Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,69 @@
-import altair as alt
-import numpy as np
-import pandas as pd
+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"] = "AIzaSyCB5NLx39vOAlfRQBDmnEG3uLBgLraGvH4"  
+
+
+# 2️⃣ Initialize Gemini via LangChain
+model = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0.5)
+
+# 3️⃣ Prompt template
+prompt = PromptTemplate.from_template(
+    "You are a helpful and motivational AI Mentor. Provide clear, constructive, actionable, and brief guidance (max 100 words) to the following query:\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})
+    response = ai_mentor(prompt_input)
+    st.session_state.messages.append({"role": "assistant", "content": response})
+    st.chat_message("assistant").write(response)
 
-"""
-# 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])),
-    ))
+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",
+    )