Name","Published","Visibility in catalog","Short description","Description","Tax status","In stock?","Stock","Sold individually?","Regular price","Categories","Images","Parent","Brands", "Attribute 1 name","Attribute 1 value(s)","Attribute 1 visible","Attribute 1 global","Attribute 2 name","Attribute 2 value(s)","Attribute 2 visible","Attribute 2 global","Attribute 3 name","Attribute 3 value(s)","Attribute 3 visible","Attribute 3 global","Meta: outline","Meta: days","Meta: location","Meta: overview","Meta: objectives","Meta: prerequisites","Meta: agenda"
-    ]
-    combined = combined[column_order]
-    buf = BytesIO(); combined.to_csv(buf, index=False, encoding="utf-8-sig"); buf.seek(0); return buf
-
-# ---------------------------------------------------------------------------
-# Gradio wrapper
-# ---------------------------------------------------------------------------
+    dsorted=df.sort_values(["Course ID","Course Start Date"])
+    d_agg = dsorted.groupby("Course ID")["Date_fmt"].apply(lambda s: ",".join(s.dropna().unique())).reset_index(name="Dates")
+    t_agg = dsorted.groupby("Course ID",group_keys=False).apply(lambda g: ",".join(f"{st}-{et} {tz}" for st,et,tz in zip(g["Course Start Time"],g["Course End Time"],g["Time Zone"]))).reset_index(name="Times")
+    parents = dsorted.drop_duplicates("Course ID").merge(d_agg).merge(t_agg)
 
-def process_file(upload):
-    return process_woocommerce_data_in_memory(upload)
+    parent = pd.DataFrame({
+        "Type":"variable","SKU":parents["Course ID"],"Name":parents["Course Name"],"Published":1,"Visibility in catalog":"visible","Short description":parents["Short_Description"],"Description":parents["Condensed_Description"],"Tax status":"taxable","In stock?":1,"Stock":1,"Sold individually?":1,"Regular price":parents["SRP Pricing"].replace("[\\$,]","",regex=True),"Categories":"courses","Images":parents["Vendor"].map(logos).fillna(""),"Parent":"","Brands":parents["Vendor"],"Attribute 1 name":"Date","Attribute 1 value(s)":parents["Dates"],"Attribute 1 visible":"visible","Attribute 1 global":1,"Attribute 2 name":"Location","Attribute 2 value(s)":"Virtual","Attribute 2 visible":"visible","Attribute 2 global":1,"Attribute 3 name":"Time","Attribute 3 value(s)":parents["Times"],"Attribute 3 visible":"visible","Attribute 3 global":1,"Meta: outline":parents["Formatted_Agenda"],"Meta: days":parents[dur],"Meta: location":"Virtual","Meta: overview":parents["Target Audience"],"Meta: objectives":parents["Formatted_Objectives"],"Meta: prerequisites":parents["Formatted_Prerequisites"],"Meta: agenda":parents["Formatted_Agenda"]})
+    child = pd.DataFrame({
+        "Type":"variation, virtual","SKU":dsorted[sid].astype(str).str.strip(),"Name":dsorted["Course Name"],"Published":1,"Visibility in catalog":"visible","Short description":dsorted["Short_Description"],"Description":dsorted["Condensed_Description"],"Tax status":"taxable","In stock?":1,"Stock":1,"Sold individually?":1,"Regular price":dsorted["SRP Pricing"].replace("[\\$,]","",regex=True),"Categories":"courses","Images":dsorted["Vendor"].map(logos).fillna(""),"Parent":dsorted["Course ID"],"Brands":dsorted["Vendor"],"Attribute 1 name":"Date","Attribute 1 value(s)":dsorted["Date_fmt"],"Attribute 1 visible":"visible","Attribute 1 global":1,"Attribute 2 name":"Location","Attribute 2 value(s)":"Virtual","Attribute 2 visible":"visible","Attribute 2 global":1,"Attribute 3 name":"Time","Attribute 3 value(s)":dsorted.apply(lambda r:f"{r['Course Start Time']}-{r['Course End Time']} {r['Time Zone']}",axis=1),"Attribute 3 visible":"visible","Attribute 3 global":1,"Meta: outline":dsorted["Formatted_Agenda"],"Meta: days":dsorted[dur],"Meta: location":"Virtual","Meta: overview":dsorted["Target Audience"],"Meta: objectives":dsorted["Formatted_Objectives"],"Meta: prerequisites":dsorted["Formatted_Prerequisites"],"Meta: agenda":dsorted["Formatted_Agenda"]})
+
+    all_rows = pd.concat([parent,child],ignore_index=True)
+    order=["Type","SKU","Name","Published","Visibility in catalog","Short description","Description","Tax status","In stock?","Stock","Sold individually?","Regular price","Categories","Images","Parent","Brands","Attribute 1 name","Attribute 1 value(s)","Attribute 1 visible","Attribute 1 global","Attribute 2 name","Attribute 2 value(s)","Attribute 2 visible","Attribute 2 global","Attribute 3 name","Attribute 3 value(s)","Attribute 3 visible","Attribute 3 global","Meta: outline","Meta: days","Meta: location","Meta: overview","Meta: objectives","Meta: prerequisites","Meta: agenda"]
+    out=BytesIO(); all_rows[order].to_csv(out,index=False,encoding="utf-8-sig"); out.seek(0); return out
 
+# -------- Gradio wrappers ----------------------------------------------------
+
+def process_file(upload):
+    csv_bytes = convert(upload.name)
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".csv") as tmp:
+        tmp.write(csv_bytes.getvalue()); path = tmp.name
+    return path
 
-interface = gr.Interface(
+ui = gr.Interface(
     fn=process_file,
-    inputs=gr.File(label="Upload NetCom CSV", file_types=[".csv"]),
+    inputs=gr.File(label="Upload NetCom CSV / Excel", file_types=[".csv",".xlsx",".xls"]),
     outputs=gr.File(label="Download WooCommerce CSV"),
     title="NetCom → WooCommerce CSV Processor",
-    description="Upload a NetCom Reseller Schedule CSV to generate a WooCommerce‑import CSV (Try 1 schema).",
+    description="Upload NetCom schedule (.csv/.xlsx) to get the Try 1‑formatted WooCommerce CSV.",
     analytics_enabled=False,
 )
 
 if __name__ == "__main__":
     if not os.getenv("OPENAI_API_KEY"):
-        print("⚠️  OPENAI_API_KEY not set – AI paraphrasing will error out")
-    interface.launch()
+        print("⚠️  OPENAI_API_KEY not set – AI features will error")
+    ui.launch()