Spaces:
Sleeping
Sleeping
Update app.py
Browse files
app.py
CHANGED
@@ -1,75 +1,68 @@
|
|
1 |
-
# app.py
|
2 |
-
|
3 |
import gradio as gr
|
4 |
import pandas as pd
|
5 |
import torch
|
|
|
6 |
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
|
7 |
|
8 |
-
# 1) Load
|
9 |
df = pd.read_csv('synthetic_profit.csv')
|
|
|
|
|
10 |
|
11 |
-
# 2)
|
12 |
-
|
13 |
-
df[col] = pd.to_numeric(df[col], errors='coerce')
|
14 |
-
|
15 |
-
# 3) Build the schema description
|
16 |
-
schema_lines = [f"- {col}: {dtype.name}" for col, dtype in df.dtypes.items()]
|
17 |
-
schema_text = "Table schema:\n" + "\n".join(schema_lines)
|
18 |
-
|
19 |
-
# 4) Few-shot examples teaching SUM and AVERAGE
|
20 |
-
few_shot = """
|
21 |
-
Example 1
|
22 |
-
Q: Total profit by region?
|
23 |
-
A: Group “Profit” by “Region” and sum → EMEA: 30172183.37; APAC: 32301788.32; Latin America: 27585378.50; North America: 25473893.34
|
24 |
|
25 |
-
|
26 |
-
Q: Average profit margin for Product B in Americas?
|
27 |
-
A: Filter Product=B & Region=Americas, take mean of “ProfitMargin” → 0.18
|
28 |
-
""".strip()
|
29 |
-
|
30 |
-
# 5) Load TAPEX-WikiSQL for table-QA
|
31 |
MODEL_ID = "microsoft/tapex-base-finetuned-wikisql"
|
32 |
device = 0 if torch.cuda.is_available() else -1
|
33 |
|
34 |
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
|
35 |
model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_ID)
|
36 |
|
37 |
-
|
38 |
-
"
|
39 |
model=model,
|
40 |
tokenizer=tokenizer,
|
41 |
framework="pt",
|
42 |
-
device=device
|
|
|
43 |
)
|
44 |
|
45 |
-
#
|
46 |
def answer_profitability(question: str) -> str:
|
47 |
-
#
|
48 |
-
|
49 |
-
|
50 |
-
|
51 |
-
|
52 |
-
|
53 |
-
|
54 |
-
|
55 |
-
Q: {question}
|
56 |
-
A:"""
|
57 |
|
|
|
58 |
try:
|
59 |
-
|
60 |
-
return out.get("answer", "No answer found.")
|
61 |
except Exception as e:
|
62 |
-
return f"Error: {e}"
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
63 |
|
64 |
-
#
|
65 |
iface = gr.Interface(
|
66 |
fn=answer_profitability,
|
67 |
inputs=gr.Textbox(lines=2, placeholder="Ask a question about profitability…"),
|
68 |
outputs="text",
|
69 |
-
title="SAP Profitability Q&A (
|
70 |
description=(
|
71 |
-
"
|
72 |
-
"
|
73 |
)
|
74 |
)
|
75 |
|
|
|
|
|
|
|
1 |
import gradio as gr
|
2 |
import pandas as pd
|
3 |
import torch
|
4 |
+
import duckdb
|
5 |
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
|
6 |
|
7 |
+
# 1) Load data into DuckDB
|
8 |
df = pd.read_csv('synthetic_profit.csv')
|
9 |
+
con = duckdb.connect(':memory:')
|
10 |
+
con.register('sap', df)
|
11 |
|
12 |
+
# 2) Build a one-line schema for prompting
|
13 |
+
schema = ", ".join(df.columns) # e.g. "Region,Product,FiscalYear,...."
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
14 |
|
15 |
+
# 3) Load TAPEX-WikiSQL as a text2text generator
|
|
|
|
|
|
|
|
|
|
|
16 |
MODEL_ID = "microsoft/tapex-base-finetuned-wikisql"
|
17 |
device = 0 if torch.cuda.is_available() else -1
|
18 |
|
19 |
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
|
20 |
model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_ID)
|
21 |
|
22 |
+
sql_gen = pipeline(
|
23 |
+
"text2text-generation",
|
24 |
model=model,
|
25 |
tokenizer=tokenizer,
|
26 |
framework="pt",
|
27 |
+
device=device,
|
28 |
+
max_length=128,
|
29 |
)
|
30 |
|
31 |
+
# 4) Core QA fn: NL → SQL → execute → return result
|
32 |
def answer_profitability(question: str) -> str:
|
33 |
+
# a) Prompt TAPEX to generate SQL
|
34 |
+
prompt = (
|
35 |
+
f"-- Translate to SQL over table `sap` with columns ({schema})\n"
|
36 |
+
f"Question: {question}\n"
|
37 |
+
"SQL:"
|
38 |
+
)
|
39 |
+
sql = sql_gen(prompt)[0]['generated_text'].strip()
|
|
|
|
|
|
|
40 |
|
41 |
+
# b) Execute the generated SQL
|
42 |
try:
|
43 |
+
result_df = con.execute(sql).df()
|
|
|
44 |
except Exception as e:
|
45 |
+
return f"❌ SQL Error: {e}\n\nGenerated SQL:\n{sql}"
|
46 |
+
|
47 |
+
# c) Format the output
|
48 |
+
if result_df.empty:
|
49 |
+
return f"No rows returned.\n\nGenerated SQL:\n{sql}"
|
50 |
+
|
51 |
+
# If it's a single cell result, just return that number
|
52 |
+
if result_df.shape == (1,1):
|
53 |
+
return str(result_df.iat[0,0])
|
54 |
+
# Otherwise pretty-print the DataFrame
|
55 |
+
return result_df.to_string(index=False)
|
56 |
|
57 |
+
# 5) Gradio UI
|
58 |
iface = gr.Interface(
|
59 |
fn=answer_profitability,
|
60 |
inputs=gr.Textbox(lines=2, placeholder="Ask a question about profitability…"),
|
61 |
outputs="text",
|
62 |
+
title="SAP Profitability Q&A (SQL-Generation)",
|
63 |
description=(
|
64 |
+
"TAPEX converts your natural-language query into SQL,\n"
|
65 |
+
"then runs it via DuckDB—no hard-coded fallbacks."
|
66 |
)
|
67 |
)
|
68 |
|