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# app.py
# Run: python app.py
# Then open http://localhost:7860 in browser
import re, random, torch, torch.nn as nn, torch.nn.functional as F
from flask import Flask, request, jsonify
# ---------------- Dataset ----------------
DATA_QA = [
("What is the capital of India?", "New Delhi."),
("What is the capital of USA?", "Washington, D.C."),
("What is the capital of France?", "Paris."),
("What is the capital of Japan?", "Tokyo."),
("What is the capital of China?", "Beijing."),
("What is the capital of Russia?", "Moscow."),
("What is the capital of Brazil?", "Brasilia."),
("What is the capital of Canada?", "Ottawa."),
]
# ---------------- Tokenizer ----------------
PUNCT = ["?", ".", ",", ":", ";", "!", "/"]
SPECIALS = ["<PAD>", "<BOS>", "<EOS>", "<UNK>"]
def basic_tokenize(text):
text = text.lower().strip()
for p in PUNCT: text = text.replace(p, f" {p} ")
return text.split()
def build_vocab(pairs):
freq = {}
for q,a in pairs:
for t in basic_tokenize("q: "+q) + basic_tokenize("a: "+a):
freq[t] = freq.get(t,0)+1
itos = list(SPECIALS)
for t in sorted(freq.keys()):
if t not in SPECIALS: itos.append(t)
stoi = {t:i for i,t in enumerate(itos)}
return stoi, itos
stoi, itos = build_vocab(DATA_QA)
PAD,BOS,EOS,UNK = [stoi[s] for s in SPECIALS]
def encode(text): return [stoi.get(t,UNK) for t in basic_tokenize(text)]
def wrap_bos_eos(tokens): return [BOS]+tokens+[EOS]
# ---------------- Model ----------------
class TinyTransformer(nn.Module):
def __init__(self,vocab_size,d_model=64,n_heads=2,n_layers=2,d_ff=128,max_len=64):
super().__init__()
self.tok_emb=nn.Embedding(vocab_size,d_model)
self.pos_emb=nn.Embedding(max_len,d_model)
enc_layer=nn.TransformerEncoderLayer(d_model,n_heads,d_ff,dropout=0.1,batch_first=True)
self.transformer=nn.TransformerEncoder(enc_layer,n_layers)
self.ln=nn.LayerNorm(d_model)
self.head=nn.Linear(d_model,vocab_size)
self.max_len=max_len
def causal_mask(self,T,device): return torch.triu(torch.ones(T,T,device=device),1)==1
def forward(self,idx):
B,T=idx.size()
pos=torch.arange(0,T,device=idx.device).unsqueeze(0).expand(B,T)
x=self.tok_emb(idx)+self.pos_emb(pos)
x=self.transformer(x,mask=self.causal_mask(T,idx.device))
x=self.ln(x)
return self.head(x)
# ---------------- Training ----------------
def make_sequences():
seqs=[]
for q,a in DATA_QA:
seq=wrap_bos_eos(encode("q: "+q)+encode("a: "+a))
seqs.append(seq)
return seqs
def train_model():
device=torch.device("cpu")
model=TinyTransformer(len(itos)).to(device)
opt=torch.optim.AdamW(model.parameters(),lr=3e-3)
seqs=make_sequences()
for ep in range(50):
random.shuffle(seqs)
for s in seqs:
x=torch.tensor(s[:-1]).unsqueeze(0)
y=torch.tensor(s[1:]).unsqueeze(0)
logits=model(x)
loss=F.cross_entropy(logits.view(-1,len(itos)),y.view(-1),ignore_index=PAD)
opt.zero_grad(); loss.backward(); opt.step()
return model
model=train_model()
model.eval()
# ---------------- Inference ----------------
def generate_answer(question,max_new_tokens=20):
q_ids=encode("q: "+question)
a_prefix=encode("a:")
tokens=wrap_bos_eos(q_ids+a_prefix)[:-1]
x=torch.tensor(tokens).unsqueeze(0)
for _ in range(max_new_tokens):
if x.size(1)>=model.max_len: break
logits=model(x)
next_id=logits[:,-1,:].argmax(-1).item()
if next_id==EOS: break
x=torch.cat([x,torch.tensor([[next_id]])],1)
gen_ids=x.squeeze(0).tolist()
prefix_len=1+len(q_ids)+len(a_prefix)
answer_ids=gen_ids[prefix_len:]
return " ".join(itos[i] for i in answer_ids if i not in (PAD,BOS,EOS))
# ---------------- Flask App ----------------
app=Flask(__name__)
BAN_REGEX=re.compile(r"(?i)\bsex\b")
@app.route("/")
def index():
return """
<!doctype html><html><head><meta charset='utf-8'><title>Chatbot</title>
<style>
body{font-family:sans-serif;background:#111;color:#eee}
#chat{height:300px;overflow-y:auto;border:1px solid #444;padding:10px;margin-bottom:10px}
.bubble{margin:5px;padding:8px;border-radius:6px}
.user{background:#2563eb;color:#fff}
.bot{background:#374151;color:#eee}
</style></head><body>
<h2>SLM Chatbot</h2>
<div><label>Username: <input id='username'></label><button onclick='setUser()'>Set</button></div>
<div id='chat'></div>
<input id='msg' placeholder='Type message'><button onclick='sendMsg()'>Submit</button>
<button onclick='clearChat()'>Clear</button>
<script>
let banned=false,username='';
function addBubble(sender,text){
let div=document.createElement('div');
div.className='bubble '+sender;
div.textContent=(sender==='user'?username||'You':'Bot')+': '+text;
document.getElementById('chat').appendChild(div);
}
function setUser(){username=document.getElementById('username').value;addBubble('bot','Hello '+username);}
async function sendMsg(){
if(banned) return;
let text=document.getElementById('msg').value.trim();
if(!text) return;
if(/\\bsex\\b/i.test(text)){banned=true;addBubble('bot','banned');return;}
addBubble('user',text);
let r=await fetch('/answer',{method:'POST',headers:{'Content-Type':'application/json'},body:JSON.stringify({q:text})});
let j=await r.json();
addBubble('bot',j.answer);
}
function clearChat(){document.getElementById('chat').innerHTML='';banned=false;}
</script></body></html>
"""
@app.route("/answer",methods=["POST"])
def answer():
q=request.json.get("q","")
if BAN_REGEX.search(q): return jsonify({"answer":"banned"})
ans=generate_answer(q)
return jsonify({"answer":ans})
if __name__=="__main__":
app.run(host="0.0.0.0",port=7860)
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