Create app.py

app.py

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+import gradio as gr
+from sentence_transformers import SentenceTransformer
+from transformers import pipeline
+import faiss
+import numpy as np
+
+# Sample documents (customize these)
+documents = [
+    "Hugging Face provides a platform called Spaces for deploying ML apps.",
+    "RAG combines document retrieval with generative models.",
+    "FAISS enables efficient similarity search in dense vectors.",
+    "Gradio allows quick creation of ML UIs."
+]
+
+# Initialize models and FAISS index
+embedder = SentenceTransformer('all-MiniLM-L6-v2')
+generator = pipeline('text2text-generation', model='google/flan-t5-base')
+
+# Process documents and create FAISS index
+chunk_embeddings = embedder.encode(documents)
+dimension = chunk_embeddings.shape[1]
+index = faiss.IndexFlatL2(dimension)
+index.add(chunk_embeddings)
+
+def answer_question(question):
+    # Retrieve relevant chunks
+    question_embed = embedder.encode([question])
+    distances, indices = index.search(question_embed, k=2)
+    context = "\n".join([documents[i] for i in indices[0]])
+    
+    # Generate answer
+    prompt = f"Answer based on context: {context}\nQuestion: {question}\nAnswer:"
+    return generator(prompt, max_length=200)[0]['generated_text']
+
+# Gradio interface
+interface = gr.Interface(
+    answer_question,
+    inputs=gr.Textbox(label="Ask a question"),
+    outputs=gr.Textbox(label="Answer"),
+    title="RAG Demo"
+)
+
+interface.launch()