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app.py

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+import os, sys, time, json, sqlite3, textwrap, requests
+import gradio as gr
+
+# -------------------------------------------------
+# 1. CONFIGURATION
+# -------------------------------------------------
+MODEL_ID = "gpt2"                                   # always exists; later swap for sqlcoder
+API_URL  = f"https://api-inference.huggingface.co/models/{MODEL_ID}"
+
+HF_TOKEN = os.getenv("HF_TOKEN")
+if not HF_TOKEN:
+    raise RuntimeError(
+        "HF_TOKEN not found. Go to Space → Settings → Secrets and add it."
+    )
+
+HEADERS = {"Authorization": f"Bearer {HF_TOKEN}"}
+
+DB_PATH     = "company.db"
+SCHEMA_FILE = "schema.sql"
+
+# -------------------------------------------------
+# 2. UTIL: BUILD DB IF NEEDED
+# -------------------------------------------------
+def create_db_if_needed():
+    if os.path.exists(DB_PATH):
+        return
+    with open(SCHEMA_FILE) as f, sqlite3.connect(DB_PATH) as conn:
+        conn.executescript(f.read())
+
+# -------------------------------------------------
+# 3. UTIL: CALL HF MODEL (with token debug)
+# -------------------------------------------------
+def nlp_to_sql(question: str, schema_ddl: str) -> str:
+    prompt = textwrap.dedent(f"""
+        Translate the following natural language question into a single valid SQLite SQL query.
+
+        ### Schema
+        {schema_ddl}
+
+        ### Question
+        {question}
+
+        ### SQL
+    """)
+    payload = {"inputs": prompt, "parameters": {"max_new_tokens": 64}}
+
+    # ---------- DEBUG ----------
+    print("=" * 60, file=sys.stderr)
+    print("DEBUG URL:", API_URL, file=sys.stderr)
+    print("DEBUG token starts with:", HF_TOKEN[:8], file=sys.stderr)
+    # ---------------------------
+
+    try:
+        r = requests.post(API_URL, headers=HEADERS, json=payload, timeout=60)
+    except Exception as e:
+        return f"[ConnErr] {e}"
+
+    # ---------- MORE DEBUG ----------
+    print("DEBUG status:", r.status_code, file=sys.stderr)
+    print("DEBUG first 200 bytes:", r.text[:200], file=sys.stderr)
+    print("=" * 60, file=sys.stderr)
+    # -------------------------------
+
+    if r.status_code != 200:
+        return f"[API {r.status_code}] {r.text[:100]}"
+
+    try:
+        generated = r.json()[0]["generated_text"]
+    except Exception as e:
+        return f"[JSONErr] {e}"
+
+    return generated.split("### SQL")[-1].strip() or "[Empty SQL]"
+
+# -------------------------------------------------
+# 4. PIPELINE
+# -------------------------------------------------
+def run_pipeline(query: str):
+    t0, trace = time.time(), []
+    create_db_if_needed()
+
+    with open(SCHEMA_FILE) as f:
+        schema = f.read()
+    trace.append(("Schema", "loaded"))
+
+    sql = nlp_to_sql(query, schema)
+    trace.append(("LLM", sql))
+
+    try:
+        with sqlite3.connect(DB_PATH) as conn:
+            cur = conn.execute(sql)
+            rows = cur.fetchall()
+            cols = [d[0] for d in cur.description] if cur.description else []
+        result = {"columns": cols, "rows": rows}
+        trace.append(("Exec", f"{len(rows)} rows"))
+    except Exception as e:
+        result = {"error": str(e)}
+        trace.append(("Exec error", str(e)))
+
+    trace.append(("Time", f"{time.time() - t0:.2f}s"))
+    return sql, json.dumps(result, indent=2), "\n".join(f"{s}: {m}" for s, m in trace)
+
+# -------------------------------------------------
+# 5. UI
+# -------------------------------------------------
+with gr.Blocks(title="Debug HF Token & API") as demo:
+    gr.Markdown("### Debugging HF TOKEN → API (uses GPT-2)")
+    q = gr.Textbox(label="Question", placeholder="e.g., How many employees?")
+    with gr.Row():
+        sql_box = gr.Code(label="SQL / debug output")
+        res_box = gr.Code(label="Result / error")
+    trace_box = gr.Textbox(label="Trace")
+    btn = gr.Button("Run")
+    btn.click(run_pipeline, q, [sql_box, res_box, trace_box])
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio==4.28.3
+requests
+sqlite-utils
+python-dotenv

schema.sql

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+DROP TABLE IF EXISTS employees;
+CREATE TABLE employees (
+  emp_id     INTEGER PRIMARY KEY,
+  name       TEXT,
+  department TEXT,
+  hire_date  DATE,
+  salary     INTEGER
+);
+
+INSERT INTO employees (name, department, hire_date, salary) VALUES
+  ('Alice',  'Sales',      '2022-01-10', 95000),
+  ('Bob',    'Engineering','2023-03-14',115000),
+  ('Carlos', 'Finance',    '2021-07-22',100000),
+  ('Dana',   'Engineering','2020-11-05',125000);