Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,294 @@
-import altair as alt
+# =============================================================
+# 📘 USTP Student Handbook Assistant (2023 Edition)
+# =============================================================
+# Enhanced: dynamic model selection + real (printed) page numbering
+
+import os
+import glob
+import json
+import time
+from typing import List, Dict, Any
 import numpy as np
-import pandas as pd
 import streamlit as st
+import PyPDF2
+import requests
+from dotenv import load_dotenv
+from huggingface_hub import InferenceClient, login
+from streamlit_chat import message as st_message
+
+# Optional: FAISS for fast vector search
+try:
+    import faiss
+except ImportError:
+    faiss = None
+
+# =============================================================
+# 🌐 Startup Fix for PermissionError
+# =============================================================
+os.environ["STREAMLIT_HOME"] = "/tmp/.streamlit"
+os.makedirs("/tmp/.streamlit", exist_ok=True)
+
+# =============================================================
+# ⚙️ Streamlit Page Setup
+# =============================================================
+st.set_page_config(page_title="📘 Handbook Assistant", page_icon="📘", layout="wide")
+st.title("📘 USTP Student Handbook Assistant (2023 Edition)")
+st.caption("Answers sourced only from the official *USTP Student Handbook 2023 Edition.pdf*.")
+
+load_dotenv()
+HF_TOKEN = os.getenv("HF_TOKEN")
+
+if not HF_TOKEN:
+    st.warning("⚠️ No Hugging Face API token found in .env file. Online models will be unavailable.")
+else:
+    try:
+        login(HF_TOKEN)
+    except Exception:
+        pass
+
+hf_client = InferenceClient(token=HF_TOKEN) if HF_TOKEN else None
+
+# =============================================================
+# ⚙️ Sidebar Configuration
+# =============================================================
+with st.sidebar:
+    st.header("⚙️ Settings")
+
+    model_options = {
+        "Qwen 2.5 14B Instruct": "Qwen/Qwen2.5-14B-Instruct",
+        "Mistral 7B Instruct": "mistralai/Mistral-7B-Instruct-v0.3",
+        "Llama 3 8B Instruct": "meta-llama/Meta-Llama-3-8B-Instruct",
+        "Mixtral 8x7B Instruct": "mistralai/Mixtral-8x7B-Instruct-v0.1",
+        "Falcon 7B Instruct": "tiiuae/falcon-7b-instruct",
+    }
+    model_choice = st.selectbox("Select reasoning model", list(model_options.keys()), index=0)
+    DEFAULT_MODEL = model_options[model_choice]
+
+    st.markdown("---")
+    similarity_threshold = st.slider("Similarity threshold", 0.3, 1.0, 0.6, 0.01)
+    top_k = st.slider("Top K retrieved chunks", 1, 10, 4)
+    chunk_size_chars = st.number_input("Chunk size (chars)", 400, 2500, 1200, 100)
+    chunk_overlap = st.number_input("Chunk overlap (chars)", 20, 600, 150, 10)
+    front_matter_pages = st.number_input(
+        "Pages before main content (e.g. table of contents, cover)", min_value=0, max_value=50, value=12
+    )
+    regenerate_index = st.button("🔁 Rebuild handbook index")
+
+# =============================================================
+# 📂 File Config
+# =============================================================
+INDEX_FILE = "handbook_faiss.index"
+META_FILE = "handbook_metadata.json"
+EMB_DIM_FILE = "handbook_emb_dim.json"
+EMBED_MODEL = "sentence-transformers/all-mpnet-base-v2"
+
+# =============================================================
+# 🧩 Utility Functions
+# =============================================================
+def find_handbook() -> List[str]:
+    preferred = "USTP Student Handbook 2023 Edition.pdf"
+    pdfs = glob.glob("*.pdf")
+    for f in pdfs:
+        if preferred.lower() in f.lower():
+            st.success(f"📘 Found handbook: {f}")
+            return [f]
+    if pdfs:
+        st.warning(f"⚠️ Preferred handbook not found. Using {os.path.basename(pdfs[0])}.")
+        return [pdfs[0]]
+    st.error("❌ No PDF found in current folder.")
+    return []
+
+
+def load_pdf_texts(pdf_paths: List[str]) -> List[Dict[str, Any]]:
+    """Extract page text while adjusting page numbering to printed handbook numbers."""
+    pages = []
+    for path in pdf_paths:
+        with open(path, "rb") as f:
+            reader = PyPDF2.PdfReader(f)
+            for i, page in enumerate(reader.pages):
+                text = page.extract_text() or ""
+                if text.strip():
+                    # Adjust logical page number to printed numbering
+                    logical_page = i + 1
+                    printed_page = logical_page - front_matter_pages
+                    if printed_page < 1:
+                        printed_page = 1
+                    pages.append({
+                        "filename": os.path.basename(path),
+                        "page": printed_page,
+                        "text": text.strip()
+                    })
+    return pages
+
+
+def chunk_text(pages: List[Dict[str, Any]], size: int, overlap: int) -> List[Dict[str, Any]]:
+    chunks = []
+    for p in pages:
+        text = p["text"]
+        start = 0
+        while start < len(text):
+            end = start + size
+            chunk = text[start:end]
+            chunks.append({
+                "filename": p["filename"],
+                "page": p["page"],
+                "content": chunk.strip()
+            })
+            start += size - overlap
+    return chunks
 
-"""
-# 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).
+def embed_texts(texts: List[str]) -> np.ndarray:
+    """Generate embeddings using Hugging Face feature extraction."""
+    if not HF_TOKEN or not hf_client:
+        st.error("❌ Missing Hugging Face token or client.")
+        return np.zeros((len(texts), 768))
+    try:
+        embeddings = hf_client.feature_extraction(texts, model=EMBED_MODEL)
+        if isinstance(embeddings[0][0], list):
+            embeddings = [np.mean(np.array(e), axis=0) for e in embeddings]
+        return np.array(embeddings)
+    except Exception as e1:
+        st.warning(f"⚠️ feature_extraction failed, using REST API fallback: {e1}")
+        headers = {"Authorization": f"Bearer {HF_TOKEN}"}
+        resp = requests.post(
+            f"https://api-inference.huggingface.co/models/{EMBED_MODEL}",
+            headers=headers,
+            json={"inputs": texts}
+        )
+        data = resp.json()
+        if isinstance(data[0][0], list):
+            data = [np.mean(np.array(e), axis=0) for e in data]
+        return np.array(data)
 
-In the meantime, below is an example of what you can do with just a few lines of code:
+
+def build_faiss_index(chunks: List[Dict[str, Any]]):
+    """Build FAISS index for chunks."""
+    texts = [c["content"] for c in chunks]
+    embeddings = embed_texts(texts)
+    if embeddings.size == 0:
+        st.error("❌ Embedding generation failed.")
+        return
+    dim = embeddings.shape[1]
+    index = faiss.IndexFlatL2(dim)
+    index.add(embeddings.astype("float32"))
+    faiss.write_index(index, INDEX_FILE)
+    with open(META_FILE, "w") as f:
+        json.dump(chunks, f)
+    with open(EMB_DIM_FILE, "w") as f:
+        json.dump({"dim": dim}, f)
+    st.success(f"✅ Indexed {len(chunks)} chunks.")
+
+
+def load_faiss_index():
+    if not os.path.exists(INDEX_FILE) or not os.path.exists(META_FILE):
+        return None, None
+    index = faiss.read_index(INDEX_FILE)
+    with open(META_FILE) as f:
+        meta = json.load(f)
+    return index, meta
+
+
+def search_index(query: str, index, meta, top_k: int, threshold: float):
+    query_emb = embed_texts([query])
+    distances, indices = index.search(query_emb.astype("float32"), top_k)
+    results = []
+    for i, dist in zip(indices[0], distances[0]):
+        if i < len(meta):
+            r = meta[i]
+            r["distance"] = float(dist)
+            results.append(r)
+    return results
+
+
+def generate_answer(context: str, query: str) -> str:
+    """Generate model-based answer using selected open-source model."""
+    prompt = f"""
+You are a precise academic assistant specialized in university policy.
+Use only the *USTP Student Handbook 2023 Edition* below.
+If the answer is not in the text, reply:
+"The handbook does not specify that."
+
+---
+📘 Context:
+{context}
+---
+🧭 Question:
+{query}
+---
+🎯 Instructions:
+- Be factual and concise.
+- Cite the correct printed page number.
+- Never make assumptions.
 """
 
-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])),
-    ))
+    try:
+        response = hf_client.text_generation(
+            model=DEFAULT_MODEL,
+            prompt=prompt,
+            max_new_tokens=400,
+            temperature=0.25
+        )
+        return response if isinstance(response, str) else str(response)
+    except Exception as e1:
+        try:
+            chat_response = hf_client.chat.completions.create(
+                model=DEFAULT_MODEL,
+                messages=[{"role": "user", "content": prompt}],
+                max_tokens=400
+            )
+            return chat_response.choices[0].message["content"]
+        except Exception as e2:
+            return f"⚠️ Error generating answer: {e2}"
+
+
+def ensure_index():
+    """Ensure FAISS index exists or rebuild."""
+    if regenerate_index or not os.path.exists(INDEX_FILE):
+        pdfs = find_handbook()
+        if not pdfs:
+            st.stop()
+        st.info("📄 Extracting handbook text...")
+        pages = load_pdf_texts(pdfs)
+        chunks = chunk_text(pages, chunk_size_chars, chunk_overlap)
+        build_faiss_index(chunks)
+    index, meta = load_faiss_index()
+    if index is None or meta is None:
+        st.error("❌ Could not load FAISS index.")
+        st.stop()
+    return index, meta
+
+# =============================================================
+# 💬 Chat Interface
+# =============================================================
+st.divider()
+st.subheader("💬 Ask about the Handbook")
+
+if "history" not in st.session_state:
+    st.session_state.history = []
+
+user_query = st.text_input("Enter your question:")
+index, meta = ensure_index()
+
+if st.button("Ask") and user_query.strip():
+    results = search_index(user_query, index, meta, top_k, similarity_threshold)
+    if not results:
+        st.warning("No relevant section found in the handbook.")
+    else:
+        context = "\n\n".join(
+            [f"(📄 Page {r['page']})\n{r['content']}" for r in results]
+        )
+        answer = generate_answer(context, user_query)
+        st.session_state.history.append({
+            "user": user_query,
+            "assistant": answer,
+            "timestamp": time.time()
+        })
+
+# ✅ Ensure unique keys to prevent StreamlitDuplicateElementId
+for i, chat in enumerate(st.session_state.history):
+    st_message(chat["user"], is_user=True, key=f"user_{i}")
+    st_message(chat["assistant"], key=f"assistant_{i}")
+
+st.caption("⚡ Powered by FAISS + Open Source Models + Accurate Page Referencing")
+