Create app.py

app.py

@@ -0,0 +1,332 @@
+# app.py
+import os
+import io
+import streamlit as st
+import pdfplumber
+from pptx import Presentation
+import docx as docx_lib
+import pandas as pd
+from sentence_transformers import SentenceTransformer
+import faiss
+from groq import Groq
+import markdown2
+from reportlab.lib.pagesizes import letter
+from reportlab.pdfgen import canvas
+
+# ---------------- CONFIG ----------------
+EMBED_MODEL = "sentence-transformers/all-MiniLM-L6-v2"
+GROQ_LLM_MODEL = "llama-3.3-70b-versatile"
+
+# ---------------- HELPERS ----------------
+@st.cache_resource
+def load_embedder():
+    return SentenceTransformer(EMBED_MODEL)
+
+embedder = load_embedder()
+
+def parse_pdf_bytes(file_bytes):
+    try:
+        text = ""
+        with pdfplumber.open(io.BytesIO(file_bytes)) as pdf:
+            for page in pdf.pages:
+                p = page.extract_text()
+                if p:
+                    text += p + "\n"
+        return text
+    except Exception as e:
+        st.warning(f"PDF parse warning: {e}")
+        return ""
+
+def parse_docx_bytes(file_bytes):
+    try:
+        doc = docx_lib.Document(io.BytesIO(file_bytes))
+        return "\n".join([p.text for p in doc.paragraphs])
+    except Exception as e:
+        st.warning(f"DOCX parse warning: {e}")
+        return ""
+
+def parse_pptx_bytes(file_bytes):
+    try:
+        prs = Presentation(io.BytesIO(file_bytes))
+        text = ""
+        for slide in prs.slides:
+            for shape in slide.shapes:
+                if hasattr(shape, "text"):
+                    text += shape.text + "\n"
+        return text
+    except Exception as e:
+        st.warning(f"PPTX parse warning: {e}")
+        return ""
+
+def parse_spreadsheet_bytes(file_bytes):
+    try:
+        try:
+            df = pd.read_excel(io.BytesIO(file_bytes))
+        except Exception:
+            df = pd.read_csv(io.BytesIO(file_bytes))
+        return df.to_csv(index=False)
+    except Exception as e:
+        st.warning(f"Spreadsheet parse warning: {e}")
+        return ""
+
+def parse_txt_bytes(file_bytes):
+    try:
+        return file_bytes.decode("utf-8", errors="ignore")
+    except Exception as e:
+        st.warning(f"TXT parse warning: {e}")
+        return ""
+
+def chunk_text(text, max_chars=1000, overlap=200):
+    if not text:
+        return []
+    chunks = []
+    start = 0
+    while start < len(text):
+        end = min(start + max_chars, len(text))
+        chunk = text[start:end].strip()
+        if chunk:
+            chunks.append(chunk)
+        if end == len(text):
+            break
+        start = end - overlap
+    return chunks
+
+def build_faiss_index(chunks, embedder):
+    if not chunks:
+        return None, None
+    embeddings = embedder.encode(chunks, convert_to_numpy=True)
+    dim = embeddings.shape[1]
+    index = faiss.IndexFlatL2(dim)
+    index.add(embeddings.astype("float32"))
+    return index, embeddings
+
+def retrieve_chunks(query, embedder, faiss_index, chunks, k=5):
+    if faiss_index is None or not chunks:
+        return []
+    q_emb = embedder.encode([query], convert_to_numpy=True).astype("float32")
+    D, I = faiss_index.search(q_emb, k)
+    results = []
+    for idx in I[0]:
+        if 0 <= idx < len(chunks):
+            results.append(chunks[idx])
+    return results
+
+# ---------------- Groq LLM ----------------
+EDU_PROMPTS = {
+    "Primary School": "Explain this to me like I'm 5 years old, in a fun and simple way with examples and analogies.",
+    "Middle School": "Explain this in a simple and clear way appropriate for a middle school student with examples.",
+    "High School": "Explain this clearly, assuming knowledge up to high school level.",
+    "Undergraduate": "Explain this in a university-level way, with clarity and useful details and examples.",
+    "Graduate": "Explain this at graduate-level rigor, including key details, nuance, and technical terms as appropriate.",
+}
+
+def get_groq_client():
+    api_key = None
+    try:
+        api_key = st.secrets["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"]
+    except Exception:
+        pass
+    if not api_key:
+        api_key = st.session_state.get("groq_api_key") or os.environ.get("GROQ_API_KEY")
+    if not api_key:
+        raise ValueError("Groq API key not found. Set st.secrets['GROQ_API_KEY'], or enter in sidebar, or set env GROQ_API_KEY.")
+    return Groq(api_key=api_key)
+
+def call_llm_with_context(question, retrieved_chunks, edu_level):
+    client = get_groq_client()
+    edu_instr = EDU_PROMPTS.get(edu_level, "")
+    context = "\n\n".join(retrieved_chunks) if retrieved_chunks else ""
+    user_content = f"{edu_instr}\n\nContext:\n{context}\n\nQuestion: {question}"
+    response = client.chat.completions.create(
+        messages=[
+            {"role": "system", "content": "You are a helpful and knowledgeable tutor."},
+            {"role": "user", "content": user_content}
+        ],
+        model=GROQ_LLM_MODEL,
+    )
+    return response.choices[0].message.content
+
+def make_summary(question, retrieved_chunks, edu_level):
+    client = get_groq_client()
+    edu_instr = EDU_PROMPTS.get(edu_level, "")
+    context = "\n\n".join(retrieved_chunks) if retrieved_chunks else ""
+    prompt = f"{edu_instr}\n\nHere is some context:\n{context}\n\nPlease give a short, easy-to-understand summary of: {question}\nKeep it concise and simple; use bullet points if helpful."
+    response = client.chat.completions.create(
+        messages=[
+            {"role": "system", "content": "You are a concise summarizer."},
+            {"role": "user", "content": prompt}
+        ],
+        model=GROQ_LLM_MODEL,
+    )
+    return response.choices[0].message.content
+
+def make_mcqs_from_summary(summary_text, count=5, difficulty="medium"):
+    client = get_groq_client()
+    prompt = (
+        f"Create {count} multiple choice questions (MCQs) from the following summary. "
+        "Each question should have 4 options labeled A-D and indicate the correct option. "
+        "Also provide a 1-2 sentence explanation for the correct answer. "
+        f"Difficulty: {difficulty}.\n\nSummary:\n{summary_text}"
+    )
+    response = client.chat.completions.create(
+        messages=[
+            {"role": "system", "content": "You are an assistant that generates high-quality multiple-choice questions."},
+            {"role": "user", "content": prompt}
+        ],
+        model=GROQ_LLM_MODEL,
+    )
+    return response.choices[0].message.content
+
+# ---------------- STREAMLIT UI ----------------
+st.set_page_config(page_title="AI Study Assistant", layout="wide")
+st.title("📚 AI Study Assistant — Exam Mode")
+
+with st.sidebar:
+    st.header("Settings")
+    groq_key = st.text_input("Groq API key (optional)", type="password")
+    if groq_key:
+        st.session_state["groq_api_key"] = groq_key
+    edu_level = st.selectbox("Education level", list(EDU_PROMPTS.keys()))
+    st.info("Upload documents and ask questions. You can generate summaries + MCQs.")
+
+uploaded_files = st.file_uploader("Upload study documents (PDF, DOCX, PPTX, XLSX/CSV, TXT)", accept_multiple_files=True)
+if not uploaded_files:
+    st.info("Please upload at least one document.")
+    st.stop()
+
+# ---------------- PARSE FILES ----------------
+all_text = ""
+for uf in uploaded_files:
+    raw = uf.read()
+    text = ""
+    name = uf.name.lower()
+    if name.endswith(".pdf"):
+        text = parse_pdf_bytes(raw)
+    elif name.endswith(".docx"):
+        text = parse_docx_bytes(raw)
+    elif name.endswith(".pptx"):
+        text = parse_pptx_bytes(raw)
+    elif name.endswith((".xls", ".xlsx", ".csv")):
+        text = parse_spreadsheet_bytes(raw)
+    elif name.endswith(".txt"):
+        text = parse_txt_bytes(raw)
+    else:
+        try:
+            text = raw.decode("utf-8")
+        except Exception:
+            text = ""
+    if text:
+        all_text += f"\n\n### From file: {uf.name}\n\n{text}"
+
+if not all_text.strip():
+    st.error("No textual content extracted.")
+    st.stop()
+
+# ---------------- CHUNK + INDEX ----------------
+with st.spinner("Processing documents..."):
+    chunks = chunk_text(all_text)
+    faiss_index, embeddings = build_faiss_index(chunks, embedder)
+    st.success(f"Prepared {len(chunks)} chunks and built vector index.")
+
+# ---------------- ASK QUESTION ----------------
+question = st.text_input("Ask a question about your materials:")
+if not question:
+    st.info("Type a question to begin.")
+    st.stop()
+
+topk = st.number_input("Top-k passages", min_value=1, max_value=10, value=5)
+mcq_count = st.number_input("MCQs to generate", min_value=1, max_value=20, value=5)
+mcq_diff = st.selectbox("MCQ difficulty", ["easy", "medium", "hard"], index=1)
+
+retrieved = retrieve_chunks(question, embedder, faiss_index, chunks, k=int(topk))
+
+if retrieved:
+    st.subheader("Relevant passages:")
+    for i, r in enumerate(retrieved):
+        st.markdown(f"**Passage {i+1}:**")
+        st.write(r[:800] + ("..." if len(r) > 800 else ""))
+else:
+    st.warning("No relevant passages found.")
+
+# ---------------- GENERATE ANSWER ----------------
+try:
+    answer = call_llm_with_context(question, retrieved, edu_level)
+    st.subheader("Answer:")
+    st.write(answer)
+except Exception as e:
+    st.error(f"LLM error: {e}")
+    st.stop()
+
+# ---------------- GENERATE SUMMARY + MCQs ----------------
+if st.checkbox("Generate summary and MCQs"):
+    try:
+        summary = make_summary(question, retrieved, edu_level)
+        st.subheader("📘 Summary")
+        st.write(summary)
+
+        # Downloads
+        md_text = summary
+        html_text = markdown2.markdown(summary)
+
+        # PDF
+        pdf_buffer = io.BytesIO()
+        p = canvas.Canvas(pdf_buffer, pagesize=letter)
+        width, height = letter
+        text_obj = p.beginText(40, height - 40)
+        for line in summary.split("\n"):
+            while len(line) > 90:
+                text_obj.textLine(line[:90])
+                line = line[90:]
+            text_obj.textLine(line)
+        p.drawText(text_obj)
+        p.showPage()
+        p.save()
+        pdf_buffer.seek(0)
+
+        # DOCX
+        docx_buffer = io.BytesIO()
+        doc = docx_lib.Document()
+        doc.add_heading("Summary", level=1)
+        for line in summary.split("\n"):
+            doc.add_paragraph(line)
+        doc.save(docx_buffer)
+        docx_buffer.seek(0)
+
+        st.download_button("⬇️ Download Summary (Markdown)", md_text, file_name="summary.md")
+        st.download_button("⬇️ Download Summary (HTML)", html_text, file_name="summary.html", mime="text/html")
+        st.download_button("⬇️ Download Summary (PDF)", pdf_buffer, file_name="summary.pdf", mime="application/pdf")
+        st.download_button("⬇️ Download Summary (DOCX)", docx_buffer, file_name="summary.docx", mime="application/vnd.openxmlformats-officedocument.wordprocessingml.document")
+
+        # MCQs
+        mcq_text = make_mcqs_from_summary(summary, count=int(mcq_count), difficulty=mcq_diff)
+        st.subheader("📝 Generated MCQs")
+        st.write(mcq_text)
+
+        mcq_docx_buf = io.BytesIO()
+        doc_mcq = docx_lib.Document()
+        doc_mcq.add_heading("MCQs", level=1)
+        for line in mcq_text.split("\n"):
+            doc_mcq.add_paragraph(line)
+        doc_mcq.save(mcq_docx_buf)
+        mcq_docx_buf.seek(0)
+
+        mcq_pdf_buf = io.BytesIO()
+        p2 = canvas.Canvas(mcq_pdf_buf, pagesize=letter)
+        text_obj2 = p2.beginText(40, height - 40)
+        for line in mcq_text.split("\n"):
+            while len(line) > 90:
+                text_obj2.textLine(line[:90])
+                line = line[90:]
+            text_obj2.textLine(line)
+        p2.drawText(text_obj2)
+        p2.showPage()
+        p2.save()
+        mcq_pdf_buf.seek(0)
+
+        st.download_button("⬇️ Download MCQs (DOCX)", mcq_docx_buf, file_name="mcqs.docx", mime="application/vnd.openxmlformats-officedocument.wordprocessingml.document")
+        st.download_button("⬇️ Download MCQs (PDF)", mcq_pdf_buf, file_name="mcqs.pdf", mime="application/pdf")
+
+    except Exception as e:
+        st.error(f"Error generating summary or MCQs: {e}")
+else:
+    st.info("Check the box above to generate summary + MCQs from retrieved content.")