Update app.py

app.py

@@ -1,70 +1,110 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
+import sqlite3
+import torch
+import os
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
 
+# --- TEIL 1: Die Dummy-Datenbank ---
+DB_PATH = "dummy_database.db"
 
-def respond(
-    message,
-    history: list[dict[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-    hf_token: gr.OAuthToken,
-):
-    """
-    For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-    """
-    client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b")
+def setup_db():
+    """Erstellt die Datenbank bei jedem Neustart frisch (ideal für Demos)"""
+    if os.path.exists(DB_PATH):
+        os.remove(DB_PATH) # Aufräumen für sauberen Start
+        
+    conn = sqlite3.connect(DB_PATH)
+    cursor = conn.cursor()
+    cursor.execute("""
+    CREATE TABLE employees (
+        id INTEGER PRIMARY KEY,
+        name TEXT,
+        department TEXT,
+        salary INTEGER,
+        hire_date DATE
+    )
+    """)
+    employees = [
+        (1, 'Alice Smith', 'Sales', 55000, '2021-01-15'),
+        (2, 'Bob Jones', 'Engineering', 85000, '2020-03-10'),
+        (3, 'Charlie Brown', 'Sales', 48000, '2022-06-23'),
+        (4, 'Diana Prince', 'Engineering', 92000, '2019-11-05'),
+        (5, 'Evan Wright', 'HR', 45000, '2021-09-30')
+    ]
+    cursor.executemany('INSERT INTO employees VALUES (?,?,?,?,?)', employees)
+    conn.commit()
+    conn.close()
+    print("✅ Datenbank initialisiert.")
 
-    messages = [{"role": "system", "content": system_message}]
+# --- TEIL 2: Der Agent ---
+class SQLAgent:
+    def __init__(self):
+        print("⏳ Lade Modell (CPU)... das dauert ca. 1 Minute...")
+        BASE_MODEL = "Qwen/Qwen2.5-1.5B-Instruct"
+        ADAPTER_ID = "manuelaschrittwieser/Qwen2.5-SQL-Assistant-Prod" 
 
-    messages.extend(history)
+        self.tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
+        # WICHTIG: Auf CPU nutzen wir float32 statt 4-bit, da stabiler
+        base_model = AutoModelForCausalLM.from_pretrained(
+            BASE_MODEL, 
+            device_map="cpu", 
+            torch_dtype=torch.float32
+        )
+        self.model = PeftModel.from_pretrained(base_model, ADAPTER_ID)
 
-    messages.append({"role": "user", "content": message})
+    def process_query(self, user_question):
+        # 1. SQL Generieren
+        schema = "CREATE TABLE employees (id INTEGER, name TEXT, department TEXT, salary INTEGER, hire_date DATE)"
+        messages = [
+            {"role": "system", "content": "You are a SQL expert. Output only the SQL query."},
+            {"role": "user", "content": f"{schema}\nQuestion: {user_question}"}
+        ]
+        prompt = self.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+        inputs = self.tokenizer(prompt, return_tensors="pt") # Kein .to("cuda") da CPU
+        
+        with torch.no_grad():
+            outputs = self.model.generate(**inputs, max_new_tokens=100)
+            
+        full_text = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+        if "assistant" in full_text:
+            sql_query = full_text.split("assistant")[-1].strip()
+        else:
+            sql_query = full_text
 
-    response = ""
+        # 2. SQL Ausführen
+        try:
+            conn = sqlite3.connect(DB_PATH)
+            cursor = conn.cursor()
+            cursor.execute(sql_query)
+            results = cursor.fetchall()
+            conn.close()
+            
+            # Formatierung der Antwort
+            return f"🧠 Gedanke (SQL):\n{sql_query}\n\n📊 Ergebnis aus Datenbank:\n{results}"
+        except Exception as e:
+            return f"❌ Fehler: {e}\n\nVersuchter SQL: {sql_query}"
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        choices = message.choices
-        token = ""
-        if len(choices) and choices[0].delta.content:
-            token = choices[0].delta.content
+# Initialisierung beim Start des Servers
+setup_db()
+agent = SQLAgent()
 
-        response += token
-        yield response
+# --- TEIL 3: Die UI (Gradio Chat Interface) ---
+def chat_response(message, history):
+    return agent.process_query(message)
 
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
+description = """
+# 🤖 SQL Agent 
+This agent translates your questions into SQL and **executes them directly on a test database**.
+* Table: `employees` (name, department, salary, hire_date)
+* Try it: "Who earns more than 80000?"
 """
-chatbot = gr.ChatInterface(
-    respond,
-    type="messages",
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
-with gr.Blocks() as demo:
-    with gr.Sidebar():
-        gr.LoginButton()
-    chatbot.render()
 
+demo = gr.ChatInterface(
+    fn=chat_response,
+    title="Autonomous SQL Agent",
+    description=description,
+    examples=["Show me all employees in Sales.", "Who earns the most?", "Count the employees in Engineering."],
+    type="messages" 
+)
 
-if __name__ == "__main__":
-    demo.launch()
+demo.launch()