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app.py

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+
+import gradio as gr
+import faiss
+import pickle
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from sentence_transformers import SentenceTransformer
+
+tokenizer = AutoTokenizer.from_pretrained("tiny-gpt2-finetuned-ajem")
+model = AutoModelForCausalLM.from_pretrained("tiny-gpt2-finetuned-ajem")
+embedder = SentenceTransformer("all-MiniLM-L6-v2")
+
+index = faiss.read_index("rag_index.faiss")
+with open("rag_texts.pkl", "rb") as f:
+    texts = pickle.load(f)
+
+def get_context(query, top_k=3):
+    q_emb = embedder.encode([query])
+    D, I = index.search(q_emb, top_k)
+    return "\n".join([texts[i] for i in I[0]])
+
+def chat(query):
+    context = get_context(query)
+    prompt = context + "\nUsuario: " + query + "\nAsistente:"
+    inputs = tokenizer(prompt, return_tensors="pt")
+    outputs = model.generate(**inputs, max_new_tokens=100, pad_token_id=tokenizer.eos_token_id)
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True).replace(prompt, "").strip()
+    return response
+
+gr.ChatInterface(chat, title="Tiny Chat RAG").launch()