Update app.py

app.py

@@ -1,8 +1,30 @@
 #!/usr/bin/env python3
 """
 ai_csv_editor_hf.py ── AI-powered CSV editor using a Hugging Face model on CPU.
+
+This version patches Gradio’s JSON‐schema introspector to skip over
+boolean schemas and avoid the "const in schema" TypeError.
 """
 
+# ─────────────────────────────────────────────────────────────────────────────
+# 0. MONKEY-PATCH for gradio_client.utils.get_type to handle bool schemas
+# ─────────────────────────────────────────────────────────────────────────────
+try:
+    import gradio_client.utils as _client_utils
+    _old_get_type = _client_utils.get_type
+    def _patched_get_type(schema):
+        # If schema is unexpectedly a bool, just return a generic "Any"
+        if isinstance(schema, bool):
+            return "Any"
+        return _old_get_type(schema)
+    _client_utils.get_type = _patched_get_type
+except ImportError:
+    # If gradio_client isn't present yet, we'll let it import later
+    pass
+
+# ─────────────────────────────────────────────────────────────────────────────
+# 1. LOAD A SMALL INSTRUCTION-FOLLOWING MODEL (CPU only)
+# ─────────────────────────────────────────────────────────────────────────────
 import json
 import tempfile
 import textwrap
@@ -13,33 +35,28 @@ import pandas as pd
 import gradio as gr
 from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
 
-# ──────────────────────────────────────────────────────────
-# 1.  LOAD A SMALL INSTRUCTION-FOLLOWING MODEL (CPU only)
-# ──────────────────────────────────────────────────────────
-MODEL_NAME  = "google/flan-t5-base"
-MAX_NEW_TOK  = 256
+MODEL_NAME   = "google/flan-t5-base"
+MAX_NEW_TOKS = 256
 TEMPERATURE  = 0.0
 
 tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
 model     = AutoModelForSeq2SeqLM.from_pretrained(
-                MODEL_NAME,
-                device_map="cpu",      # force CPU placement
-                torch_dtype="auto"
-            )
-
-# **Important change**: no `device=` argument here!
+    MODEL_NAME,
+    device_map="cpu",      # force CPU placement
+    torch_dtype="auto"
+)
 generator = pipeline(
     "text2text-generation",
     model=model,
     tokenizer=tokenizer,
-    max_new_tokens=MAX_NEW_TOK,
+    max_new_tokens=MAX_NEW_TOKS,
     temperature=TEMPERATURE,
     do_sample=False,
 )
 
-# ──────────────────────────────────────────────────────────
-# 2.  PROMPT → JSON “EDIT PLAN”
-# ──────────────────────────────────────────────────────────
+# ─────────────────────────────────────────────────────────────────────────────
+# 2. PROMPT → JSON “EDIT PLAN”
+# ─────────────────────────────────────────────────────────────────────────────
 SYSTEM_PROMPT = textwrap.dedent("""\
 You are an assistant that converts natural-language spreadsheet commands
 into JSON edit plans. Respond with ONLY valid JSON matching this schema:
@@ -72,20 +89,24 @@ into JSON edit plans. Respond with ONLY valid JSON matching this schema:
 
 def plan_from_command(cmd: str) -> Dict[str, Any]:
     prompt = f"{SYSTEM_PROMPT}\n\nUser: {cmd}\nJSON:"
-    output = generator(prompt)[0]["generated_text"]
+    output = generator(
+        prompt,
+        max_new_tokens=MAX_NEW_TOKS,
+        temperature=TEMPERATURE,
+        do_sample=False,
+    )[0]["generated_text"]
     try:
         return json.loads(output)
     except json.JSONDecodeError as e:
         raise ValueError(f"Model returned invalid JSON:\n{output}") from e
 
-# ──────────────────────────────────────────────────────────
-# 3.  DATA OPERATIONS
-# ───────────────────────────��──────────────────────────────
+# ─────────────────────────────────────────────────────────────────────────────
+# 3. DATA OPERATIONS
+# ─────────────────────────────────────────────────────────────────────────────
 def apply_action(df: pd.DataFrame,
                  uploads: Dict[str, pd.DataFrame],
                  act: Dict[str, Any]) -> pd.DataFrame:
     op = act["operation"]
-
     if op == "concat":
         sep = act.get("separator", "")
         df[act["target"]] = (
@@ -93,7 +114,6 @@ def apply_action(df: pd.DataFrame,
             .astype(str)
             .agg(sep.join, axis=1)
         )
-
     elif op in {"vlookup", "xlookup"}:
         lookup_df = uploads[act["lookup_file"]]
         right = lookup_df[[act["lookup_column"], act["return_column"]]] \
@@ -102,20 +122,17 @@ def apply_action(df: pd.DataFrame,
                 act["return_column"]: act["target"]
             })
         df = df.merge(right, on=act["lookup_value"], how="left")
-
     elif op == "sumif":
         mask = df[act["criteria_column"]] == act["criteria"]
         total = df.loc[mask, act["sum_column"]].sum()
         df[act["target"]] = total
-
     else:
         raise ValueError(f"Unsupported operation: {op}")
-
     return df
 
-# ──────────────────────────────────────────────────────────
-# 4.  GRADIO UI
-# ──────────────────────────────────────────────────────────
+# ─────────────────────────────────────────────────────────────────────────────
+# 4. GRADIO UI
+# ─────────────────────────────────────────────────────────────────────────────
 def run_editor(files: List[gr.File], command: str):
     if not files:
         return None, "⚠️ Please upload at least one CSV file.", None