Update app.py

app.py

@@ -1,240 +1,240 @@
-import streamlit as st
-from rag_pipeline import build_rag_pipeline
-from streamlit_extras.add_vertical_space import add_vertical_space
-
-# --- PAGE CONFIG ---
-st.set_page_config(
-    page_title="💬 Personalized Lifecycle Companion",
-    page_icon="💫",
-    layout="centered",
-)
-
-# --- CUSTOM CSS (bubbles + badge) ---
-st.markdown("""
-<style>
-    .main-title { text-align: center; font-size: 2.2em; font-weight: 700; color: #4A90E2; }
-    .subtitle { text-align: center; font-size: 1.1em; color: #666; margin-bottom: 1.5em; }
-    .user-bubble {
-        background: linear-gradient(180deg, #dbe9ff, #c7ddff);
-        padding: 0.75em 1em;
-        border-radius: 12px;
-        margin: 0.5em 0 0.25em 0;
-        max-width: 80%;
-    }
-    .assistant-bubble {
-        background: #f5f7fa;
-        padding: 0.9em 1em;
-        border-radius: 12px;
-        margin: 0.25em 0 0.8em 0;
-        border: 1px solid #e1e4e8;
-        max-width: 80%;
-    }
-    .meta-badge {
-        display: inline-block;
-        font-size: 0.72em;
-        padding: 2px 8px;
-        border-radius: 999px;
-        margin-left: 8px;
-        vertical-align: middle;
-    }
-    .badge-dataset { background: #fff6ea; color: #b36b00; border: 1px solid #f0e68c; }
-    .badge-general { background: #eefcf3; color: #0a7f53; border: 1px solid #bfead4; }
-    .doc-box { background-color: #fffbe6; padding: 0.6em 0.8em; border-radius: 8px; border: 1px solid #f0e68c; margin-bottom: 0.5em; }
-    .doc-q { font-weight: 600; color: #333; }
-    .doc-a { color: #555; }
-
-    /* Make chat area scrollable and avoid hiding under input */
-    .chat-area {
-        max-height: 70vh;
-        overflow-y: auto;
-        padding-right: 8px;
-        padding-bottom: 120px; /* Space for input bar */
-    }
-
-    /* Fix the input container at the bottom */
-    .input-container {
-        position: fixed;
-        bottom: 0;
-        left: 0;
-        right: 0;
-        background-color: #ffffff;
-        padding: 1rem 2rem;
-        box-shadow: 0 -2px 10px rgba(0, 0, 0, 0.05);
-        z-index: 999;
-    }
-
-    /* Optional: make buttons line up neatly */
-    .stButton button {
-        height: 2.5em;
-    }
-
-    /* Hide Streamlit footer and hamburger for cleaner look */
-    #MainMenu {visibility: hidden;}
-    footer {visibility: hidden;}
-    header {visibility: hidden;}
-</style>
-""", unsafe_allow_html=True)
-
-# --- HEADER ---
-st.markdown('<div class="main-title">💬 MoodMate</div>', unsafe_allow_html=True)
-st.markdown('<div class="subtitle">Ask anything about personal, social, or business growth — powered by RAG + Gemini</div>', unsafe_allow_html=True)
-
-add_vertical_space(2)
-
-# --- LOAD PIPELINE ---
-@st.cache_resource
-def load_chain():
-    return build_rag_pipeline()
-
-llm, retriever, rag_chain = load_chain()
-
-# --- USER SETTINGS ---
-st.markdown("### ⚙️ Answer Selection Settings")
-
-# Automatic vs Manual mode
-auto_mode = st.checkbox("Automatic answer selection (default)", value=True)
-
-# Manual answer type selection appears only if auto_mode is off
-if not auto_mode:
-    answer_type = st.radio(
-        "Select answer type:",
-        ("Dataset-Based Answer", "General Reasoning Answer"),
-        index=0
-    )
-add_vertical_space(1)
-
-# --- SESSION STATE MEMORY ---
-if "chat_history" not in st.session_state:
-    st.session_state.chat_history = []
-
-# Ensure input_box key exists so it persists across runs
-if "input_box" not in st.session_state:
-    st.session_state.input_box = ""
-
-# --- LAYOUT: chat area + input at bottom ---
-chat_col = st.container()
-
-# Render chat area (so it updates live on each run)
-with chat_col:
-    st.markdown("## 💬 Conversation")
-    chat_area = st.container()
-    with chat_area:
-        # Render each turn in order
-        for i, turn in enumerate(st.session_state.chat_history):
-            # User bubble (left)
-            st.markdown(f'<div class="user-bubble">🧑 You: {turn["user"]}</div>', unsafe_allow_html=True)
-
-            # Assistant bubble with subtle badge
-            typ = turn.get("type", "General Reasoning")
-            badge_html = (
-                f'<span class="meta-badge badge-dataset">Dataset-Based</span>'
-                if typ == "Dataset-Based Answer"
-                else f'<span class="meta-badge badge-general">General Reasoning</span>'
-            )
-
-            st.markdown(f'<div class="assistant-bubble">🤖 Assistant: {turn["ai"]} {badge_html}</div>', unsafe_allow_html=True)
-
-            # If dataset-based and has docs, show small expander for docs
-            if turn.get("type") == "Dataset-Based Answer" and turn.get("docs"):
-                with st.expander(f"📂 Top Retrieved Documents for message {i+1}"):
-                    for d in turn["docs"][:3]:
-                        parts = d.page_content.split("\n")
-                        q_text = parts[0].replace("Q: ", "") if len(parts) > 0 else ""
-                        a_text = parts[1].replace("A: ", "") if len(parts) > 1 else ""
-                        st.markdown(
-                            f'<div class="doc-box"><div class="doc-q">Q: {q_text}</div><div class="doc-a">A: {a_text}</div></div>',
-                            unsafe_allow_html=True
-                        )
-
-# --- SEND CALLBACK LOGIC ---
-def handle_send():
-    query = st.session_state.input_box.strip()
-    if not query:
-        st.warning("Please enter a message.")
-        return
-
-    with st.spinner("🔍 Thinking and retrieving relevant information..."):
-        # --- Build unified chat history for contextual prompting ---
-        N_keep = 6  # keep last 6 turns
-        history_for_prompt = st.session_state.chat_history[-N_keep:]
-        full_prompt = ""
-        for turn in history_for_prompt:
-            full_prompt += f"User: {turn['user']}\nAI: {turn['ai']}\n"
-        full_prompt += f"User: {query}\nAI:"
-
-        rag_answer, general_answer, docs = "", "", []
-
-        # --- AUTO MODE ---
-        if auto_mode:
-            # Step 1: Try dataset-based (RAG) first
-            rag_result = rag_chain({"question": query})
-            rag_answer = rag_result.get("answer", "")
-            docs = rag_result.get("source_documents", [])
-
-            # Step 2: Evaluate RAG answer quality
-            # Automatically decide whether to show the dataset-based answer or fall back to general reasoning
-            # Explanation:
-            # - any(kw in rag_answer.lower() for kw in fallback_keywords): checks if any "bad" keyword appears
-            # - len(rag_answer.strip()) < 50: checks if the dataset-based answer is too short (likely low quality)
-            # - not (...): inverts the condition — we show dataset answer only if it’s *good enough*            
-            fallback_keywords = ["cannot answer", "no information", "based on the context", "i'm sorry"]
-            rag_too_short = len(rag_answer.strip()) < 50
-            rag_weak = any(kw in rag_answer.lower() for kw in fallback_keywords)
-
-            if rag_weak or rag_too_short:
-                # Step 3: Fallback to general reasoning ONLY if RAG is weak
-                general_response_obj = llm.invoke(full_prompt)
-                general_answer = getattr(general_response_obj, "content", str(general_response_obj))
-                chosen_answer = general_answer
-                chosen_type = "General Reasoning"
-            else:
-                chosen_answer = rag_answer
-                chosen_type = "Dataset-Based Answer"
-
-        # --- MANUAL MODE ---
-        else:
-            if answer_type == "Dataset-Based Answer":
-                rag_result = rag_chain({"question": query})
-                rag_answer = rag_result.get("answer", "")
-                docs = rag_result.get("source_documents", [])
-                chosen_answer = rag_answer
-                chosen_type = "Dataset-Based Answer"
-            else:
-                general_response_obj = llm.invoke(full_prompt)
-                general_answer = getattr(general_response_obj, "content", str(general_response_obj))
-                chosen_answer = general_answer
-                chosen_type = "General Reasoning"
-
-        # --- Append to unified chat history ---
-        st.session_state.chat_history.append({
-            "user": query,
-            "ai": chosen_answer,
-            "type": chosen_type,
-            "docs": docs if chosen_type == "Dataset-Based Answer" else None
-        })
-
-    # ✅ Clear input after sending
-    st.session_state.input_box = ""
-
-# --- INPUT AREA (stays at bottom) ---
-# --- FIXED INPUT BAR ---
-st.markdown('<div class="input-container">', unsafe_allow_html=True)
-
-query = st.text_input(
-    "💭 Type your message here...",
-    key="input_box",
-    placeholder="e.g. How can I improve my communication skills?",
-    label_visibility="collapsed"
-)
-
-col1, col2 = st.columns([0.2, 0.8])
-with col1:
-    st.button("Send 💬", key="send_button", on_click=handle_send)
-with col2:
-    st.button("🧹 Clear Chat", key="clear_button", help="Clears conversation history (not persistent).", on_click=lambda: (
-        st.session_state.chat_history.clear(),
-        st.session_state.update({"input_box": ""}),
-        st.rerun()
-    ))
-
+import streamlit as st
+from rag_pipeline import build_rag_pipeline
+from streamlit_extras.add_vertical_space import add_vertical_space
+
+# --- PAGE CONFIG ---
+st.set_page_config(
+    page_title="💬 MoodMate",
+    page_icon="💫",
+    layout="centered",
+)
+
+# --- CUSTOM CSS (bubbles + badge) ---
+st.markdown("""
+<style>
+    .main-title { text-align: center; font-size: 2.2em; font-weight: 700; color: #4A90E2; }
+    .subtitle { text-align: center; font-size: 1.1em; color: #666; margin-bottom: 1.5em; }
+    .user-bubble {
+        background: linear-gradient(180deg, #dbe9ff, #c7ddff);
+        padding: 0.75em 1em;
+        border-radius: 12px;
+        margin: 0.5em 0 0.25em 0;
+        max-width: 80%;
+    }
+    .assistant-bubble {
+        background: #f5f7fa;
+        padding: 0.9em 1em;
+        border-radius: 12px;
+        margin: 0.25em 0 0.8em 0;
+        border: 1px solid #e1e4e8;
+        max-width: 80%;
+    }
+    .meta-badge {
+        display: inline-block;
+        font-size: 0.72em;
+        padding: 2px 8px;
+        border-radius: 999px;
+        margin-left: 8px;
+        vertical-align: middle;
+    }
+    .badge-dataset { background: #fff6ea; color: #b36b00; border: 1px solid #f0e68c; }
+    .badge-general { background: #eefcf3; color: #0a7f53; border: 1px solid #bfead4; }
+    .doc-box { background-color: #fffbe6; padding: 0.6em 0.8em; border-radius: 8px; border: 1px solid #f0e68c; margin-bottom: 0.5em; }
+    .doc-q { font-weight: 600; color: #333; }
+    .doc-a { color: #555; }
+
+    /* Make chat area scrollable and avoid hiding under input */
+    .chat-area {
+        max-height: 70vh;
+        overflow-y: auto;
+        padding-right: 8px;
+        padding-bottom: 120px; /* Space for input bar */
+    }
+
+    /* Fix the input container at the bottom */
+    .input-container {
+        position: fixed;
+        bottom: 0;
+        left: 0;
+        right: 0;
+        background-color: #ffffff;
+        padding: 1rem 2rem;
+        box-shadow: 0 -2px 10px rgba(0, 0, 0, 0.05);
+        z-index: 999;
+    }
+
+    /* Optional: make buttons line up neatly */
+    .stButton button {
+        height: 2.5em;
+    }
+
+    /* Hide Streamlit footer and hamburger for cleaner look */
+    #MainMenu {visibility: hidden;}
+    footer {visibility: hidden;}
+    header {visibility: hidden;}
+</style>
+""", unsafe_allow_html=True)
+
+# --- HEADER ---
+st.markdown('<div class="main-title">💬 MoodMate</div>', unsafe_allow_html=True)
+st.markdown('<div class="subtitle">Ask anything about personal, social, or business growth — powered by RAG + Gemini</div>', unsafe_allow_html=True)
+
+add_vertical_space(2)
+
+# --- LOAD PIPELINE ---
+@st.cache_resource
+def load_chain():
+    return build_rag_pipeline()
+
+llm, retriever, rag_chain = load_chain()
+
+# --- USER SETTINGS ---
+st.markdown("### ⚙️ Answer Selection Settings")
+
+# Automatic vs Manual mode
+auto_mode = st.checkbox("Automatic answer selection (default)", value=True)
+
+# Manual answer type selection appears only if auto_mode is off
+if not auto_mode:
+    answer_type = st.radio(
+        "Select answer type:",
+        ("Dataset-Based Answer", "General Reasoning Answer"),
+        index=0
+    )
+add_vertical_space(1)
+
+# --- SESSION STATE MEMORY ---
+if "chat_history" not in st.session_state:
+    st.session_state.chat_history = []
+
+# Ensure input_box key exists so it persists across runs
+if "input_box" not in st.session_state:
+    st.session_state.input_box = ""
+
+# --- LAYOUT: chat area + input at bottom ---
+chat_col = st.container()
+
+# Render chat area (so it updates live on each run)
+with chat_col:
+    st.markdown("## 💬 Conversation")
+    chat_area = st.container()
+    with chat_area:
+        # Render each turn in order
+        for i, turn in enumerate(st.session_state.chat_history):
+            # User bubble (left)
+            st.markdown(f'<div class="user-bubble">🧑 You: {turn["user"]}</div>', unsafe_allow_html=True)
+
+            # Assistant bubble with subtle badge
+            typ = turn.get("type", "General Reasoning")
+            badge_html = (
+                f'<span class="meta-badge badge-dataset">Dataset-Based</span>'
+                if typ == "Dataset-Based Answer"
+                else f'<span class="meta-badge badge-general">General Reasoning</span>'
+            )
+
+            st.markdown(f'<div class="assistant-bubble">🤖 Assistant: {turn["ai"]} {badge_html}</div>', unsafe_allow_html=True)
+
+            # If dataset-based and has docs, show small expander for docs
+            if turn.get("type") == "Dataset-Based Answer" and turn.get("docs"):
+                with st.expander(f"📂 Top Retrieved Documents for message {i+1}"):
+                    for d in turn["docs"][:3]:
+                        parts = d.page_content.split("\n")
+                        q_text = parts[0].replace("Q: ", "") if len(parts) > 0 else ""
+                        a_text = parts[1].replace("A: ", "") if len(parts) > 1 else ""
+                        st.markdown(
+                            f'<div class="doc-box"><div class="doc-q">Q: {q_text}</div><div class="doc-a">A: {a_text}</div></div>',
+                            unsafe_allow_html=True
+                        )
+
+# --- SEND CALLBACK LOGIC ---
+def handle_send():
+    query = st.session_state.input_box.strip()
+    if not query:
+        st.warning("Please enter a message.")
+        return
+
+    with st.spinner("🔍 Thinking and retrieving relevant information..."):
+        # --- Build unified chat history for contextual prompting ---
+        N_keep = 6  # keep last 6 turns
+        history_for_prompt = st.session_state.chat_history[-N_keep:]
+        full_prompt = ""
+        for turn in history_for_prompt:
+            full_prompt += f"User: {turn['user']}\nAI: {turn['ai']}\n"
+        full_prompt += f"User: {query}\nAI:"
+
+        rag_answer, general_answer, docs = "", "", []
+
+        # --- AUTO MODE ---
+        if auto_mode:
+            # Step 1: Try dataset-based (RAG) first
+            rag_result = rag_chain({"question": query})
+            rag_answer = rag_result.get("answer", "")
+            docs = rag_result.get("source_documents", [])
+
+            # Step 2: Evaluate RAG answer quality
+            # Automatically decide whether to show the dataset-based answer or fall back to general reasoning
+            # Explanation:
+            # - any(kw in rag_answer.lower() for kw in fallback_keywords): checks if any "bad" keyword appears
+            # - len(rag_answer.strip()) < 50: checks if the dataset-based answer is too short (likely low quality)
+            # - not (...): inverts the condition — we show dataset answer only if it’s *good enough*            
+            fallback_keywords = ["cannot answer", "no information", "based on the context", "i'm sorry"]
+            rag_too_short = len(rag_answer.strip()) < 50
+            rag_weak = any(kw in rag_answer.lower() for kw in fallback_keywords)
+
+            if rag_weak or rag_too_short:
+                # Step 3: Fallback to general reasoning ONLY if RAG is weak
+                general_response_obj = llm.invoke(full_prompt)
+                general_answer = getattr(general_response_obj, "content", str(general_response_obj))
+                chosen_answer = general_answer
+                chosen_type = "General Reasoning"
+            else:
+                chosen_answer = rag_answer
+                chosen_type = "Dataset-Based Answer"
+
+        # --- MANUAL MODE ---
+        else:
+            if answer_type == "Dataset-Based Answer":
+                rag_result = rag_chain({"question": query})
+                rag_answer = rag_result.get("answer", "")
+                docs = rag_result.get("source_documents", [])
+                chosen_answer = rag_answer
+                chosen_type = "Dataset-Based Answer"
+            else:
+                general_response_obj = llm.invoke(full_prompt)
+                general_answer = getattr(general_response_obj, "content", str(general_response_obj))
+                chosen_answer = general_answer
+                chosen_type = "General Reasoning"
+
+        # --- Append to unified chat history ---
+        st.session_state.chat_history.append({
+            "user": query,
+            "ai": chosen_answer,
+            "type": chosen_type,
+            "docs": docs if chosen_type == "Dataset-Based Answer" else None
+        })
+
+    # ✅ Clear input after sending
+    st.session_state.input_box = ""
+
+# --- INPUT AREA (stays at bottom) ---
+# --- FIXED INPUT BAR ---
+st.markdown('<div class="input-container">', unsafe_allow_html=True)
+
+query = st.text_input(
+    "💭 Type your message here...",
+    key="input_box",
+    placeholder="e.g. How can I improve my communication skills?",
+    label_visibility="collapsed"
+)
+
+col1, col2 = st.columns([0.2, 0.8])
+with col1:
+    st.button("Send 💬", key="send_button", on_click=handle_send)
+with col2:
+    st.button("🧹 Clear Chat", key="clear_button", help="Clears conversation history (not persistent).", on_click=lambda: (
+        st.session_state.chat_history.clear(),
+        st.session_state.update({"input_box": ""}),
+        st.rerun()
+    ))
+
 st.markdown('</div>', unsafe_allow_html=True)