Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,215 @@
-import altair as alt
-import numpy as np
-import pandas as pd
+%%writefile app.py
+import os
 import streamlit as st
+from groq import Groq
+
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import FAISS
+from langchain_community.embeddings import HuggingFaceEmbeddings
+
+import pandas as pd
+import docx
+from pypdf import PdfReader
+
+from reportlab.platypus import SimpleDocTemplate, Paragraph
+from reportlab.lib.styles import getSampleStyleSheet
+import tempfile
+
+# -----------------------------
+# GROQ CLIENT
+# -----------------------------
+client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
+
+# -----------------------------
+# PAGE CONFIG
+# -----------------------------
+st.set_page_config(
+    page_title="AI Study Assistant 📚",
+    page_icon="🎓",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# -----------------------------
+# SIDEBAR
+# -----------------------------
+st.sidebar.title("📌 Settings")
+education_level = st.sidebar.selectbox(
+    "Select Education Level",
+    ["Primary School", "Middle School", "Secondary School", "High School", "Undergraduate", "Graduate"]
+)
+st.sidebar.markdown("---")
+st.sidebar.write("Developed by **Ahmad Bilal** | Fiverr Portfolio Demo")
+
+# -----------------------------
+# HEADER
+# -----------------------------
+st.markdown(
+    """
+    <div style='text-align:center; padding:10px; background-color:#f0f2f6; border-radius:10px'>
+        <h1 style='color:#0f4c81'>🎓 AI Study Assistant</h1>
+        <p style='font-size:18px; color:#333'>Upload your study material and ask questions. Get answers & summaries instantly!</p>
+    </div>
+    """,
+    unsafe_allow_html=True
+)
+
+# -----------------------------
+# FILE UPLOADER (CENTER)
+# -----------------------------
+st.markdown("### 📂 Upload Your Study Materials")
+uploaded_files = st.file_uploader(
+    "",
+    type=["pdf","docx","txt","csv","xlsx"],
+    accept_multiple_files=True,
+    help="Upload PDFs, Word Docs, Excel, CSV, or TXT files."
+)
+
+# -----------------------------
+# FILE PROCESSING
+# -----------------------------
+def load_pdf(file):
+    reader = PdfReader(file)
+    text=""
+    for page in reader.pages:
+        text+=page.extract_text()
+    return text
+
+def load_docx(file):
+    doc=docx.Document(file)
+    return "\n".join([p.text for p in doc.paragraphs])
+
+def load_csv(file):
+    df=pd.read_csv(file)
+    return df.to_string()
+
+def load_xlsx(file):
+    df=pd.read_excel(file)
+    return df.to_string()
+
+def load_txt(file):
+    return file.read().decode("utf-8")
 
+def process_docs(files):
+    text=""
+    for file in files:
+        if file.type=="application/pdf":
+            text+=load_pdf(file)
+        elif file.type=="application/vnd.openxmlformats-officedocument.wordprocessingml.document":
+            text+=load_docx(file)
+        elif file.type=="text/csv":
+            text+=load_csv(file)
+        elif file.type=="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet":
+            text+=load_xlsx(file)
+        else:
+            text+=load_txt(file)
+    return text
+
+# -----------------------------
+# VECTOR STORE
+# -----------------------------
+@st.cache_resource
+def create_vectorstore(text):
+    splitter = RecursiveCharacterTextSplitter(chunk_size=800, chunk_overlap=100)
+    chunks = splitter.split_text(text)
+    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+    vectorstore = FAISS.from_texts(chunks, embeddings)
+    return vectorstore
+
+# -----------------------------
+# PROMPT BUILDER
+# -----------------------------
+def build_prompt(context, question, level):
+    style={
+        "Primary School":"Explain like teaching a 5 year old child using simple fun examples.",
+        "Middle School":"Explain using easy examples.",
+        "Secondary School":"Explain clearly using simple concepts.",
+        "High School":"Explain with reasoning and examples.",
+        "Undergraduate":"Explain in academic but easy language.",
+        "Graduate":"Provide detailed academic explanation."
+    }
+    prompt=f"""
+Use the study material below to answer the question.
+
+Study Material:
+{context}
+
+Question:
+{question}
+
+Explanation Style:
+{style[level]}
 """
-# Welcome to Streamlit!
+    return prompt
+
+# -----------------------------
+# GROQ LLM CALL
+# -----------------------------
+def ask_llm(prompt):
+    chat_completion = client.chat.completions.create(
+        messages=[{"role":"user","content":prompt}],
+        model="llama-3.3-70b-versatile"
+    )
+    return chat_completion.choices[0].message.content
 
-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).
+# -----------------------------
+# SUMMARY GENERATOR
+# -----------------------------
+def generate_summary(text):
+    prompt=f"""
+Create a short and easy summary of the following material.
 
-In the meantime, below is an example of what you can do with just a few lines of code:
+{text}
 """
+    return ask_llm(prompt)
+
+# -----------------------------
+# PDF GENERATOR
+# -----------------------------
+def create_pdf(text):
+    temp_file=tempfile.NamedTemporaryFile(delete=False,suffix=".pdf")
+    styles=getSampleStyleSheet()
+    story=[Paragraph(text,styles["Normal"])]
+    doc=SimpleDocTemplate(temp_file.name)
+    doc.build(story)
+    return temp_file.name
+
+# -----------------------------
+# MAIN APP LOGIC
+# -----------------------------
+if uploaded_files:
+
+    raw_text=process_docs(uploaded_files)
+    vectorstore=create_vectorstore(raw_text)
+
+    st.markdown("---")
+    st.markdown("### ❓ Ask a Question")
+    question=st.text_input("Type your question here:")
+
+    if question:
+
+        # Use columns for nicer layout
+        col1, col2 = st.columns([2,1])
+
+        docs = vectorstore.similarity_search(question, k=3)
+        context = "\n".join([doc.page_content for doc in docs])
+        prompt = build_prompt(context, question, education_level)
+        answer = ask_llm(prompt)
+
+        with col1:
+            st.markdown("### 📖 Answer")
+            st.success(answer)
+
+        with col2:
+            st.markdown("### 📝 Generate Summary")
+            if st.button("Create Summary & Download"):
+                summary = generate_summary(context)
+                st.markdown("#### Summary")
+                st.info(summary)
+                st.download_button("Download Markdown", summary, file_name="summary.md")
+                pdf_file=create_pdf(summary)
+                with open(pdf_file,"rb") as f:
+                    st.download_button("Download PDF", f, file_name="summary.pdf")
 
-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])),
-    ))
+else:
+    st.markdown("⚠️ Please upload at least one study document to start.")