Add FoodHub chatbot UI

app.py

@@ -1,69 +1,97 @@
+import os
+import re
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-
-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")
-
-    messages = [{"role": "system", "content": system_message}]
-
-    messages.extend(history)
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    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
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-chatbot = gr.ChatInterface(
-    respond,
-    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)",
-        ),
-    ],
+
+from langchain_groq import ChatGroq
+from langchain.sql_database import SQLDatabase
+from langchain.agents.agent_toolkits import SQLDatabaseToolkit
+from langchain.agents import create_sql_agent
+from langchain_core.messages import SystemMessage
+
+
+# --------------------------
+# CONFIG
+# --------------------------
+DB_PATH = "customer_orders.db"
+MODEL_NAME = "meta-llama/llama-4-scout-17b-16e-instruct"
+
+groq_api_key = os.getenv("GROQ_API_KEY")
+if not groq_api_key:
+    raise ValueError("GROQ_API_KEY not found. Add it in Hugging Face Space → Settings → Secrets.")
+
+llm = ChatGroq(
+    model=MODEL_NAME,
+    temperature=0,
+    max_tokens=300,
+    groq_api_key=groq_api_key
 )
 
-with gr.Blocks() as demo:
-    with gr.Sidebar():
-        gr.LoginButton()
-    chatbot.render()
+# --------------------------
+# LOAD DB + SQL AGENT
+# --------------------------
+db = SQLDatabase.from_uri(f"sqlite:///{DB_PATH}")
+toolkit = SQLDatabaseToolkit(db=db, llm=llm)
+
+system_message = SystemMessage(content="""
+You are FoodHub support assistant with database access.
+
+Rules:
+1) Only answer using information from the database.
+2) Only retrieve a SINGLE order when a valid order_id is provided.
+3) If no order_id is provided, ask politely for the Order ID.
+4) Do not reveal bulk order data or all orders.
+5) Replies must be concise, polite and formal.
+""")
+
+sql_agent = create_sql_agent(
+    llm=llm,
+    toolkit=toolkit,
+    verbose=False,
+    system_message=system_message
+)
+
+VALID_ORDER_PATTERN = r"^O\d{5}$"
+
 
+def extract_order_id(text: str):
+    match = re.search(r"\bO\d{5}\b", text.upper())
+    return match.group(0) if match else None
+
+
+def chat_fn(user_message, history):
+    order_id = extract_order_id(user_message)
+
+    # If user didn't provide order_id, ask
+    if not order_id:
+        return "Please provide your Order ID (example: O12488)."
+
+    # Validate order id format
+    if not re.match(VALID_ORDER_PATTERN, order_id):
+        return "Order ID format seems invalid. Please enter like O12488."
+
+    # Ask SQL agent for that order only
+    query = f"Retrieve all columns for order_id {order_id}"
+    try:
+        result = sql_agent.invoke(query)
+        content = result.get("output", str(result))
+
+        # Convert raw output to user-friendly response (simple formatting)
+        # You can customize this further.
+        return f"Here are the details for Order ID {order_id}:\n\n{content}"
+
+    except Exception as e:
+        return f"Sorry, I couldn't fetch that order right now. Error: {e}"
+
+
+# --------------------------
+# GRADIO UI
+# --------------------------
+demo = gr.ChatInterface(
+    fn=chat_fn,
+    title="FoodHub – AI Powered Food Delivery Chatbot",
+    description="Ask order queries like: 'Where is my order O12488?' or 'Cancel order O12487'.",
+    theme="soft"
+)
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()

requirements.txt

@@ -0,0 +1,8 @@
+gradio
+langchain
+langchain-community
+langchain-core
+langchain-groq
+sqlalchemy
+pandas
+numpy