Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,107 @@
-import altair as alt
-import numpy as np
-import pandas as pd
+import os
+
+# Force Streamlit to use /tmp for config, cache, metrics
+os.environ["HOME"] = "/tmp"
+os.environ["XDG_CONFIG_HOME"] = "/tmp"
+os.environ["XDG_CACHE_HOME"] = "/tmp"
+os.environ["STREAMLIT_BROWSER_GATHERUSAGESTATS"] = "false"
+
+# Create /tmp/.streamlit manually so Streamlit doesn't try to write to /
+os.makedirs("/tmp/.streamlit", exist_ok=True)
+
+# --- now import the rest ---
+import json
+from datetime import datetime
 import streamlit as st
+from supabase import create_client
+from openai import OpenAI
 
-"""
-# Welcome to Streamlit!
+# --- env vars ---
+SUPABASE_URL = os.environ["SUPABASE_URL"]
+SUPABASE_SERVICE_ROLE_KEY = os.environ["SUPABASE_SERVICE_ROLE_KEY"]
+OPENAI_API_KEY = os.environ["OPENAI_API_KEY"]
+
+sb = create_client(SUPABASE_URL, SUPABASE_SERVICE_ROLE_KEY)
+client = OpenAI(api_key=OPENAI_API_KEY)
+
+# --- UI ---
+st.set_page_config(page_title="Email → LLM → Supabase Demo", layout="centered")
+st.title("📧 Email → 🤖 LLM → 📦 Supabase")
+st.caption("Paste an email, classify with LLM, store in Supabase, approve or decline the action.")
 
-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).
+raw = st.text_area("Email body", height=200, placeholder="Paste email text here…")
+col1, col2 = st.columns(2)
+with col1:
+    subject = st.text_input("Subject (optional)")
+with col2:
+    sender = st.text_input("From (optional)")
 
-In the meantime, below is an example of what you can do with just a few lines of code:
+# --- helpers ---
+def classify_email(email_text: str) -> dict:
+    prompt = f"""
+Classify the email as one of: offer_update, rental_request, general.
+Return STRICT JSON: {{"label":"...","confidence":0.0,"suggestion":"..."}}
+Email:
+{email_text}
 """
+    resp = client.chat.completions.create(
+        model="gpt-4o-mini",
+        temperature=0.2,
+        messages=[{"role": "user", "content": prompt}],
+    )
+    text = resp.choices[0].message.content or "{}"
+    try:
+        start, end = text.find("{"), text.rfind("}") + 1
+        return json.loads(text[start:end])
+    except Exception:
+        return {"label": "general", "confidence": 0.6, "suggestion": "Acknowledge receipt."}
+
+def insert_email(raw_text, subject, from_addr):
+    res = sb.table("emails").insert(
+        {"raw_text": raw_text, "subject": subject, "from_addr": from_addr}
+    ).execute()
+    return res.data[0]
+
+def insert_classification(email_id, cls):
+    sb.table("classifications").insert({
+        "email_id": email_id,
+        "label": cls["label"],
+        "confidence": cls["confidence"],
+        "suggestion": cls["suggestion"],
+    }).execute()
+
+def approve_action(email_id, suggestion):
+    sb.table("action_log").insert({
+        "email_id": email_id,
+        "action": suggestion,
+        "approved": True,
+        "approved_at": datetime.utcnow().isoformat(),
+    }).execute()
+
+# --- flow ---
+if st.button("🔎 Classify and Save", type="primary", disabled=not raw.strip()):
+    try:
+        email_row = insert_email(raw, subject, sender)
+        cls = classify_email(raw)
+        insert_classification(email_row["id"], cls)
+        st.session_state["current"] = {"email": email_row, "cls": cls}
+        st.success("✅ Email classified and saved.")
+    except Exception as e:
+        st.error(f"Error: {e}")
+
+current = st.session_state.get("current")
+if current:
+    st.subheader("Result")
+    st.write(f"**Label:** {current['cls']['label']}")
+    st.write(f"**Confidence:** {current['cls']['confidence']:.2f}")
+    st.write("**Suggested action:**")
+    st.code(current["cls"]["suggestion"])
 
-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])),
-    ))
+    if st.button("👍 Approve"):
+        try:
+            approve_action(current["email"]["id"], current["cls"]["suggestion"])
+            st.success("Approved and logged.")
+        except Exception as e:
+            st.error(f"Error approving: {e}")
+    if st.button("👎 Decline"):
+        st.info("Declined. No action logged.")