Initial deployment

Dockerfile

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+FROM python:3.11-slim
+
+WORKDIR /app
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY main.py .
+COPY static/ ./static/
+
+RUN mkdir -p checkpoints
+
+EXPOSE 7860
+
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

main.py

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+# deploy/main.py
+# Production server — serves API + static Next.js files
+
+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.staticfiles import StaticFiles
+from fastapi.responses import FileResponse
+from pydantic import BaseModel, Field
+from contextlib import asynccontextmanager
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+from huggingface_hub import hf_hub_download
+import os
+import math
+import time
+
+# ─────────────────────────────────────────
+# CONFIG
+# ─────────────────────────────────────────
+HF_REPO_ID = "debojitbasak/minigpt-models"
+CACHE_DIR  = "./checkpoints"
+os.makedirs(CACHE_DIR, exist_ok=True)
+
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+print(f"Device: {device}")
+
+# ─────────────────────────────────────────
+# MODEL ARCHITECTURE
+# ─────────────────────────────────────────
+class Head(nn.Module):
+    def __init__(self, n_embd, head_size, block_size, dropout):
+        super().__init__()
+        self.query   = nn.Linear(n_embd, head_size, bias=False)
+        self.key     = nn.Linear(n_embd, head_size, bias=False)
+        self.value   = nn.Linear(n_embd, head_size, bias=False)
+        self.register_buffer('tril', torch.tril(torch.ones(block_size, block_size)))
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x):
+        B, T, C = x.shape
+        q   = self.query(x)
+        k   = self.key(x)
+        wei = q @ k.transpose(-2, -1) * C**-0.5
+        wei = wei.masked_fill(self.tril[:T, :T] == 0, float('-inf'))
+        wei = F.softmax(wei, dim=-1)
+        wei = self.dropout(wei)
+        return wei @ self.value(x)
+
+class MultiHeadAttention(nn.Module):
+    def __init__(self, n_embd, num_heads, head_size, block_size, dropout):
+        super().__init__()
+        self.heads   = nn.ModuleList([Head(n_embd, head_size, block_size, dropout) for _ in range(num_heads)])
+        self.proj    = nn.Linear(n_embd, n_embd)
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x):
+        out = torch.cat([h(x) for h in self.heads], dim=-1)
+        return self.dropout(self.proj(out))
+
+class FeedForward(nn.Module):
+    def __init__(self, n_embd, dropout):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Linear(n_embd, 4 * n_embd),
+            nn.GELU(),
+            nn.Linear(4 * n_embd, n_embd),
+            nn.Dropout(dropout),
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
+class Block(nn.Module):
+    def __init__(self, n_embd, n_head, block_size, dropout):
+        super().__init__()
+        head_size = n_embd // n_head
+        self.sa   = MultiHeadAttention(n_embd, n_head, head_size, block_size, dropout)
+        self.ffwd = FeedForward(n_embd, dropout)
+        self.ln1  = nn.LayerNorm(n_embd)
+        self.ln2  = nn.LayerNorm(n_embd)
+
+    def forward(self, x):
+        x = x + self.sa(self.ln1(x))
+        x = x + self.ffwd(self.ln2(x))
+        return x
+
+class GPTLanguageModel(nn.Module):
+    def __init__(self, vocab_size, n_embd, n_head, n_layer, block_size, dropout):
+        super().__init__()
+        self.block_size = block_size
+        self.token_embedding_table    = nn.Embedding(vocab_size, n_embd)
+        self.position_embedding_table = nn.Embedding(block_size, n_embd)
+        self.blocks  = nn.Sequential(*[Block(n_embd, n_head, block_size, dropout) for _ in range(n_layer)])
+        self.ln_f    = nn.LayerNorm(n_embd)
+        self.lm_head = nn.Linear(n_embd, vocab_size)
+
+    def forward(self, idx, targets=None):
+        B, T    = idx.shape
+        tok_emb = self.token_embedding_table(idx)
+        pos_emb = self.position_embedding_table(torch.arange(T, device=idx.device))
+        x       = tok_emb + pos_emb
+        x       = self.blocks(x)
+        x       = self.ln_f(x)
+        logits  = self.lm_head(x)
+        loss    = None
+        if targets is not None:
+            B, T, C = logits.shape
+            loss    = F.cross_entropy(logits.view(B*T, C), targets.view(B*T))
+        return logits, loss
+
+    @torch.no_grad()
+    def generate(self, idx, max_new_tokens, temperature=1.0, top_k=None):
+        for _ in range(max_new_tokens):
+            idx_cond  = idx[:, -self.block_size:]
+            logits, _ = self(idx_cond)
+            logits    = logits[:, -1, :] / temperature
+            if top_k is not None:
+                v      = torch.topk(logits, min(top_k, logits.size(-1)))
+                logits[logits < v[0][:, [-1]]] = float('-inf')
+            probs    = F.softmax(logits, dim=-1)
+            idx_next = torch.multinomial(probs, num_samples=1)
+            idx      = torch.cat((idx, idx_next), dim=1)
+        return idx
+
+# ─────────────────────────────────────────
+# MODEL LOADER
+# ─────────────────────────────────────────
+HF_FILENAMES = {
+    'english': 'english_maxed_model.pth',
+    'bangla':  'bangla_improved_model.pth',
+}
+
+_models = {}
+
+def get_checkpoint_path(language: str) -> str:
+    filename   = HF_FILENAMES[language]
+    local_path = os.path.join(CACHE_DIR, filename)
+    if os.path.exists(local_path):
+        print(f"  Found locally: {local_path}")
+        return local_path
+    print(f"  Downloading {filename} from Hugging Face...")
+    path = hf_hub_download(
+        repo_id   = HF_REPO_ID,
+        filename  = filename,
+        repo_type = "model",
+        local_dir = CACHE_DIR,
+    )
+    print(f"  Downloaded: {path}")
+    return path
+
+def load_model(language: str):
+    if language in _models:
+        return _models[language]
+    print(f"Loading {language} model...")
+    path       = get_checkpoint_path(language)
+    checkpoint = torch.load(path, map_location=device, weights_only=False)
+    hp         = checkpoint['hyperparameters']
+    model      = GPTLanguageModel(
+        vocab_size = checkpoint['vocab_size'],
+        n_embd     = hp['n_embd'],
+        n_head     = hp['n_head'],
+        n_layer    = hp['n_layer'],
+        block_size = hp['block_size'],
+        dropout    = hp['dropout'],
+    ).to(device)
+    model.load_state_dict(checkpoint['model_state_dict'])
+    model.eval()
+    params = sum(p.numel() for p in model.parameters()) / 1e6
+    print(f"  Loaded! {params:.2f}M params | val_loss={checkpoint['val_loss']:.4f}")
+    _models[language] = (model, checkpoint['stoi'], checkpoint['itos'])
+    return _models[language]
+
+def generate_text(language, prompt="", max_tokens=200, temperature=1.0, top_k=40):
+    model, stoi, itos = load_model(language)
+    encode = lambda s: [stoi[c] for c in s if c in stoi]
+    decode = lambda l: ''.join([itos[i] for i in l])
+    if prompt and prompt.strip():
+        encoded = encode(prompt)
+        if not encoded:
+            encoded = [0]
+        context = torch.tensor(encoded, dtype=torch.long, device=device).unsqueeze(0)
+    else:
+        context = torch.zeros((1, 1), dtype=torch.long, device=device)
+    output    = model.generate(context, max_new_tokens=max_tokens, temperature=temperature, top_k=top_k)
+    full_text = decode(output[0].tolist())
+    if prompt and prompt.strip():
+        return full_text[len(prompt):]
+    return full_text
+
+# ─────────────────────────────────────────
+# LIFESPAN
+# ─────────────────────────────────────────
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    print("Preloading models...")
+    for lang in ['english', 'bangla']:
+        try:
+            load_model(lang)
+            print(f"  {lang} model ready ✓")
+        except Exception as e:
+            print(f"  {lang} model failed: {e}")
+    yield
+
+# ─────────────────────────────────────────
+# APP
+# ─────────────────────────────────────────
+app = FastAPI(
+    title    = "MiniGPT API",
+    version  = "1.0.0",
+    lifespan = lifespan,
+)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins  = ["*"],
+    allow_methods  = ["*"],
+    allow_headers  = ["*"],
+)
+
+# ─────────────────────────────────────────
+# REQUEST / RESPONSE
+# ─────────────────────────────────────────
+class GenerateRequest(BaseModel):
+    language:    str   = Field(default="english")
+    prompt:      str   = Field(default="")
+    max_tokens:  int   = Field(default=200, ge=10, le=500)
+    temperature: float = Field(default=1.0, ge=0.1, le=2.0)
+    top_k:       int   = Field(default=40, ge=1, le=100)
+
+class GenerateResponse(BaseModel):
+    generated_text:   str
+    prompt:           str
+    language:         str
+    time_taken:       float
+    tokens_generated: int
+
+# ─────────────────────────────────────────
+# API ROUTES
+# ─────────────────────────────────────────
+@app.get("/health")
+def health():
+    return {"status": "ok"}
+
+@app.get("/models")
+def models():
+    return {
+        lang: {
+            "loaded": lang in _models,
+            "file":   HF_FILENAMES.get(lang)
+        }
+        for lang in ['english', 'bangla']
+    }
+
+@app.post("/generate", response_model=GenerateResponse)
+def generate(req: GenerateRequest):
+    if req.language not in ["english", "bangla"]:
+        raise HTTPException(status_code=400, detail="language must be 'english' or 'bangla'")
+    try:
+        start = time.time()
+        text  = generate_text(
+            language    = req.language,
+            prompt      = req.prompt,
+            max_tokens  = req.max_tokens,
+            temperature = req.temperature,
+            top_k       = req.top_k,
+        )
+        return GenerateResponse(
+            generated_text   = text,
+            prompt           = req.prompt,
+            language         = req.language,
+            time_taken       = round(time.time() - start, 2),
+            tokens_generated = len(text),
+        )
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+# ─────────────────────────────────────────
+# SERVE STATIC FRONTEND
+# must be LAST — catches all remaining routes
+# ─────────────────────────────────────────
+app.mount("/", StaticFiles(directory="static", html=True), name="static")

requirements.txt

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+fastapi==0.115.0
+uvicorn==0.30.0
+torch==2.1.0
+huggingface_hub==0.24.0
+python-multipart==0.0.9
+aiofiles==23.2.1

static/404.html

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static/404/index.html

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+<!DOCTYPE html><!--nn4qaT7p51YtxU5tkN_2F--><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="/_next/static/chunks/f586a29ffaa3f3e4.css" data-precedence="next"/><link rel="preload" as="script" fetchPriority="low" href="/_next/static/chunks/4b9eae0c8dc7e975.js"/><script src="/_next/static/chunks/f2f58a7e93290fbb.js" async=""></script><script src="/_next/static/chunks/5636b81df088f077.js" async=""></script><script src="/_next/static/chunks/turbopack-6ad3b2dca33be858.js" async=""></script><script src="/_next/static/chunks/2f236954d6a65e12.js" async=""></script><meta name="robots" content="noindex"/><meta name="next-size-adjust" content=""/><title>404: This page could not be found.</title><title>MiniGPT</title><meta name="description" content="GPT trained from scratch — English &amp; Bangla"/><link rel="icon" href="/favicon.ico?favicon.0b3bf435.ico" sizes="256x256" type="image/x-icon"/><script 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static/_next/static/nn4qaT7p51YtxU5tkN-2F/_buildManifest.js

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static/_next/static/nn4qaT7p51YtxU5tkN-2F/_clientMiddlewareManifest.json

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static/_next/static/nn4qaT7p51YtxU5tkN-2F/_ssgManifest.js

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static/_not-found/__next._index.txt

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static/index.html

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+<!DOCTYPE html><!--nn4qaT7p51YtxU5tkN_2F--><html lang="en"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="preload" href="/_next/static/media/797e433ab948586e-s.p.dbea232f.woff2" as="font" crossorigin="" type="font/woff2"/><link rel="preload" href="/_next/static/media/caa3a2e1cccd8315-s.p.853070df.woff2" as="font" crossorigin="" type="font/woff2"/><link rel="stylesheet" href="/_next/static/chunks/f586a29ffaa3f3e4.css" data-precedence="next"/><link rel="preload" as="script" fetchPriority="low" href="/_next/static/chunks/4b9eae0c8dc7e975.js"/><script src="/_next/static/chunks/f2f58a7e93290fbb.js" async=""></script><script src="/_next/static/chunks/5636b81df088f077.js" async=""></script><script src="/_next/static/chunks/turbopack-6ad3b2dca33be858.js" async=""></script><script src="/_next/static/chunks/2f236954d6a65e12.js" async=""></script><script src="/_next/static/chunks/74990dc58d03c07f.js" async=""></script><meta name="next-size-adjust" content=""/><title>MiniGPT</title><meta name="description" content="GPT trained from scratch — English &amp; Bangla"/><link rel="icon" href="/favicon.ico?favicon.0b3bf435.ico" sizes="256x256" type="image/x-icon"/><script src="/_next/static/chunks/a6dad97d9634a72d.js" noModule=""></script></head><body class="geist_2ae47f08-module__h69qWW__variable geist_mono_eb58308d-module__w_p2Lq__variable antialiased"><div hidden=""><!--$--><!--/$--></div><main class="min-h-screen bg-gradient-to-br from-gray-50 to-blue-50 p-4 md:p-8"><div class="max-w-4xl mx-auto"><div class="text-center mb-8"><h1 class="text-4xl font-bold text-gray-900 mb-2">🤖 MiniGPT</h1><p class="text-gray-500">GPT trained from scratch · English &amp; Bangla</p></div><div class="grid grid-cols-1 lg:grid-cols-3 gap-6"><div class="lg:col-span-1 space-y-4"><div class="bg-white rounded-2xl p-5 shadow-sm border border-gray-100"><h2 class="text-sm font-semibold text-gray-500 uppercase tracking-wide mb-3">Model</h2><div class="grid grid-cols-2 gap-2"><button class="p-3 rounded-xl border-2 transition-all
+                      text-left
+                      border-blue-500 bg-blue-50"><div class="font-medium text-sm">📖 English</div><div class="text-xs text-gray-400">Shakespeare</div></button><button class="p-3 rounded-xl border-2 transition-all
+                      text-left
+                      border-gray-200 hover:border-gray-300"><div class="font-medium text-sm">বা Bangla</div><div class="text-xs text-gray-400">Wikipedia</div></button></div></div><div class="bg-white rounded-2xl p-5 shadow-sm border border-gray-100 space-y-5"><h2 class="text-sm font-semibold text-gray-500 uppercase tracking-wide">Settings</h2><div><div class="flex justify-between items-center mb-1"><label class="text-sm font-medium text-gray-700">Temperature</label><span class="text-sm font-mono bg-gray-100 px-2 py-0.5 rounded text-gray-800">1</span></div><input type="range" min="0.1" max="2" step="0.1" class="w-full h-2 bg-gray-200 rounded-lg appearance-none cursor-pointer accent-blue-500" value="1"/><div class="flex justify-between text-xs text-gray-400 mt-1"><span>0.1</span><span class="text-gray-500">Balanced</span><span>2</span></div></div><div><div class="flex justify-between items-center mb-1"><label class="text-sm font-medium text-gray-700">Max Tokens</label><span class="text-sm font-mono bg-gray-100 px-2 py-0.5 rounded text-gray-800">200</span></div><input type="range" min="50" max="500" step="10" class="w-full h-2 bg-gray-200 rounded-lg appearance-none cursor-pointer accent-blue-500" value="200"/><div class="flex justify-between text-xs text-gray-400 mt-1"><span>50</span><span class="text-gray-500">length</span><span>500</span></div></div><div><div class="flex justify-between items-center mb-1"><label class="text-sm font-medium text-gray-700">Top-K</label><span class="text-sm font-mono bg-gray-100 px-2 py-0.5 rounded text-gray-800">40</span></div><input type="range" min="1" max="100" step="1" class="w-full h-2 bg-gray-200 rounded-lg appearance-none cursor-pointer accent-blue-500" value="40"/><div class="flex justify-between text-xs text-gray-400 mt-1"><span>1</span><span class="text-gray-500">diversity</span><span>100</span></div></div></div><div class="bg-white rounded-2xl p-5 shadow-sm border border-gray-100"><h2 class="text-sm font-semibold text-gray-500 uppercase tracking-wide mb-3">Quick Prompts</h2><div class="space-y-2"><button class="w-full text-left text-sm px-3 py-2 rounded-lg bg-gray-50 hover:bg-blue-50 hover:text-blue-700 transition-all text-gray-700 border border-gray-100 hover:border-blue-200">ROMEO:</button><button class="w-full text-left text-sm px-3 py-2 rounded-lg bg-gray-50 hover:bg-blue-50 hover:text-blue-700 transition-all text-gray-700 border border-gray-100 hover:border-blue-200">To be or not to be</button><button class="w-full text-left text-sm px-3 py-2 rounded-lg bg-gray-50 hover:bg-blue-50 hover:text-blue-700 transition-all text-gray-700 border border-gray-100 hover:border-blue-200">HAMLET:</button><button class="w-full text-left text-sm px-3 py-2 rounded-lg bg-gray-50 hover:bg-blue-50 hover:text-blue-700 transition-all text-gray-700 border border-gray-100 hover:border-blue-200">Once upon a time</button></div></div></div><div class="lg:col-span-2 space-y-4"><div class="bg-white rounded-2xl p-5 shadow-sm border border-gray-100"><h2 class="text-sm font-semibold text-gray-500 uppercase tracking-wide mb-3">Prompt<span class="ml-2 text-xs font-normal text-gray-400 normal-case">(optional — leave empty to generate freely)</span></h2><textarea placeholder="ROMEO: or To be or not to be..." rows="3" class="w-full resize-none rounded-xl border border-gray-200 p-3 text-sm focus:outline-none focus:ring-2 focus:ring-blue-300 text-gray-800 placeholder-gray-300"></textarea><p class="text-xs text-gray-400 mt-1">Ctrl+Enter to generate</p></div><button class="w-full py-4 rounded-2xl font-semibold
+                         text-white text-lg transition-all shadow-sm
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