add files

.gitignore

@@ -0,0 +1,5 @@
+.env
+.env1
+# model.pkl
+.vscode/
+Datasets/

Dockerfile

@@ -0,0 +1,17 @@
+# base image
+FROM python:3.9-slim
+
+# workdir
+WORKDIR /app
+
+# copy
+COPY . /app
+
+# run
+RUN pip install -r requirements.txt
+
+# port
+EXPOSE 5000
+
+# command
+CMD ["streamlit", "run", "app.py", "--server.port=5000", "--server.address=0.0.0.0", "--server.headless=true"]

README.md

@@ -1,11 +1,41 @@
+# 🎬 Book Recommender System (Collaborative Filtering)
+
+![Python](https://img.shields.io/badge/Python-3.9%2B-blue.svg)
+![NLP](https://img.shields.io/badge/Domain-Natural%20Language%20Processing-orange.svg)
+![Scikit-learn](https://img.shields.io/badge/Library-Scikit--learn-yellow.svg)
+![Streamlit](https://img.shields.io/badge/Framework-Streamlit-red.svg)
+![License](https://img.shields.io/badge/License-MIT-green.svg)
+
 ---
-title: Collaborative Filtering Recommender System
-emoji: 🌍
-colorFrom: red
-colorTo: green
-sdk: docker
-pinned: false
-license: mit
+
+## 📖 Project Overview
+
+The **Book Recommender System** is a **collaborative filtering recommendation model** built using Python and NLP techniques.  
+
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+## 🧩 Project Workflow
+
+<p align="center">
+  <img src="/assets/process_flow.png" alt="Workflow" width="850" style="border-radius:8px;">
+</p>
+
+<p align="center">
+  <img src="/assets/demo.png" alt="Workflow" width="850" style="border-radius:8px;">
+</p>
+
+
+
+
+
+
+## 👨‍💻 Author
+
+**Sadmanul Hoque**   
+📧 sadmnulhoque21@gmail.com  
+🌐 [LinkedIn Profile](https://www.linkedin.com/in/sadmanul-hoque/)  
+💾 [GitHub Profile](https://github.com/masterArnob)
+
+
+
+

app.py

@@ -0,0 +1,111 @@
+import streamlit as st
+import joblib
+import time
+
+# -------------------------------------------------
+# Page config & title
+# -------------------------------------------------
+st.set_page_config(page_title="Book Recommender System", layout="wide")
+st.title("Book Recommender System")
+
+# -------------------------------------------------
+# Load model (cached once)
+# -------------------------------------------------
+@st.cache_resource
+def load_model():
+    model = joblib.load("model.pkl")
+    return {
+        "data": model["data"],
+        "similarity": model["similarity"],
+        "pivot_index": model["pivot_index"],
+    }
+
+with st.spinner("Loading model and book catalogue..."):
+    model_data = load_model()
+    data = model_data["data"]
+    similarity = model_data["similarity"]
+    pivot_index = model_data["pivot_index"]
+
+st.success(f"Loaded **{len(pivot_index):,}** books successfully!")
+
+# -------------------------------------------------
+# Safe image URL
+# -------------------------------------------------
+def safe_image_url(book_name: str) -> str:
+    try:
+        url = data[data["book_name"] == book_name].iloc[0]["image_url"]
+        return url if url and isinstance(url, str) and url.strip() else "https://via.placeholder.com/120x180?text=No+Image"
+    except:
+        return "https://via.placeholder.com/120x180?text=No+Image"
+
+# -------------------------------------------------
+# Recommend 7 books
+# -------------------------------------------------
+def recommend(book_name: str):
+    if book_name not in pivot_index:
+        st.error("Book not found in the similarity dataset!")
+        return []
+
+    book_idx = pivot_index.index(book_name)
+    distances = similarity[book_idx]
+    similar_items = sorted(
+        enumerate(distances), key=lambda x: x[1], reverse=True
+    )[1:8]  # Top 7 (skip itself)
+
+    recs = []
+    for idx, _ in similar_items:
+        title = pivot_index[idx]
+        img = safe_image_url(title)
+        recs.append((title, img))
+    return recs
+
+# -------------------------------------------------
+# UI: Book selector
+# -------------------------------------------------
+book_name = st.selectbox(
+    "Select a book:",
+    options=pivot_index,
+    index=None,
+    placeholder="Start typing or choose a book..."
+)
+
+# -------------------------------------------------
+# Recommend button
+# -------------------------------------------------
+if st.button("Recommend"):
+    if not book_name:
+        st.warning("Please select a book first!")
+    else:
+        st.subheader("Recommended Books:")
+        with st.spinner("Finding 7 similar books..."):
+            time.sleep(0.6)  # Optional: makes spinner visible
+            recommendations = recommend(book_name)
+
+        if recommendations:
+            # Force image height to 180px
+            st.markdown(
+                """
+                <style>
+                .book-img img {
+                    height: 180px !important;
+                    width: auto !important;
+                    object-fit: contain;
+                    border-radius: 8px;
+                    box-shadow: 0 2px 6px rgba(0,0,0,0.1);
+                }
+                </style>
+                """,
+                unsafe_allow_html=True,
+            )
+
+            # 7 columns
+            cols = st.columns(7, gap="medium")
+            for i, (title, img_url) in enumerate(recommendations):
+                with cols[i]:
+                    st.markdown(
+                        f'<div class="book-img"><img src="{img_url}"></div>',
+                        unsafe_allow_html=True,
+                    )
+                    st.caption(title, unsafe_allow_html=True)
+        else:
+            st.info("No recommendations found.")

model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:67cd0b1879c077f3c69a5892ec3ae21e31023cab9d03902ab71d68596d92d573
+size 42839711

requirements.txt

@@ -0,0 +1,5 @@
+streamlit==1.50.0
+joblib
+numpy
+pandas
+scikit-learn