Create app.py

app.py

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+import gradio as gr
+from sentence_transformers import SentenceTransformer
+import faiss
+import numpy as np
+from transformers import pipeline
+
+docs = [
+    "RAG stands for Retrieval Augmented Generation.",
+    "This chatbot runs on Hugging Face Spaces.",
+    "FAISS is a vector database."
+]
+
+embedder = SentenceTransformer("all-MiniLM-L6-v2")
+doc_embeddings = embedder.encode(docs)
+
+index = faiss.IndexFlatL2(doc_embeddings.shape[1])
+index.add(np.array(doc_embeddings))
+
+llm = pipeline("text2text-generation", model="google/flan-t5-base")
+
+def chat(q):
+    q_emb = embedder.encode([q])
+    _, I = index.search(np.array(q_emb), k=2)
+    context = " ".join([docs[i] for i in I[0]])
+    prompt = f"Context: {context}\nQuestion: {q}\nAnswer:"
+    return llm(prompt, max_length=120)[0]["generated_text"]
+
+gr.Interface(chat, "text", "text", title="Mobile RAG Bot").launch()