Upload folder using huggingface_hub

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2025 Nirman Patel
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1,12 +1,388 @@
 ---
-title: Semantic Book Recommender
-emoji: 🐠
-colorFrom: blue
-colorTo: green
+title: semantic-book-recommender
+app_file: gradio_dashboard.py
 sdk: gradio
 sdk_version: 5.38.0
-app_file: app.py
-pinned: false
 ---
+# 📚 Semantic Book Recommendation System
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+[![Python](https://img.shields.io/badge/python-3.8%2B-blue.svg)](https://www.python.org/downloads/)
+[![Transformers](https://img.shields.io/badge/transformers-4.21.0-orange.svg)](https://huggingface.co/transformers/)
+[![Gradio](https://img.shields.io/badge/gradio-3.40.0-green.svg)](https://gradio.app/)
+[![LangChain](https://img.shields.io/badge/langchain-0.1.0-red.svg)](https://langchain.readthedocs.io/)
+[![License](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE)
+
+A sophisticated book recommendation system that combines semantic search with emotion analysis to provide personalized book suggestions. The system uses vector embeddings, zero-shot classification, and emotion detection to understand user preferences and recommend books based on content similarity and emotional tone.
+
+## 🌟 Features
+
+- **Semantic Search**: Uses HuggingFace embeddings and ChromaDB for vector-based similarity search
+- **Emotion Analysis**: Analyzes book descriptions for emotional content (joy, sadness, anger, fear, surprise, disgust, neutral)
+- **Zero-Shot Classification**: Automatically categorizes books into Fiction/Non-Fiction using BART-large-MNLI
+- **Interactive Dashboard**: Gradio-based web interface for easy book discovery
+- **Advanced Filtering**: Filter by category, emotional tone, and rating
+- **Data Visualization**: Statistical insights and data exploration tools
+
+## 🏗️ System Architecture
+
+```
+books.csv → Data Cleaning → Category Classification → Emotion Analysis → Vector Database → Gradio UI
+```
+
+### Pipeline Components:
+
+1. **Data Exploration & Cleaning** (`data_exploration.py`)
+   - Handles missing values and data quality issues
+   - Filters books with substantial descriptions (25+ words)
+   - Creates correlation analysis and visualizations
+
+2. **Text Classification** (`text_classification.py`)
+   - Zero-shot classification for Fiction/Non-Fiction categorization
+   - Uses Facebook's BART-large-MNLI model
+   - Achieves high accuracy in automated categorization
+
+3. **Sentiment Analysis** (`sentiment_analysis.py`)
+   - Emotion detection using DistilRoBERTa model
+   - Analyzes 7 emotions: anger, disgust, fear, joy, sadness, surprise, neutral
+   - Sentence-level emotion scoring with max aggregation
+
+4. **Vector Search** (`vector_search.py`)
+   - Creates embeddings using HuggingFace sentence-transformers
+   - Implements ChromaDB for efficient similarity search
+   - Supports semantic book discovery
+
+5. **Gradio Dashboard** (`gradio_dashboard.py`)
+   - Interactive web interface for book recommendations
+   - Real-time filtering and visualization
+   - Statistical dashboards and data insights
+
+## 📁 Project Structure
+
+```
+semantic-book-recommender/
+├── 📄 Core Files
+│   ├── .env.example                 # Template for environment variables
+│   ├── .gitignore                   # Git ignore file (IMPORTANT!)
+│   ├── README.md                    # This file
+│   └── requirements.txt             # Python dependencies
+│
+├── 🐍 Python Scripts
+│   ├── data_exploration.py          # Data cleaning and exploration
+│   ├── text_classification.py       # Zero-shot classification
+│   ├── sentiment_analysis.py        # Emotion analysis
+│   ├── vector_search.py            # Vector database operations
+│   └── gradio_dashboard.py         # Web interface
+│
+├── 📊 Data Files (Generated/Input)
+│   ├── books.csv                   # Input dataset (not included in repo)
+│   ├── books_cleaned.csv           # Cleaned dataset
+│   ├── books_with_categories.csv   # Dataset with categories
+│   ├── books_with_emotions.csv     # Final dataset with emotions
+│   ├── tagged_description.txt      # Generated text file for embeddings
+│   └── predictions_results.csv     # Classification results
+│
+├── 🖼️ Assets
+│   └── cover-not-found.jpg         # Default book cover image
+│
+├── 🗄️ Vector Databases (Auto-generated)
+│   ├── chroma_db_books/            # OpenAI embeddings vector DB
+│   └── chroma_db_books_hf/         # HuggingFace embeddings vector DB
+│
+└── 🔧 Environment (Ignored)
+    ├── .env                        # Your API keys (NEVER commit!)
+    └── .venv/                      # Virtual environment (ignored)
+```
+
+### 📋 File Descriptions
+
+| File | Purpose | Generated By |
+|------|---------|--------------|
+| `data_exploration.py` | Data cleaning, missing value analysis, correlation heatmaps | Manual |
+| `text_classification.py` | Zero-shot classification (Fiction/Non-Fiction) | Manual |
+| `sentiment_analysis.py` | Emotion analysis (7 emotions) | Manual |
+| `vector_search.py` | Vector embeddings and similarity search | Manual |
+| `gradio_dashboard.py` | Interactive web interface | Manual |
+| `books.csv` | Original dataset | User provided |
+| `books_cleaned.csv` | Cleaned dataset (25+ word descriptions) | `data_exploration.py` |
+| `books_with_categories.csv` | Dataset with Fiction/Non-Fiction labels | `text_classification.py` |
+| `books_with_emotions.csv` | Final dataset with emotion scores | `sentiment_analysis.py` |
+| `tagged_description.txt` | Text file for vector embeddings | `vector_search.py` |
+| `predictions_results.csv` | Classification accuracy results | `text_classification.py` |
+
+### 🔄 Processing Pipeline
+
+```
+books.csv 
+    ↓ (data_exploration.py)
+books_cleaned.csv 
+    ↓ (text_classification.py)
+books_with_categories.csv 
+    ↓ (sentiment_analysis.py)
+books_with_emotions.csv 
+    ↓ (vector_search.py)
+tagged_description.txt + Vector DB
+    ↓ (gradio_dashboard.py)
+📱 Web Interface
+```
+
+## 🔒 Security Setup (IMPORTANT!)
+
+### Before uploading to GitHub:
+
+1. **Create `.gitignore` file** (copy the one provided below)
+2. **Never commit `.env` files** - they contain your API keys
+3. **Use `.env.example`** as a template for others
+4. **Remove any API keys** from code files
+
+### Required `.gitignore` file:
+```gitignore
+# Environment variables (NEVER commit these!)
+.env
+.env.local
+.env.development.local
+.env.test.local
+.env.production.local
+
+# Virtual environment
+venv/
+.venv/
+env/
+ENV/
+
+# Python cache
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# Vector databases (large files)
+chroma_db_books/
+chroma_db_books_hf/
+*.db
+*.sqlite
+
+# Data files (add to .gitignore if sensitive)
+books.csv
+books_cleaned.csv
+books_with_categories.csv
+books_with_emotions.csv
+tagged_description.txt
+predictions_results.csv
+
+# IDE files
+.vscode/
+.idea/
+*.swp
+*.swo
+*~
+
+# OS files
+.DS_Store
+.DS_Store?
+._*
+.Spotlight-V100
+.Trashes
+ehthumbs.db
+Thumbs.db
+
+# Jupyter Notebook checkpoints
+.ipynb_checkpoints
+
+# PyTorch model files
+*.pth
+*.pt
+
+# Logs
+*.log
+logs/
+```
+
+## 🚀 Quick Start
+
+### Prerequisites
+
+- Python 3.8 or higher
+- Virtual environment (recommended)
+
+### Installation
+
+1. Clone the repository:
+```bash
+git clone https://github.com/yourusername/semantic-book-recommender.git
+cd semantic-book-recommender
+```
+
+2. Create and activate virtual environment:
+```bash
+python -m venv venv
+source venv/bin/activate  # On Windows: venv\Scripts\activate
+```
+
+3. Install dependencies:
+```bash
+pip install -r requirements.txt
+```
+
+4. Set up environment variables:
+```bash
+cp .env.example .env
+# Edit .env with your OpenAI API key (optional, for OpenAI embeddings)
+```
+
+### Running the System
+
+1. **Data Processing Pipeline**:
+```bash
+# Step 1: Clean and explore data
+python data_exploration.py
+
+# Step 2: Classify books into categories
+python text_classification.py
+
+# Step 3: Analyze emotions in book descriptions
+python sentiment_analysis.py
+
+# Step 4: Create vector database
+python vector_search.py
+```
+
+2. **Launch Dashboard**:
+```bash
+python gradio_dashboard.py
+```
+
+Access the dashboard at `http://localhost:7860`
+
+## 📊 Data Requirements
+
+The system expects a `books.csv` file with the following columns:
+- `isbn13`: Unique book identifier
+- `title`: Book title
+- `subtitle`: Book subtitle (optional)
+- `authors`: Author names (semicolon-separated)
+- `categories`: Book categories
+- `description`: Book description
+- `num_pages`: Number of pages
+- `average_rating`: Average rating (1-5 scale)
+- `published_year`: Publication year
+- `thumbnail`: Book cover image URL
+
+## 🎯 Usage Examples
+
+### Semantic Search
+```python
+from vector_search import retrieve_semantic_recommendations
+
+# Find books similar to a query
+results = retrieve_semantic_recommendations(
+    "A mystery novel about redemption and forgiveness",
+    top_k=10
+)
+```
+
+### Emotion-Based Filtering
+```python
+# Get happy books in fiction category
+recommendations = retrieve_semantic_recommendations(
+    query="adventure story",
+    category="Fiction",
+    tone="Happy"
+)
+```
+
+## 🔧 Configuration
+
+### Model Settings
+- **Embedding Model**: `sentence-transformers/all-MiniLM-L6-v2` (384 dimensions)
+- **Classification Model**: `facebook/bart-large-mnli`
+- **Emotion Model**: `j-hartmann/emotion-english-distilroberta-base`
+
+### Performance Tuning
+- Adjust `initial_top_k` and `final_top_k` in recommendation functions
+- Modify chunk size and overlap in text splitting
+- Configure vector database persistence settings
+
+## 📈 Model Performance
+
+- **Zero-Shot Classification Accuracy**: ~85% on Fiction/Non-Fiction categorization
+- **Emotion Detection**: 7-class emotion classification with confidence scores
+- **Semantic Search**: Cosine similarity-based ranking with embedding vectors
+
+## 🛠️ Technical Details
+
+### Dependencies
+- **Core ML**: `transformers`, `torch`, `sentence-transformers`
+- **Vector Database**: `chromadb`, `langchain`
+- **Data Processing**: `pandas`, `numpy`
+- **Visualization**: `matplotlib`, `seaborn`, `gradio`
+- **Utilities**: `tqdm`, `tabulate`, `python-dotenv`
+
+### Hardware Requirements
+- **RAM**: 8GB+ recommended for model loading
+- **GPU**: Optional, supports CUDA/MPS for faster inference
+- **Storage**: 2GB+ for model weights and vector database
+
+## 📝 API Reference
+
+### Main Functions
+
+#### `retrieve_semantic_recommendations(query, category, tone, initial_top_k, final_top_k)`
+Returns book recommendations based on semantic similarity and filters.
+
+**Parameters:**
+- `query` (str): Search query describing desired book
+- `category` (str): Book category filter ("All", "Fiction", "Non-Fiction", etc.)
+- `tone` (str): Emotional tone filter ("Happy", "Sad", "Suspenseful", etc.)
+- `initial_top_k` (int): Initial number of candidates to retrieve
+- `final_top_k` (int): Final number of recommendations to return
+
+**Returns:**
+- `pandas.DataFrame`: Filtered book recommendations with metadata
+
+## 🤝 Contributing
+
+1. Fork the repository
+2. Create a feature branch (`git checkout -b feature/amazing-feature`)
+3. Commit changes (`git commit -m 'Add amazing feature'`)
+4. Push to branch (`git push origin feature/amazing-feature`)
+5. Open a Pull Request
+
+## 📄 License
+
+This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
+
+## 🙏 Acknowledgments
+
+- HuggingFace for providing pre-trained models
+- OpenAI for embedding models
+- ChromaDB for vector database functionality
+- Gradio for the intuitive web interface
+- The open-source community for various Python libraries
+
+## 📚 References
+
+- [Sentence Transformers Documentation](https://www.sbert.net/)
+- [LangChain Documentation](https://python.langchain.com/)
+- [Gradio Documentation](https://gradio.app/docs/)
+- [ChromaDB Documentation](https://docs.trychroma.com/)
+
+---
+
+**Note**: This system is designed for educational and research purposes. Ensure compliance with data usage policies and model licenses when deploying in production environments.

gradio_dashboard.py

@@ -0,0 +1,219 @@
+import pandas as pd
+import numpy as np
+from dotenv import load_dotenv
+from langchain_community.document_loaders import TextLoader
+from langchain_openai import OpenAIEmbeddings
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_text_splitters import CharacterTextSplitter
+from langchain_chroma import Chroma
+import matplotlib.pyplot as plt
+import seaborn as sns
+import io
+import gradio as gr
+from PIL import Image
+
+load_dotenv()
+
+books = pd.read_csv("books_with_emotions.csv")
+books["large_thumbnail"] = books["thumbnail"] + "&fife=w800"
+books ["large_thumbnail"] = np.where(
+    books["large_thumbnail"].isna(),
+    "cover-not-found.jpg",
+    books ["large_thumbnail"],
+)
+
+raw_documents = TextLoader("tagged_description.txt").load()
+text_splitter = CharacterTextSplitter(separator="\n", chunk_size=0, chunk_overlap=0)
+documents = text_splitter.split_documents(raw_documents)
+db_books = Chroma.from_documents(documents, HuggingFaceEmbeddings())
+
+def retrieve_semantic_recommendations(
+    query: str,
+    category: str = None,
+    tone: str = None,
+    initial_top_k: int = 50,
+    final_top_k: int = 16,
+) -> pd.DataFrame:
+    recs = db_books.similarity_search_with_score(query, k=initial_top_k)
+    books_list = [
+        int(rec[0].page_content.strip('"').split()[0]) if isinstance(rec, tuple)
+        else int(rec.page_content.strip('"').split()[0])
+        for rec in recs]    
+    book_recs = books.loc[books["isbn13"].isin(books_list)]
+    if category != "All":
+        book_recs = book_recs[book_recs["simple_categories"] == category]
+
+    # Tone-based emotion sorting
+    if tone and tone != "All":
+        tone_column_map = {
+            "Happy": "joy",
+            "Surprising": "surprise",
+            "Angry": "anger",
+            "Suspenseful": "fear",
+            "Sad": "sadness"
+        }
+        tone_col = tone_column_map.get(tone)
+        if tone_col and tone_col in book_recs.columns:
+            book_recs = book_recs.sort_values(by=tone_col, ascending=False)
+
+    return book_recs.head(final_top_k)
+
+def recommend_books(
+        query: str,
+        category: str,
+        tone: str
+):
+    recommendations = retrieve_semantic_recommendations(query, category, tone)
+    results = []
+
+    for _, row in recommendations.iterrows ():
+        description = row["description"]
+        truncated_desc_split = description.split()
+        truncated_description = " ".join(truncated_desc_split[:30]) + "..."
+
+        authors_split = row["authors"].split(";")
+        if len(authors_split) == 2:
+            authors_str = f"{authors_split[0]} and {authors_split[1]}"
+        elif len(authors_split) > 2:
+            authors_str = f"{', '.join(authors_split[:-1])}, and {authors_split[-1]}"
+        else:
+            authors_str = row["authors"]
+
+        caption = f"{row['title']} by {authors_str}: {truncated_description}"
+        results.append((row["large_thumbnail"], caption))
+    
+    return results
+
+# --- Functions for Visuals and Stats --- #
+def plot_pie(column):
+    fig, ax = plt.subplots(figsize=(6, 6))
+    books[column].fillna("Unknown").value_counts().head(5).plot.pie(autopct="%1.1f%%", startangle=90, ax=ax)
+    ax.set_ylabel("")
+    ax.set_title(f"Top 5 {column} Distribution")
+    buf = io.BytesIO()
+    plt.savefig(buf, format="png", bbox_inches="tight")
+    buf.seek(0)
+    plt.close()
+    return Image.open(buf)
+
+def get_missing_df():
+    return books.isnull().sum().reset_index().rename(columns={"index": "Column", 0: "Missing Values"})
+
+def get_summary_df():
+    return books.describe(include="all").T.fillna("").reset_index().rename(columns={"index": "Column"})
+
+def filter_by_rating(min_rating):
+    filtered = books[books["average_rating"] >= min_rating]
+    return filtered[["title", "average_rating", "authors"]].head(20)
+
+def plot_author_boxplot():
+    fig, ax = plt.subplots(figsize=(8, 4))
+    author_counts = books["simple_categories"].value_counts().index[:5]
+    data = books[books["simple_categories"].isin(author_counts)]
+    data["num_authors"] = data["authors"].fillna("").apply(lambda x: len(str(x).split(";")))
+    sns.boxplot(data=data, x="simple_categories", y="num_authors", hue="simple_categories", palette="Set2", ax=ax, legend=False)
+    ax.set_title("Number of Authors per Category")
+    ax.set_ylabel("Number of Authors")
+    ax.set_xlabel("Category")
+    buf = io.BytesIO()
+    plt.savefig(buf, format="png", bbox_inches="tight")
+    buf.seek(0)
+    plt.close()
+    return Image.open(buf)
+
+def get_thumbnails(category):
+    df = books[books["simple_categories"] == category].dropna(subset=["thumbnail"]).head(8)
+    return list(df["thumbnail"])
+
+# Category & tone setup
+categories = ["All"] + sorted(books["simple_categories"].unique())
+tones = ["All", "Happy", "Surprising", "Angry", "Suspenseful", "Sad"]
+
+# Custom theme
+custom_theme = gr.themes.Base(
+    primary_hue="violet",
+    secondary_hue="stone",
+    font=["Plus-jakarta-sans", "sans-serif"]
+)
+
+# Gradio UI
+with gr.Blocks(theme=custom_theme) as dashboard:
+    with gr.Tab("🔍 Recommender"):
+        gr.Markdown("""
+        <style>
+        #form-section {
+            padding: 18px;
+            border: 1px solid #dcdcdc;
+            border-radius: 15px;
+            background: #fdfdfd;
+            margin-bottom: 1.5rem;
+        }
+        .title {
+            font-size: 32px;
+            font-weight: bold;
+            text-align: center;
+            margin-bottom: 1em;
+        }
+        </style>
+        """)
+
+        gr.Markdown("# 📚 Semantic Book Recommender", elem_classes="title")
+        gr.Markdown("Describe your ideal book and get smart recommendations based on semantics and emotions 🎯")
+
+        with gr.Group(elem_id="form-section"):
+            with gr.Row():
+                user_query = gr.Textbox(
+                    label="🔍 Book Description",
+                    placeholder="e.g., A story about forgiveness, mystery, and redemption",
+                    lines=2
+                )
+            with gr.Row():
+                category_dropdown = gr.Dropdown(choices=categories, label="📂 Category", value="All")
+                tone_dropdown = gr.Dropdown(choices=tones, label="🎭 Emotional Tone", value="All")
+            with gr.Row():
+                submit_button = gr.Button("🚀 Find Recommendations", variant="primary")
+
+        gr.Markdown("## 🧠 Smart Recommendations")
+        output = gr.Gallery(label="📚 Recommended Books", columns=4, rows=2, height="auto", preview=False)
+
+        submit_button.click(
+            fn=recommend_books,
+            inputs=[user_query, category_dropdown, tone_dropdown],
+            outputs=output
+        )
+
+    with gr.Tab("📊 Dataset Statistics"):
+        gr.Markdown("## 🧮 Dataset Summary Table")
+        gr.Dataframe(value=get_summary_df(), interactive=False)
+
+        gr.Markdown("## ❓ Missing Values Table")
+        gr.Dataframe(value=get_missing_df(), interactive=False)
+
+        gr.Markdown("## 🧁 Pie Chart Visualization")
+        categorical_cols = books.select_dtypes(include=["object", "category"]).columns.tolist()
+        col_dropdown = gr.Dropdown(
+            choices=categorical_cols,
+            value=categorical_cols[0] if categorical_cols else None,
+            label="Select Column"
+        )
+        pie_img = gr.Image(type="pil", label="Pie Chart")
+        col_dropdown.change(fn=plot_pie, inputs=col_dropdown, outputs=pie_img)
+
+        gr.Markdown("## 🌡️ Histogram Filter by Rating")
+        rating_slider = gr.Slider(minimum=0, maximum=5, step=0.1, value=3.5, label="Minimum Rating")
+        rating_table = gr.Dataframe(label="Books Above Rating", interactive=False)
+        rating_slider.change(fn=filter_by_rating, inputs=rating_slider, outputs=rating_table)
+
+        gr.Markdown("## 📦 Boxplot: Authors per Category")
+        box_img = gr.Image(type="pil", value=plot_author_boxplot, label="Author Count Boxplot")
+
+        gr.Markdown("## 🖼️ Top Book Covers by Category")
+        cat_dropdown = gr.Dropdown(choices=books["simple_categories"].dropna().unique().tolist(), label="Select Category")
+        gallery = gr.Gallery(label="Thumbnails", columns=4, height="auto")
+        cat_dropdown.change(fn=get_thumbnails, inputs=cat_dropdown, outputs=gallery)
+
+
+# Run app
+if __name__ == "__main__":
+    dashboard.launch()
+

predictions_results.csv

@@ -0,0 +1,601 @@
+actual_categories,predicted_categories
+Fiction,Fiction
+Fiction,Fiction
+Fiction,Fiction
+Fiction,Nonfiction
+Fiction,Fiction
+Fiction,Fiction
+Fiction,Fiction
+Fiction,Fiction
+Fiction,Fiction
+Fiction,Fiction
+Fiction,Fiction
+Fiction,Fiction
+Fiction,Fiction
+Fiction,Fiction
+Fiction,Fiction
+Fiction,Fiction
+Fiction,Fiction
+Fiction,Fiction
+Fiction,Fiction
+Fiction,Nonfiction
+Fiction,Fiction
+Fiction,Nonfiction
+Fiction,Nonfiction
+Fiction,Fiction
+Fiction,Fiction
+Fiction,Nonfiction
+Fiction,Fiction
+Fiction,Nonfiction
+Fiction,Nonfiction
+Fiction,Fiction
+Fiction,Fiction
+Fiction,Fiction
+Fiction,Fiction
+Fiction,Fiction
+Fiction,Nonfiction
+Fiction,Nonfiction
+Fiction,Fiction
+Fiction,Fiction
+Fiction,Nonfiction
+Fiction,Fiction
+Fiction,Nonfiction
+Fiction,Nonfiction
+Fiction,Fiction
+Fiction,Fiction
+Fiction,Fiction
+Fiction,Fiction
+Fiction,Fiction
+Fiction,Nonfiction
+Fiction,Fiction
+Fiction,Fiction
+Fiction,Fiction
+Fiction,Fiction
+Fiction,Nonfiction
+Fiction,Fiction
+Fiction,Fiction
+Fiction,Nonfiction
+Fiction,Fiction
+Fiction,Fiction
+Fiction,Fiction
+Fiction,Fiction
+Fiction,Fiction
+Fiction,Fiction
+Fiction,Fiction
+Fiction,Fiction
+Fiction,Fiction
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requirements.txt

@@ -0,0 +1,38 @@
+# Core ML and NLP libraries
+transformers>=4.21.0
+torch>=1.12.0
+sentence-transformers>=2.2.0
+tokenizers>=0.13.0
+
+# Vector database and search
+chromadb>=0.4.0
+langchain>=0.1.0
+langchain-community>=0.0.20
+langchain-chroma>=0.1.0
+langchain-openai>=0.0.5
+langchain-huggingface>=0.0.1
+langchain-text-splitters>=0.0.1
+
+# Data processing and analysis
+pandas>=1.5.0
+numpy>=1.21.0
+scikit-learn>=1.1.0
+
+# Visualization and UI
+matplotlib>=3.5.0
+seaborn>=0.11.0
+gradio>=3.40.0
+plotly>=5.10.0
+Pillow>=9.0.0
+
+# Utilities
+tqdm>=4.64.0
+tabulate>=0.8.0
+python-dotenv>=0.19.0
+
+# OpenAI (optional, for OpenAI embeddings)
+openai>=1.0.0
+
+# Additional dependencies for specific functionalities
+datasets>=2.5.0
+accelerate>=0.21.0