Update src/streamlit_app.py

src/streamlit_app.py

@@ -25,18 +25,26 @@ client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
 st.set_page_config(
     page_title="AI Study Assistant 📚",
     page_icon="🎓",
-    layout="wide",
-    initial_sidebar_state="expanded"
+    layout="wide"
 )
 
 # -----------------------------
 # SIDEBAR
 # -----------------------------
 st.sidebar.title("📌 Settings")
+
 education_level = st.sidebar.selectbox(
     "Select Education Level",
-    ["Primary School", "Middle School", "Secondary School", "High School", "Undergraduate", "Graduate"]
+    [
+        "Primary School",
+        "Middle School",
+        "Secondary School",
+        "High School",
+        "Undergraduate",
+        "Graduate"
+    ]
 )
+
 st.sidebar.markdown("---")
 st.sidebar.write("Developed by **Ahmad Bilal** | Fiverr Portfolio Demo")
 
@@ -47,97 +55,130 @@ 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>
+        <p style='font-size:18px'>Upload study materials and ask questions instantly!</p>
     </div>
     """,
     unsafe_allow_html=True
 )
 
 # -----------------------------
-# FILE UPLOADER (CENTER)
+# FILE UPLOADER
 # -----------------------------
 uploaded_files = st.file_uploader(
     "Upload Study Documents",
-    type=["pdf", "docx", "txt", "csv", "xlsx"],
-    accept_multiple_files=True,
-    label_visibility="visible"
+    type=["pdf","docx","txt","csv","xlsx"],
+    accept_multiple_files=True
 )
 
+valid_files = []
+
 if uploaded_files:
-    st.success(f"{len(uploaded_files)} file(s) uploaded successfully!")
-    MAX_FILE_SIZE = 20 * 1024 * 1024  # 20MB
 
-valid_files = []
+    MAX_FILE_SIZE = 20 * 1024 * 1024
+
+    for file in uploaded_files:
+        if file.size > MAX_FILE_SIZE:
+            st.error(f"{file.name} is too large. Upload files under 20MB.")
+        else:
+            valid_files.append(file)
+
+    st.success(f"{len(valid_files)} file(s) ready for processing")
 
-for file in uploaded_files:
-    if file.size > MAX_FILE_SIZE:
-        st.error(f"{file.name} is too large. Please upload files under 20MB.")
-    else:
-        valid_files.append(file)
 # -----------------------------
-# FILE PROCESSING
+# FILE LOADERS
 # -----------------------------
 def load_pdf(file):
     reader = PdfReader(file)
-    text=""
+    text = ""
     for page in reader.pages:
-        text+=page.extract_text()
+        if page.extract_text():
+            text += page.extract_text()
     return text
 
+
 def load_docx(file):
-    doc=docx.Document(file)
+    doc = docx.Document(file)
     return "\n".join([p.text for p in doc.paragraphs])
 
+
 def load_csv(file):
-    df=pd.read_csv(file)
+    df = pd.read_csv(file)
     return df.to_string()
 
+
 def load_xlsx(file):
-    df=pd.read_excel(file)
+    df = pd.read_excel(file)
     return df.to_string()
 
+
 def load_txt(file):
     return file.read().decode("utf-8")
 
-raw_text = process_docs(valid_files):
-    text=""
+
+# -----------------------------
+# DOCUMENT PROCESSING
+# -----------------------------
+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)
+
+        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)
+            text += load_txt(file)
+
     return text
 
+
 # -----------------------------
 # VECTOR STORE
 # -----------------------------
 @st.cache_resource
 def create_vectorstore(text):
-    splitter = RecursiveCharacterTextSplitter(chunk_size=800, chunk_overlap=100)
+
+    splitter = RecursiveCharacterTextSplitter(
+        chunk_size=800,
+        chunk_overlap=100
+    )
+
     chunks = splitter.split_text(text)
-    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+
+    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."
+
+    style = {
+        "Primary School": "Explain like teaching a 5 year old using fun examples.",
+        "Middle School": "Explain with easy examples.",
+        "Secondary School": "Explain clearly using simple ideas.",
+        "High School": "Explain with reasoning and examples.",
+        "Undergraduate": "Explain in academic but clear language.",
+        "Graduate": "Provide detailed academic explanation."
     }
-    prompt=f"""
+
+    prompt = f"""
 Use the study material below to answer the question.
 
 Study Material:
@@ -149,76 +190,114 @@ Question:
 Explanation Style:
 {style[level]}
 """
+
     return prompt
 
+
 # -----------------------------
-# GROQ LLM CALL
+# GROQ LLM
 # -----------------------------
 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
 
+
 # -----------------------------
-# SUMMARY GENERATOR
+# SUMMARY
 # -----------------------------
 def generate_summary(text):
-    prompt=f"""
-Create a short and easy summary of the following material.
+
+    prompt = f"""
+Create a short and simple summary of this study material.
 
 {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)
+
+    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
+# MAIN LOGIC
 # -----------------------------
-if uploaded_files:
+if valid_files:
+
+    raw_text = process_docs(valid_files)
 
-    raw_text=process_docs(uploaded_files)
-    vectorstore=create_vectorstore(raw_text)
+    vectorstore = create_vectorstore(raw_text)
 
     st.markdown("---")
-    st.markdown("### ❓ Ask a Question")
-    question=st.text_input("Type your question here:")
+
+    st.subheader("❓ Ask a Question")
+
+    question = st.text_input("Type your question")
 
     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"):
+
+            st.markdown("### 📝 Summary")
+
+            if st.button("Generate Summary"):
+
                 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")
+
+                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"
+                    )
 
 else:
-    st.markdown("⚠️ Please upload at least one study document to start.")
+
+    st.info("📂 Upload at least one study document to start.")