Rename src/streamlit_app.py to src/app.py

src/app.py

@@ -0,0 +1,114 @@
+import streamlit as st
+import requests
+import uuid
+import time
+
+# --- CONFIGURATION ---
+API_URL = "https://lightrt-text2sql-backend.hf.space" # Your FastAPI server URL
+
+st.set_page_config(page_title="Text@SQL Agent", page_icon="🤖", layout="centered")
+
+# --- SESSION STATE INITIALIZATION ---
+# This ensures variables survive when Streamlit re-renders the page
+if "thread_id" not in st.session_state:
+    st.session_state.thread_id = str(uuid.uuid4()) # Unique session ID for LangGraph memory
+if "user_id" not in st.session_state:
+    st.session_state.user_id = "tenant_" + str(uuid.uuid4())[:8]
+if "is_db_connected" not in st.session_state:
+    st.session_state.is_db_connected = False
+if "connection_url" not in st.session_state:
+    st.session_state.connection_url = ""
+if "chat_history" not in st.session_state:
+    st.session_state.chat_history = []
+
+# --- SIDEBAR: DATABASE CONNECTION ---
+with st.sidebar:
+    st.header("⚙️ Database Setup")
+    
+    # If already connected, disable the input to enforce ONE database connection
+    db_input = st.text_input(
+        "Enter Database URL:",
+        disabled=st.session_state.is_db_connected
+    )
+    
+    if not st.session_state.is_db_connected:
+        if st.button("Connect & Initialize", type="primary", use_container_width=True):
+            if not db_input:
+                st.error("Please enter a valid URL.")
+            else:
+                with st.spinner("Building embeddings and initializing agent..."):
+                    try:
+                        # 1. Hit your FastAPI upload endpoint
+                        payload = {"connection_url": db_input, "user_id": st.session_state.user_id}
+                        response = requests.post(f"{API_URL}/upload_url", json=payload)
+                        
+                        if response.status_code == 200:
+                            # 2. Lock the connection and unlock the chat
+                            st.session_state.is_db_connected = True
+                            st.session_state.connection_url = db_input
+                            
+                            # Because your FastAPI upload uses BackgroundTasks, it returns instantly. 
+                            # We add a 2-second UI buffer here so the Qdrant embeddings have time to finish 
+                            # before the user fires off their first chat question.
+                            time.sleep(15) 
+                            
+                            st.success("Database connected securely!")
+                            st.rerun() # Refresh UI to unlock the chat window
+                        else:
+                            st.error(f"Failed to connect: {response.text}")
+                    except requests.exceptions.ConnectionError:
+                        st.error("🚨 Cannot connect to backend. Is FastAPI running?")
+    else:
+        st.success("✅ Connected to Database")
+        st.caption(f"URL: {st.session_state.connection_url}")
+        
+        # Add a reset button just in case they want to start completely over
+        if st.button("Disconnect & Reset", use_container_width=True):
+            st.session_state.clear()
+            st.rerun()
+
+# --- MAIN CHAT INTERFACE ---
+st.title("🗣️ Text2SQL Agent")
+
+# The Lock: Do not render the chat if DB is not connected
+if not st.session_state.is_db_connected:
+    st.info("👈 Please connect your database in the sidebar to begin analyzing data.")
+else:
+    # 1. Display previous chat messages from session state
+    for msg in st.session_state.chat_history:
+        with st.chat_message(msg["role"]):
+            st.markdown(msg["content"])
+
+    # 2. The Chat Input box
+    if user_query := st.chat_input("Ask a question about your data..."):
+        
+        # Immediately display the user's question in the UI
+        st.session_state.chat_history.append({"role": "user", "content": user_query})
+        with st.chat_message("user"):
+            st.markdown(user_query)
+
+        # 3. Call the LangGraph Backend
+        with st.chat_message("assistant"):
+            with st.spinner("Analyzing schema and generating SQL..."):
+                try:
+                    payload = {
+                        "message": user_query,
+                        "thread_id": st.session_state.thread_id,
+                        "user_id": st.session_state.user_id,
+                        "connection_url": st.session_state.connection_url
+                    }
+                    
+                    response = requests.post(f"{API_URL}/chat", json=payload)
+                    
+                    if response.status_code == 200:
+                        # Extract the final_result from your FastAPI JSON response
+                        answer = response.json().get("response", "No response found.")
+                        st.markdown(answer)
+                        
+                        # Save the assistant's answer to the UI history
+                        st.session_state.chat_history.append({"role": "assistant", "content": answer})
+                    else:
+                        st.error(f"Agent Error: {response.text}")
+                        
+                except requests.exceptions.ConnectionError:
+                    st.error("🚨 Connection dropped. Ensure FastAPI is running.")

src/streamlit_app.py

@@ -1,40 +0,0 @@
-import altair as alt
-import numpy as np
-import pandas as pd
-import streamlit as st
-
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))