Create App.py

App.py

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+"""
+Hybrid RAG System - Production Demo
+Deployed on HuggingFace Spaces
+"""
+
+import gradio as gr
+import pandas as pd
+import numpy as np
+import torch
+from sentence_transformers import SentenceTransformer, util
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import nltk
+
+# Download NLTK data
+try:
+    nltk.download('stopwords', quiet=True)
+    nltk.download('punkt', quiet=True)
+except:
+    pass
+
+print("🚀 Loading Hybrid RAG System...")
+
+class SimpleRAGDemo:
+    def __init__(self):
+        self.device = "cpu"  # Force CPU for HF Spaces
+        
+        print("Loading models...")
+        # Embedding model
+        self.emb_model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
+        
+        # Generator
+        self.tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-small")
+        self.model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-small")
+        
+        # Sample corpus (for demo)
+        self.texts = [
+            "machine learning is a subset of artificial intelligence that enables computers to learn from data without explicit programming",
+            "deep learning uses artificial neural networks with multiple layers to process information in a hierarchical manner",
+            "natural language processing helps computers understand analyze and generate human language",
+            "retrieval augmented generation combines information retrieval with language generation for more accurate responses",
+            "transformers are neural network architectures that use self-attention mechanisms for processing sequential data",
+            "bert is a transformer-based model pretrained on large text corpora for natural language understanding tasks",
+            "question answering systems retrieve relevant information and generate concise answers to user queries",
+            "semantic search uses embeddings to find documents based on meaning rather than exact keyword matches",
+            "neural embeddings represent words or documents as dense vectors in a continuous space",
+            "language models predict the probability of word sequences and can generate coherent text"
+        ]
+        
+        print("Creating embeddings...")
+        self.embeddings = self.emb_model.encode(self.texts, convert_to_tensor=True)
+        
+        print("✅ System ready!")
+    
+    def retrieve_and_answer(self, query, top_k=3):
+        if not query or len(query.strip()) < 3:
+            return "❌ Please enter a valid question.", ""
+        
+        # Dense retrieval
+        query_emb = self.emb_model.encode(query, convert_to_tensor=True)
+        similarities = util.cos_sim(query_emb, self.embeddings)[0]
+        top_idx = torch.argsort(similarities, descending=True)[:top_k]
+        
+        contexts = [self.texts[i] for i in top_idx]
+        scores = [float(similarities[i]) for i in top_idx]
+        
+        # Generate answer
+        context_text = "\n".join([f"[{i+1}] {c}" for i, c in enumerate(contexts)])
+        prompt = f"Answer based on context:\n{context_text}\n\nQuestion: {query}\nAnswer:"
+        
+        inputs = self.tokenizer(prompt, max_length=512, truncation=True, return_tensors="pt")
+        
+        with torch.no_grad():
+            outputs = self.model.generate(**inputs, max_length=100)
+        
+        answer = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+        
+        # Format outputs
+        answer_md = f"""
+### 🤖 Generated Answer
+
+**{answer}**
+
+---
+
+### 📊 Key Research Finding
+
+This demo showcases my Master's thesis work where I achieved a **64.5% improvement** 
+in semantic similarity using dense retrieval compared to traditional sparse methods.
+
+**Evaluation:** 198 queries | 5,000 documents | MS MARCO dataset
+"""
+        
+        retrieved_md = "### 📄 Retrieved Documents\n\n"
+        for i, (ctx, score) in enumerate(zip(contexts, scores), 1):
+            retrieved_md += f"**[{i}]** Relevance: `{score:.4f}`\n\n{ctx}\n\n---\n\n"
+        
+        return answer_md, retrieved_md
+
+# Initialize system
+print("Initializing RAG system...")
+rag = SimpleRAGDemo()
+
+# Create Gradio interface
+demo = gr.Blocks(
+    title="Hybrid RAG System - Master's Thesis Demo",
+    theme=gr.themes.Soft()
+)
+
+with demo:
+    gr.Markdown("""
+    # 🎯 Hybrid RAG System - Interactive Demo
+    
+    **Master's Thesis Project** | AI-Powered Evidence-Based Question Answering
+    
+    ---
+    
+    ## 🌟 Research Achievement
+    
+    **64.5% improvement** in semantic similarity over baseline methods!
+    
+    This system demonstrates advanced Retrieval-Augmented Generation combining:
+    - 🔍 Dense neural retrieval (Sentence Transformers)
+    - 🤖 Answer generation (FLAN-T5)
+    - 📊 Production-ready pipeline
+    
+    ---
+    """)
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            query_input = gr.Textbox(
+                label="❓ Enter Your Question",
+                placeholder="e.g., What is machine learning?",
+                lines=3
+            )
+            
+            submit_btn = gr.Button("🚀 Get Answer", variant="primary", size="lg")
+            
+            gr.Markdown("### 📝 Example Questions:")
+            gr.Examples(
+                examples=[
+                    "What is machine learning?",
+                    "Explain deep learning",
+                    "What are transformers in NLP?",
+                    "How does semantic search work?",
+                    "What is retrieval augmented generation?",
+                ],
+                inputs=query_input
+            )
+        
+        with gr.Column(scale=2):
+            answer_output = gr.Markdown(label="Answer & Research Info")
+            retrieved_output = gr.Markdown(label="Retrieved Context")
+    
+    submit_btn.click(
+        fn=rag.retrieve_and_answer,
+        inputs=[query_input],
+        outputs=[answer_output, retrieved_output]
+    )
+    
+    gr.Markdown("""
+    ---
+    
+    ## 📊 Complete Research Results
+    
+    | Method | Semantic Similarity | Improvement |
+    |--------|-------------------|-------------|
+    | **Dense (Neural Embeddings)** | **0.1966** | **+64.5%** ⭐ |
+    | Hybrid (Weighted Fusion) | 0.1816 | +51.9% |
+    | Hybrid (RRF) | 0.1542 | +28.9% |
+    | Sparse (BM25) | 0.1196 | Baseline |
+    
+    **Evaluation Details:**
+    - Dataset: MS MARCO
+    - Corpus: 5,000 documents
+    - Queries: 198 real-world questions
+    - Metrics: ROUGE, BLEU, Semantic Similarity
+    
+    ---
+    
+    ## 👨‍💻 About This Project
+    
+    **Title:** AI-Powered Retrieval-Augmented Assistant for Evidence-Based Question Answering
+    
+    **Author:** [Your Name]  
+    **Institution:** [Your University]  
+    **Location:** Berlin, Germany 🇩🇪  
+    
+    **Technologies:**
+    - Python, PyTorch, Transformers
+    - Sentence-Transformers, BM25
+    - Gradio, HuggingFace
+    
+    **GitHub:** [Your GitHub Link]  
+    **LinkedIn:** [Your LinkedIn]  
+    **Email:** [Your Email]
+    
+    ---
+    
+    ### 💼 Currently Seeking Full-Time Opportunities
+    
+    I'm actively looking for **AI/ML Engineer** positions in Berlin, Germany!
+    
+    If you're interested in my work, please reach out at [[email protected]]
+    
+    ---
+    
+    Built with ❤️ using Gradio and HuggingFace Transformers
+    """)
+
+# Launch
+demo.launch()