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app.py

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+"""UTN Student Chatbot — Gradio app with CRAG pipeline."""
+
+import logging
+import re
+
+import gradio as gr
+import spaces
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+from prompt import REWRITE_PROMPT, build_chat_messages
+from retriever import Retriever
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+MODEL_ID = "saeedbenadeeb/UTN-Qwen3-0.6B-LoRA-merged"
+
+logger.info("Initializing retriever...")
+retriever = Retriever(
+    faiss_index_path="faiss.index",
+    chunks_meta_path="chunks_meta.jsonl",
+    embedding_model="BAAI/bge-small-en-v1.5",
+    top_k=5,
+)
+
+logger.info("Loading model: %s", MODEL_ID)
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    trust_remote_code=True,
+)
+model.eval()
+logger.info("Model loaded.")
+
+
+def _generate(messages: list[dict], max_tokens: int = 512) -> str:
+    prompt = tokenizer.apply_chat_template(
+        messages, tokenize=False, add_generation_prompt=True, enable_thinking=False,
+    )
+    inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=2048)
+    inputs = {k: v.to(model.device) for k, v in inputs.items()}
+    with torch.no_grad():
+        out = model.generate(
+            **inputs,
+            max_new_tokens=max_tokens,
+            temperature=0.3,
+            top_p=0.9,
+            do_sample=True,
+            pad_token_id=tokenizer.pad_token_id,
+        )
+    return tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True).strip()
+
+
+def _grade_relevance(question: str, sources: list[dict]) -> bool:
+    if not sources:
+        return False
+    top_score = sources[0].get("score", 0)
+    q_tokens = set(re.findall(r"\w+", question.lower()))
+    doc_tokens = set(re.findall(r"\w+", sources[0].get("text", "").lower()))
+    stopwords = {
+        "i", "a", "the", "is", "it", "to", "do", "if", "my", "can", "in", "of",
+        "for", "and", "or", "at", "on", "no", "not", "what", "how", "when", "where",
+        "who", "which", "this", "that", "be", "are", "was", "have", "has",
+    }
+    q_content = q_tokens - stopwords
+    overlap = len(q_content & doc_tokens) / max(len(q_content), 1)
+    return top_score >= 0.02 or overlap >= 0.35
+
+
+@spaces.GPU
+def crag_answer(message: str, history: list[dict]) -> str:
+    question = message.strip()
+    if not question:
+        return "Please ask a question about UTN."
+
+    sources = retriever.retrieve(question)
+    relevant = _grade_relevance(question, sources)
+
+    if not relevant:
+        rewrite_msgs = [
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": REWRITE_PROMPT.format(question=question)},
+        ]
+        rewritten = _generate(rewrite_msgs, max_tokens=100)
+        rewritten = rewritten.split("\n")[0].strip()
+        if rewritten and rewritten != question:
+            sources = retriever.retrieve(rewritten)
+
+    context = retriever.format_context(sources)
+    messages = build_chat_messages(question, context)
+    answer = _generate(messages)
+
+    sources_md = retriever.format_sources_markdown(sources)
+    return answer + sources_md
+
+
+demo = gr.ChatInterface(
+    fn=crag_answer,
+    type="messages",
+    title="UTN Student Chatbot",
+    description="Ask questions about the University of Technology Nuremberg (UTN) — admissions, programs, courses, deadlines, and more. Powered by a finetuned Qwen3-0.6B with Corrective RAG.",
+    examples=[
+        "What are the admission requirements for AI & Robotics?",
+        "Are there tuition fees?",
+        "What courses are in the first semester?",
+        "Is there a Welcome Week?",
+        "What TOEFL score do I need?",
+    ],
+    theme=gr.themes.Soft(),
+)
+
+if __name__ == "__main__":
+    demo.launch()