Upload streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,140 @@
-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])),
-    ))
+import streamlit as st
+from langchain_community.utilities import SQLDatabase
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_openai import ChatOpenAI
+import os
+import re
+
+# === Konfigurasi API Hugging Face Inference API ===
+
+HF_TOKEN = os.environ.get("HF_TOKEN", "")  # simpan secret via HF Spaces
+os.environ["OPENAI_API_KEY"] = HF_TOKEN
+HF_BASE_URL = "https://api-inference.huggingface.com/v1"
+
+# Fungsi koneksi database
+def connectDatabase(username, port, host, password, database):
+    try:
+        db_uri = f"postgresql://{username}:{password}@{host}:{port}/{database}"
+        st.session_state.db = SQLDatabase.from_uri(db_uri)
+        st.success("✅ Database connected successfully.")
+    except Exception as e:
+        st.session_state.db = None
+        st.error(f"❌ Failed to connect to database:\n{e}")
+
+# Fungsi menjalankan query SQL
+def runQuery(query):
+    if "db" in st.session_state and st.session_state.db:
+        try:
+            return st.session_state.db.run(query)
+        except Exception as e:
+            return f"❌ Query execution failed: {e}"
+    return "❌ Please connect to database first."
+
+# Ambil skema database
+def getDatabaseSchema():
+    if "db" in st.session_state and st.session_state.db:
+        try:
+            return st.session_state.db.get_table_info()
+        except Exception as e:
+            return f"❌ Failed to fetch schema: {e}"
+    return "❌ Please connect to database first."
+
+# Prompt untuk menghasilkan SQL
+def getQueryFromLLM(question):
+    template = """
+                Below is the schema of a PostgreSQL database. Read the schema carefully and answer the user's question using a valid SQL query. Be careful with table and column names (case-sensitive). Only provide the SQL query, and nothing else.
+
+                Schema:
+                {schema}
+
+                Sekarang giliran Anda:
+                Question: {question}
+                SQL query:
+                """
+    try:
+        schema = getDatabaseSchema()[:3000]
+        prompt = ChatPromptTemplate.from_template(template)
+        chain = prompt | llm
+        response = chain.invoke({"question": question, "schema": schema})
+        query_only = re.sub(r"<think>.*?</think>", "", response.content, flags=re.DOTALL).strip()
+        return query_only
+    except Exception as e:
+        st.error(f"❌ Model gagal merespons: {e}")
+        return "Gagal membuat query SQL."
+
+# Prompt untuk menjelaskan hasil
+def getResponseForQueryResult(question, query, result):
+    template = """
+            Berdasarkan pertanyaan, SQL query, dan hasilnya, berikan jawaban dalam bahasa alami. Gunakan bahasa Indonesia jika pertanyaannya dalam bahasa Indonesia.
+
+            Schema:
+            {schema}
+
+            Pertanyaan: {question}
+            SQL query: {query}
+            Hasil: {result}
+            Jawaban:
+            """
+    prompt2 = ChatPromptTemplate.from_template(template)
+    chain2 = prompt2 | llm
+    response = chain2.invoke({
+        "question": question,
+        "schema": getDatabaseSchema(),
+        "query": query,
+        "result": result
+    })
+    return response.content.strip()
+
+# Konfigurasi halaman
+st.set_page_config(page_title="Chat with PostgreSQL DB", page_icon="🧠", layout="centered")
+st.title("🧠 Chat with your Database")
+
+# Inisialisasi state
+if "chat" not in st.session_state:
+    st.session_state.chat = []
+
+# Sidebar
+with st.sidebar:
+    st.subheader("🔗 Koneksi Database")
+    host = st.text_input("Host", value="aws-0-ap-southeast-1.pooler.supabase.com")
+    port = st.text_input("Port", value="6543")
+    username = st.text_input("Username", value="postgres")
+    password = st.text_input("Password", type="password", value="password")
+    database = st.text_input("Database", value="postgres")
+    if st.button("Connect"):
+        connectDatabase(username, port, host, password, database)
+
+# Inisialisasi LLM
+llm = ChatOpenAI(
+    model="qwen/qwen3-1.7b",
+    base_url=HF_BASE_URL,
+    api_key=HF_TOKEN,
+    temperature=0
+)
+
+# Input pertanyaan
+question = st.chat_input("Tanyakan sesuatu tentang database Anda...")
+
+# Proses pertanyaan
+if question:
+    if "db" not in st.session_state or st.session_state.db is None:
+        st.error("❌ Please connect to database first.")
+    else:
+        st.session_state.chat.append({"role": "user", "content": question})
+
+        with st.spinner("💡 Sedang berpikir..."):
+            query = getQueryFromLLM(question)
+            result = runQuery(query)
+            response = getResponseForQueryResult(question, query, result)
+
+        st.session_state.chat.append({"role": "assistant", "content": response})
+
+        with st.expander("🧾 Generated SQL Query"):
+            st.code(query, language="sql")
+
+        with st.expander("📊 Raw Result"):
+            st.write(result)
+
+# Tampilkan chat
+for chat in st.session_state.chat:
+    st.chat_message(chat["role"]).markdown(chat["content"])