Update app.py

app.py

@@ -1,72 +1,105 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
+from sqlalchemy import create_engine, Table, Column, String, Integer, Float, Text, TIMESTAMP, MetaData
+from sqlalchemy.dialects.postgresql import UUID
+from sqlalchemy import text
+from llama_index.core import SQLDatabase
+from llama_index.core.query_engine import NLSQLTableQueryEngine
+from llama_index.llms.huggingface import HuggingFaceLLM
+import logging
 
-"""
-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("HuggingFaceH4/zephyr-7b-beta")
+# Set up logging
+logging.basicConfig(level=logging.DEBUG)
+logger = logging.getLogger(__name__)
 
-demo = gr.ChatInterface(..., title="Wiser AI Assistant")
+# PostgreSQL DB connection (converted from JDBC)
+engine = create_engine("postgresql+psycopg2://postgres:password@localhost:5434/postgres")
 
+metadata_obj = MetaData()
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+# Define the machine_current_log table
+machine_current_log_table = Table(
+    "machine_current_log",
+    metadata_obj,
+    Column("mac", Text, primary_key=True),
+    Column("created_at", TIMESTAMP(timezone=True), primary_key=True),
+    Column("CT1", Float),
+    Column("CT2", Float),
+    Column("CT3", Float),
+    Column("CT_Avg", Float),
+    Column("total_current", Float),
+    Column("state", Text),
+    Column("state_duration", Integer),
+    Column("fault_status", Text),
+    Column("fw_version", Text),
+    Column("machineId", UUID),
+    Column("hi", Text),
+)
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+# Create the table
+metadata_obj.create_all(engine)
 
-    messages.append({"role": "user", "content": message})
+# Convert to TimescaleDB hypertable
+with engine.connect() as conn:
+    conn.execute(text("SELECT create_hypertable('machine_current_log', 'created_at', if_not_exists => TRUE);"))
+    print("TimescaleDB hypertable created")
+    conn.commit()
 
-    response = ""
+# Query 1: Get all MAC addresses
+print("\nQuerying all MAC addresses:")
+with engine.connect() as con:
+    rows = con.execute(text("SELECT mac from machine_current_log"))
+    for row in rows:
+        print(row)
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
+# Query 2: Get all data and count
+print("\nQuerying all data and count:")
+stmt = text("""
+    SELECT mac, created_at, CT1, CT2, CT3, CT_Avg, 
+           total_current, state, state_duration, fault_status, 
+           fw_version, machineId
+    FROM machine_current_log
+""")
 
-        response += token
-        yield response
+with engine.connect() as connection:
+    print("hello")
+    count_stmt = text("SELECT COUNT(*) FROM machine_current_log")
+    count = connection.execute(count_stmt).scalar()
+    print(f"Total number of rows in table: {count}")
+    results = connection.execute(stmt).fetchall()
+    print(results)
 
+# Set up LlamaIndex natural language querying
+sql_database = SQLDatabase(engine)
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    title="🤖 Wiser AI Assistant",
-    description="Your smart manufacturing assistant powered by Wiser Machines. Ask me anything about automation, productivity, factory operations, or how Wiser can help!",
-    additional_inputs=[
-        gr.Textbox(value="You are Wiser, an AI assistant specializing in smart manufacturing and factory automation. Respond clearly, concisely, and use real-world manufacturing examples when needed.", 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)"),
-    ],
+llm = HuggingFaceLLM(
+    model_name="HuggingFaceH4/zephyr-7b-beta",
+    context_window=2048,
+    max_new_tokens=256,
+    generate_kwargs={"temperature": 0.7, "top_p": 0.95},
 )
 
+query_engine = NLSQLTableQueryEngine(
+    sql_database=sql_database,
+    tables=["machine_current_log"],
+    llm=llm
+)
 
-demo = gr.ChatInterface(..., title="Wiser AI Assistant")
+def natural_language_query(question: str):
+    try:
+        response = query_engine.query(question)
+        return str(response)
+    except Exception as e:
+        logger.error(f"Query error: {e}")
+        return f"Error processing query: {str(e)}"
 
 if __name__ == "__main__":
-    with gr.Blocks() as demo:
-    gr.Markdown("## Welcome to Wiser AI Assistant")
-    gr.Markdown("Ask questions about factory automation, productivity, or how Wiser Machines can help streamline your operations.")
-    chat = gr.ChatInterface(
-        respond,
-        additional_inputs=[...],  # your sliders and system message
-    )
-
-    demo.launch()
+    # Natural language query examples
+    print("\nNatural Language Query Examples:")
+    questions = [
+        "What is the average CT1 reading?",
+        "Which machine has the highest total current?",
+        "Show me the latest fault status for each machine"
+    ]
+    
+    for question in questions:
+        print(f"\nQuestion: {question}")
+        print("Answer:", natural_language_query(question))