Update app.py

app.py

@@ -1,16 +1,12 @@
 import os
 import re
 import json
+import sqlite3
 from typing import Any, Optional, Dict
 import streamlit as st
 
 from langchain_groq import ChatGroq
 from langchain_core.prompts import ChatPromptTemplate
-from langchain_core.messages import SystemMessage
-from langchain_community.utilities import SQLDatabase
-from langchain_community.agent_toolkits import SQLDatabaseToolkit
-from langchain_community.agent_toolkits.sql.base import create_sql_agent
-
 
 # ============================================================
 # CONFIG
@@ -18,56 +14,28 @@ from langchain_community.agent_toolkits.sql.base import create_sql_agent
 DB_PATH = "customer_orders.db"
 MODEL_NAME = "meta-llama/llama-4-scout-17b-16e-instruct"
 
-
 # ============================================================
-# LLM & AGENT INIT
+# LLM INIT
 # ============================================================
 @st.cache_resource
 def init_llm():
-    groq_key = os.getenv("GROQ_API_KEY")  # HF Spaces secrets -> env vars
+    groq_key = os.getenv("GROQ_API_KEY")
     if not groq_key:
-        st.error("Missing GROQ_API_KEY. Add it in Hugging Face Space → Settings → Secrets.")
+        st.error("Missing GROQ_API_KEY. Add it in Hugging Face → Settings → Secrets.")
         st.stop()
 
-    # Most compatible approach across langchain_groq versions
     os.environ["GROQ_API_KEY"] = groq_key
 
-    return ChatGroq(model=MODEL_NAME, temperature=0, max_tokens=400, timeout=30, max_retries=2)
-
-
-@st.cache_resource
-def init_db_and_agent():
-    if not os.path.exists(DB_PATH):
-        st.error(f"Database file '{DB_PATH}' not found. Upload it to the Space root folder.")
-        st.stop()
-
-    db = SQLDatabase.from_uri(f"sqlite:///{DB_PATH}")
-    llm = init_llm()
-    toolkit = SQLDatabaseToolkit(db=db, llm=llm)
-
-    system_message = """
-You are a read-only SQL assistant for FoodHub.
-Rules:
-- Use ONLY SELECT queries (no INSERT/UPDATE/DELETE/DROP).
-- Query only the existing tables/columns.
-- When asked for an order, filter by order_id exactly.
-- Return the final answer as a valid JSON object only (no extra text).
-"""
-
-    agent = create_sql_agent(
-        llm=llm,
-        toolkit=toolkit,
-        verbose=False,
-        system_message=SystemMessage(content=system_message),
+    return ChatGroq(
+        model=MODEL_NAME,
+        temperature=0,
+        max_tokens=400,
+        timeout=30,
+        max_retries=2,
     )
 
-    return agent
-
-
-db_agent = init_db_and_agent()
 llm = init_llm()
 
-
 # ============================================================
 # HELPERS & LOGIC
 # ============================================================
@@ -82,66 +50,64 @@ def try_extract_order_id(text: str) -> Optional[str]:
 
 def fetch_order_as_json(order_id: str) -> Dict[str, Any]:
     """
-    Calls SQL agent and tries to return a dict.
-    If agent output isn't valid JSON, returns {_error: ...}.
+    ✅ FINAL FIX
+    Deterministic, read-only SQLite fetch.
+    Works the same in Colab and Hugging Face.
     """
-    prompt = f"""
-Return ONLY JSON (no explanation).
-Retrieve exactly one row from the orders table where order_id = '{order_id}'.
-JSON keys must include:
-order_id, cust_id, order_time, order_status, payment_status, item_in_order,
-preparing_eta, prepared_time, delivery_eta, delivery_time
-If not found, return: {{ "_error": "NOT_FOUND" }}.
-"""
     try:
-        res = db_agent.invoke({"input": prompt})
-        raw = (res.get("output") or "").strip()
-
-        # Try direct JSON parse
-        try:
-            parsed = json.loads(raw)
-            if isinstance(parsed, dict):
-                return parsed
-        except Exception:
-            pass
-
-        # Try to extract the first JSON object block
-        start = raw.find("{")
-        end = raw.rfind("}")
-        if start != -1 and end != -1 and end > start:
-            candidate = raw[start:end+1]
-            try:
-                parsed = json.loads(candidate)
-                if isinstance(parsed, dict):
-                    return parsed
-            except Exception:
-                pass
+        conn = sqlite3.connect(DB_PATH)
+        conn.row_factory = sqlite3.Row
+        cur = conn.cursor()
+
+        cur.execute(
+            "SELECT * FROM orders WHERE order_id = ? LIMIT 1;",
+            (order_id,)
+        )
+        row = cur.fetchone()
+        conn.close()
+
+        if row is None:
+            return {"_error": "NOT_FOUND"}
+
+        return dict(row)
 
-        return {"_error": "NOT_FOUND"}
     except Exception:
         return {"_error": "NOT_FOUND"}
 
 
 def chat_agent_response(user_query: str, session_order_id: str) -> str:
     oid = session_order_id or try_extract_order_id(user_query)
+
     if not oid:
         return "I’d be happy to help. Could you please share your Order ID (example: O12486)?"
 
     order_data = fetch_order_as_json(oid)
+
     if order_data.get("_error") == "NOT_FOUND":
-        return f"I'm sorry — I couldn't find any details for Order ID **{oid}**. Please re-check the ID and try again."
+        return f"I'm sorry — I couldn't find any details for Order ID **{oid}**. Please re-check and try again."
 
-    # Redact customer id
+    # Privacy guardrail
     if "cust_id" in order_data:
         order_data["cust_id"] = "[redacted]"
 
     prompt = ChatPromptTemplate.from_messages([
-        ("system", "You are FoodHub Support. Be polite and concise (2–4 lines). Use only the provided context."),
-        ("human", "User: {user_query}\n\nOrder Context (JSON): {context}\n\nReply:")
+        ("system",
+         "You are FoodHub customer support.\n"
+         "Write a polite, concise reply (2–4 lines).\n"
+         "Use ONLY the provided order context.\n"
+         "Do NOT expose customer identifiers.\n"),
+        ("human",
+         "User query:\n{user_query}\n\n"
+         "Order context (JSON):\n{context}\n\n"
+         "Final reply:")
     ])
 
     chain = prompt | llm
-    resp = chain.invoke({"user_query": user_query, "context": json.dumps(order_data)})
+    resp = chain.invoke({
+        "user_query": user_query,
+        "context": json.dumps(order_data)
+    })
+
     return getattr(resp, "content", str(resp))
 
 
@@ -156,12 +122,12 @@ if "messages" not in st.session_state:
 if "order_id" not in st.session_state:
     st.session_state.order_id = ""
 
-# Display history
+# Show chat history
 for msg in st.session_state.messages:
     with st.chat_message(msg["role"]):
         st.markdown(msg["content"])
 
-# User Input
+# User input
 user_text = st.chat_input("How can I help with your order?")
 
 if user_text:
@@ -169,9 +135,9 @@ if user_text:
     with st.chat_message("user"):
         st.markdown(user_text)
 
-    detected_id = try_extract_order_id(user_text)
-    if detected_id:
-        st.session_state.order_id = detected_id
+    detected = try_extract_order_id(user_text)
+    if detected:
+        st.session_state.order_id = detected
 
     with st.spinner("Checking FoodHub records..."):
         response = chat_agent_response(user_text, st.session_state.order_id)
@@ -179,3 +145,4 @@ if user_text:
     st.session_state.messages.append({"role": "assistant", "content": response})
     with st.chat_message("assistant"):
         st.markdown(response)
+