src/streamlit_app.py

import os
from io import BytesIO

import streamlit as st
from langchain.prompts import PromptTemplate
from langchain.chains.llm import LLMChain
from langchain_google_genai import ChatGoogleGenerativeAI
from fpdf import FPDF

# 1️⃣ Configure with your API key
os.environ["GOOGLE_API_KEY"] = os.getenv("G_API")


# 2️⃣ Initialize Gemini via LangChain
model = ChatGoogleGenerativeAI(model="gemini-2.0-flash", 
                               temperature=0.0)

# 3️⃣ Prompt template

prompt = PromptTemplate.from_template(
    "You are an expert and motivational AI Mentor. Provide detailed, thoughtful, and practical guidance in response to the following query. Avoid unnecessary fluff or emojis.\n\n{input}"
)

chain = LLMChain(llm=model, prompt=prompt)

def ai_mentor(prompt_input: str) -> str:
    return chain.run(input=prompt_input)

def create_pdf_buffer(messages) -> BytesIO:
    buffer = BytesIO()
    pdf = FPDF()
    pdf.add_page()
    pdf.set_font("Helvetica", size=14)
    pdf.cell(200, 20, "Chat History with AI Mentor", ln=1, align="C")
    pdf.ln(10)
    pdf.set_font("Helvetica", size=12)
    for msg in messages:
        role = msg["role"].capitalize()
        pdf.multi_cell(0, 8, f"{role}: {msg['content']}")
        pdf.ln(2)
    # Write PDF to memory as bytes
    pdf_bytes = pdf.output(dest="S").encode("latin-1")
    buffer.write(pdf_bytes)
    buffer.seek(0)
    return buffer

# ── Streamlit UI ──
st.title("AI Mentor (Gemini with LangChain)")
st.sidebar.write("Chat with your AI Mentor. Type questions or worries below 😊")

if "messages" not in st.session_state:
    st.session_state.messages = []

for msg in st.session_state.messages:
    st.chat_message(msg["role"]).write(msg["content"])

prompt_input = st.chat_input("Write your message here...")

if prompt_input:
    st.session_state.messages.append({"role": "user", "content": prompt_input})
    with st.spinner("AI Mentor is thinking..."):
        response = ai_mentor(prompt_input)
    st.session_state.messages.append({"role": "assistant", "content": response})
    st.chat_message("assistant").write(response)


if st.session_state.messages:
    pdf_buffer = create_pdf_buffer(st.session_state.messages)
    st.download_button(
        label="Download chat history as PDF",
        data=pdf_buffer,
        file_name="chat_history.pdf",
        mime="application/pdf",
    )