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"""
RAG Knowledge Assistant - Hugging Face Spaces Demo
Production-ready Retrieval-Augmented Generation system
"""
import gradio as gr
import os
import numpy as np
from pathlib import Path
from typing import List, Tuple, Dict
import time
# Mock classes for Hugging Face demo
class MockDocumentProcessor:
def __init__(self, chunk_size=400, overlap=50):
self.chunk_size = chunk_size
self.overlap = overlap
def process_text_file(self, file_path: str) -> List[Dict]:
with open(file_path, 'r', encoding='utf-8') as f:
text = f.read()
chunks = []
for i in range(0, len(text), self.chunk_size):
chunk_text = text[i:i + self.chunk_size]
if chunk_text.strip():
chunks.append({
'text': chunk_text.strip(),
'chunk_id': f"chunk_{len(chunks)}",
'source': file_path,
'char_count': len(chunk_text)
})
return chunks
class MockRAGDemo:
def __init__(self):
self.document_processor = MockDocumentProcessor()
self.chunks = []
self.processed_docs = []
print("🚀 RAG Demo initialized")
def process_file(self, file):
if file is None:
return "❌ No file uploaded"
try:
chunks = self.document_processor.process_text_file(file.name)
self.chunks.extend(chunks)
file_name = Path(file.name).name
self.processed_docs.append({
'name': file_name,
'chunks': len(chunks),
'timestamp': time.strftime("%H:%M:%S")
})
return f"✅ Processed {file_name}!\n📊 Created {len(chunks)} chunks\n🎉 Total: {len(self.chunks)} chunks"
except Exception as e:
return f"❌ Error: {str(e)}"
def chat(self, message: str, history: List[Tuple[str, str]]):
if not message.strip():
return "", history
if not self.chunks:
response = "⚠️ Upload a document first!"
history.append((message, response))
return "", history
# Mock search and response
relevant_chunks = self.chunks[:3] # Mock: take first 3 chunks
context = "\n".join([chunk['text'][:200] + "..." for chunk in relevant_chunks])
response = f"""🤖 **Demo Response** (Mock AI for Hugging Face)
Based on your uploaded documents, here's what I found:
**Context:** {context}
**Mock Analysis:** This is a demonstration of the RAG system architecture. In the full version with OpenAI API:
- Real similarity search finds most relevant chunks
- GPT-4 generates contextual responses
- Source attribution with confidence scores
📚 **Sources:** {', '.join([Path(c['source']).name for c in relevant_chunks])}
🔗 **Full Version:** [GitHub Repository](https://github.com/drbinna/rag-knowledge-assistant)"""
history.append((message, response))
return "", history
# Create demo
rag_demo = MockRAGDemo()
with gr.Blocks(title="RAG Knowledge Assistant", theme=gr.themes.Soft()) as demo:
gr.HTML("""
<div style="text-align: center; background: linear-gradient(90deg, #667eea 0%, #764ba2 100%); color: white; padding: 20px; border-radius: 10px; margin-bottom: 20px;">
<h1>🤖 RAG Knowledge Assistant</h1>
<p>Production-ready Retrieval-Augmented Generation system</p>
<p><em>Real Similarity Search • Smart Document Processing • AI Integration</em></p>
</div>
""")
with gr.Tabs():
with gr.TabItem("💬 Chat"):
chatbot = gr.Chatbot(label="Conversation", height=400)
with gr.Row():
msg = gr.Textbox(label="Your Question", placeholder="Ask about your documents...", scale=4)
send_btn = gr.Button("Send", variant="primary", scale=1)
with gr.TabItem("📁 Upload"):
gr.Markdown("### Upload Text Documents")
file_upload = gr.File(label="Choose TXT file", file_types=[".txt"])
upload_btn = gr.Button("Process Document", variant="primary")
upload_status = gr.Textbox(label="Status", lines=5, interactive=False)
with gr.TabItem("ℹ️ About"):
gr.Markdown("""
## RAG Knowledge Assistant
**Production-ready Retrieval-Augmented Generation system**
### 🔧 Features
- Real cosine similarity search with NumPy
- Smart document chunking (400 chars + overlap)
- OpenAI GPT-4 integration
- Professional error handling
### 🚀 Full Version
**[GitHub Repository](https://github.com/drbinna/rag-knowledge-assistant)**
- PDF support
- Local deployment
- Advanced configuration
Built with Python, OpenAI, NumPy, and Gradio.
""")
# Event handlers
msg.submit(rag_demo.chat, [msg, chatbot], [msg, chatbot])
send_btn.click(rag_demo.chat, [msg, chatbot], [msg, chatbot])
upload_btn.click(rag_demo.process_file, file_upload, upload_status)
if __name__ == "__main__":
demo.launch()
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