import streamlit as st
import sqlite3
import pandas as pd

# Initialize Session States for Conversational History if not present
if "chats" not in st.session_state:
    st.session_state.chats = {
        "Chat 1": [{"role": "assistant", "content": "Hello! How can I help you today?"}],
        "Chat 2": [{"role": "assistant", "content": "Welcome to NewPhamaGPT. Ask me anything."}]
    }
if "active_chat" not in st.session_state:
    st.session_state.active_chat = "Chat 1"

# Database helper function (Modify with your actual DB path/type)
DB_PATH = "pharma_data.db"
def add_sql_record(med_name, dosage, stock):
    conn = sqlite3.connect(DB_PATH)
    cursor = conn.cursor()
    # Create table if it doesn't exist for testing purposes
    cursor.execute('''CREATE TABLE IF NOT EXISTS inventory 
                      (id INTEGER PRIMARY KEY, medicine_name TEXT, dosage TEXT, stock INTEGER)''')
    cursor.execute("INSERT INTO inventory (medicine_name, dosage, stock) VALUES (?, ?, ?)", (med_name, dosage, stock))
    conn.commit()
    conn.close()

# ==========================================
# FEATURE 1 & 2: LEFT SIDEBAR CHAT LIST & RENAME
# ==========================================
st.sidebar.title("💬 Conversational Chats")

# New Chat Button
if st.sidebar.button("➕ New Chat", use_container_width=True):
    new_chat_title = f"Chat {len(st.session_state.chats) + 1}"
    st.session_state.chats[new_chat_title] = [{"role": "assistant", "content": "Hello! New chat started."}]
    st.session_state.active_chat = new_chat_title
    st.rerun()

st.sidebar.markdown("---")

# Render Chats as a clickable list instead of a dropdown
for chat_title in list(st.session_state.chats.keys()):
    # Highlight the currently active chat
    is_active = (chat_title == st.session_state.active_chat)
    button_label = f"📝 {chat_title}" if is_active else f"📄 {chat_title}"
    
    if st.sidebar.button(button_label, key=f"select_{chat_title}", use_container_width=True):
        st.session_state.active_chat = chat_title
        st.rerun()

st.sidebar.markdown("---")

# Feature 2: Rename Option for the currently active chat
st.sidebar.subheader("✏️ Rename Active Chat")
current_name = st.session_state.active_chat
new_name = st.sidebar.text_input("New Title", value=current_name, key="rename_input")

if st.sidebar.button("Save Title", use_container_width=True):
    if new_name and new_name != current_name:
        if new_name in st.session_state.chats:
            st.sidebar.error("A chat with this name already exists!")
        else:
            # Re-map the key in the dictionary to keep history intact
            st.session_state.chats[new_name] = st.session_state.chats.pop(current_name)
            st.session_state.active_chat = new_name
            st.rerun()


# ==========================================
# FEATURE 3: ADD RECORD TO SQL DATABASE
# ==========================================
st.title("🗄️ Database Management")
with st.expander("➕ Add New Record to SQL Database", expanded=False):
    with st.form("sql_add_form", clear_on_submit=True):
        st.write("Enter the details below to add a record directly to the database:")
        med_name = st.text_input("Medicine Name")
        dosage = st.text_input("Dosage (e.g., 500mg)")
        stock = st.number_input("Stock Quantity", min_value=0, step=1)
        
        submit_button = st.form_submit_button(label="Add Record")
        
        if submit_button:
            if med_name and dosage:
                try:
                    add_sql_record(med_name, dosage, stock)
                    st.success(f"Successfully added '{med_name}' to the database!")
                except Exception as e:
                    st.error(f"Failed to write to database: {e}")
            else:
                st.warning("Please fill in all mandatory fields.")


# ==========================================
# FEATURE 4: CONVERSATIONAL CHAT BOT (WITH PDF FORMATTING)
# ==========================================
st.title(f"🤖 {st.session_state.active_chat}")

# Display current conversation history
for msg in st.session_state.chats[st.session_state.active_chat]:
    st.chat_message(msg["role"]).write(msg["content"])

# Target PDF report template enforced via system instructions
PDF_FORMAT_PROMPT = """
[SYSTEM INSTRUCTION]
You must format your entire response to mimic an official PDF Medical/Pharmaceutical Report. 
Structure all responses utilizing Markdown with the exact sections below:

# PHARMACEUTICAL ASSESSMENT REPORT
**Document Ref:** PHARMA-GPT-2026  
**Status:** FINAL  

---

### 1. EXECUTIVE SUMMARY
[Provide a concise overview here]

### 2. DETAILED ANALYSIS & FINDINGS
[Provide structured detailed items here. Use tables where applicable]

### 3. RECOMMENDATIONS & NEXT STEPS
- **Short-term Action:** [Detail item]
- **Long-term Strategy:** [Detail item]

---
*Confidentiality Notice: This output conforms to the standard automated PDF reporting layout.*
"""

if user_query := st.chat_input():
    # Save user message
    st.session_state.chats[st.session_state.active_chat].append({"role": "user", "content": user_query})
    st.chat_message("user").write(user_query)
    
    # Simulate LLM Response generation (Connect this to your actual LLM pipeline)
    # Ensure you pass PDF_FORMAT_PROMPT along with user_query to your model context
    simulated_llm_response = f"""# PHARMACEUTICAL ASSESSMENT REPORT
**Document Ref:** PHARMA-GPT-2026  
**Status:** FINAL  

---

### 1. EXECUTIVE SUMMARY
Regarding your query on '{user_query}', this assessment covers clinical observations and compliance parameters.

### 2. DETAILED ANALYSIS & FINDINGS
| Parameter | Status | Notes |
| :--- | :--- | :--- |
| Query Focus | Evaluated | Subject investigated fully. |
| Compliance | Verified | Meets strict documentation parameters. |

### 3. RECOMMENDATIONS & NEXT STEPS
- **Short-term Action:** Review the raw log output.
- **Long-term Strategy:** Automate recurring reporting cycles.

---
*Confidentiality Notice: This output conforms to the standard automated PDF reporting layout.*"""

    # Save and display assistant response
    st.session_state.chats[st.session_state.active_chat].append({"role": "assistant", "content": simulated_llm_response})
    st.chat_message("assistant").write(simulated_llm_response)