chore: project cleanup and standard readme

.gitattributes

@@ -1,35 +0,0 @@
-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ckpt filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
-*.npz filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pickle filter=lfs diff=lfs merge=lfs -text
-*.pkl filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-*.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tgz filter=lfs diff=lfs merge=lfs -text
-*.wasm filter=lfs diff=lfs merge=lfs -text
-*.xz filter=lfs diff=lfs merge=lfs -text
-*.zip filter=lfs diff=lfs merge=lfs -text
-*.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,11 +1,39 @@
----
-title: Code Complexity Predictor
-emoji: 📉
-colorFrom: blue
-colorTo: yellow
-sdk: docker
-pinned: false
-license: apache-2.0
----
+# ⚙️ Code Complexity Predictor
+
+An AI-powered web application that instantly predicts the Big-O Time Complexity of Python and Java code snippets using **GraphCodeBERT**. 
+
+## 🚀 Features
+- **Intelligent Analysis:** Powered by Microsoft's GraphCodeBERT fine-tuned on the CodeParrot/CodeComplex dataset.
+- **Premium Interface:** A stunning Glassmorphism dark-mode UI with syntax highlighting and micro-animations.
+- **Lightning Fast:** Built on a lightweight FastAPI backend for near-instant inference.
+- **Cloud-Ready:** Completely containerized with Docker, configured for automatic deploy on Render.com.
+
+## 🛠️ Tech Stack
+- **Frontend:** HTML5, Vector CSS (Vanilla), JavaScript, PrismJS
+- **Backend:** Python, FastAPI, Uvicorn
+- **AI/ML:** PyTorch, HuggingFace Transformers (`GraphCodeBERT`)
+- **Deployment:** Docker, Render
+
+## 💻 Running Locally
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+1. **Install Dependencies**
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+2. **Download Model files**
+   Ensure you have configured `download_model.py` with your Google Drive File ID, then run:
+   ```bash
+   python download_model.py
+   ```
+
+3. **Start the Server**
+   ```bash
+   uvicorn backend.main:app --host 0.0.0.0 --port 8000 --reload
+   ```
+
+4. **Open the App**
+   Navigate to `http://localhost:8000` in your web browser.
+
+---
+*Built with ❤️ for algorithmic analysis.*

train_extracted.py

@@ -1,315 +0,0 @@
-!pip install transformers datasets torch scikit-learn
-
-# --- CELL ---
-
-from datasets import load_dataset
-
-dataset = load_dataset("codeparrot/codecomplex")
-print(dataset)
-print(dataset['train'][0])
-
-# --- CELL ---
-
-import pandas as pd
-
-df = pd.DataFrame(dataset['train'])
-
-# Check complexity labels
-print("Complexity classes:")
-print(df['complexity'].value_counts())
-
-print("\nLanguages:")
-print(df['from'].value_counts())
-
-print("\nTotal samples:", len(df))
-
-# --- CELL ---
-
-from sklearn.preprocessing import LabelEncoder
-from sklearn.model_selection import train_test_split
-
-# Encode labels
-le = LabelEncoder()
-df['label'] = le.fit_transform(df['complexity'])
-
-print("Label mapping:")
-for i, cls in enumerate(le.classes_):
-    print(f"  {cls} → {i}")
-
-# Split data
-train_df, test_df = train_test_split(df, test_size=0.2, random_state=42, stratify=df['label'])
-
-print(f"\nTrain size: {len(train_df)}")
-print(f"Test size: {len(test_df)}")
-
-# --- CELL ---
-
-from transformers import AutoTokenizer
-
-tokenizer = AutoTokenizer.from_pretrained("microsoft/codebert-base")
-
-print("✅ CodeBERT tokenizer loaded!")
-
-# Test it
-sample = df['src'][0][:200]
-tokens = tokenizer(sample, truncation=True, max_length=512, return_tensors="pt")
-print("Sample token shape:", tokens['input_ids'].shape)
-
-# --- CELL ---
-
-import torch
-from torch.utils.data import Dataset
-
-class CodeDataset(Dataset):
-    def __init__(self, dataframe, tokenizer, max_length=512):
-        self.data = dataframe
-        self.tokenizer = tokenizer
-        self.max_length = max_length
-
-    def __len__(self):
-        return len(self.data)
-
-    def __getitem__(self, idx):
-        code = str(self.data.iloc[idx]['src'])
-        label = int(self.data.iloc[idx]['label'])
-
-        encoding = self.tokenizer(
-            code,
-            truncation=True,
-            max_length=self.max_length,
-            padding='max_length',
-            return_tensors='pt'
-        )
-
-        return {
-            'input_ids': encoding['input_ids'].squeeze(),
-            'attention_mask': encoding['attention_mask'].squeeze(),
-            'label': torch.tensor(label, dtype=torch.long)
-        }
-
-# Create datasets
-train_dataset = CodeDataset(train_df.reset_index(drop=True), tokenizer)
-test_dataset = CodeDataset(test_df.reset_index(drop=True), tokenizer)
-
-print(f"✅ Train dataset: {len(train_dataset)} samples")
-print(f"✅ Test dataset: {len(test_dataset)} samples")
-
-# --- CELL ---
-
-from transformers import AutoModelForSequenceClassification
-import torch
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-print(f"Using device: {device}")
-
-model = AutoModelForSequenceClassification.from_pretrained(
-    "microsoft/codebert-base",
-    num_labels=7
-)
-
-model = model.to(device)
-print("✅ CodeBERT model loaded!")
-print(f"Total parameters: {sum(p.numel() for p in model.parameters()):,}")
-
-# --- CELL ---
-
-from torch.utils.data import DataLoader
-from torch.optim import AdamW
-from transformers import get_linear_schedule_with_warmup
-
-# DataLoaders
-train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)
-test_loader = DataLoader(test_dataset, batch_size=16, shuffle=False)
-
-# Optimizer
-optimizer = AdamW(model.parameters(), lr=2e-5, weight_decay=0.01)
-
-# Scheduler
-total_steps = len(train_loader) * 3  # 3 epochs
-scheduler = get_linear_schedule_with_warmup(
-    optimizer,
-    num_warmup_steps=total_steps // 10,
-    num_training_steps=total_steps
-)
-
-print(f"✅ DataLoaders ready!")
-print(f"Total training steps: {total_steps}")
-print(f"Steps per epoch: {len(train_loader)}")
-
-# --- CELL ---
-
-from tqdm import tqdm
-
-def train_epoch(model, loader, optimizer, scheduler, device):
-    model.train()
-    total_loss = 0
-    correct = 0
-    total = 0
-
-    for batch in tqdm(loader, desc="Training"):
-        input_ids = batch['input_ids'].to(device)
-        attention_mask = batch['attention_mask'].to(device)
-        labels = batch['label'].to(device)
-
-        optimizer.zero_grad()
-        outputs = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)
-        loss = outputs.loss
-        logits = outputs.logits
-
-        loss.backward()
-        torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
-        optimizer.step()
-        scheduler.step()
-
-        total_loss += loss.item()
-        preds = torch.argmax(logits, dim=1)
-        correct += (preds == labels).sum().item()
-        total += labels.size(0)
-
-    return total_loss / len(loader), correct / total
-
-
-def evaluate(model, loader, device):
-    model.eval()
-    correct = 0
-    total = 0
-
-    with torch.no_grad():
-        for batch in tqdm(loader, desc="Evaluating"):
-            input_ids = batch['input_ids'].to(device)
-            attention_mask = batch['attention_mask'].to(device)
-            labels = batch['label'].to(device)
-
-            outputs = model(input_ids=input_ids, attention_mask=attention_mask)
-            preds = torch.argmax(outputs.logits, dim=1)
-            correct += (preds == labels).sum().item()
-            total += labels.size(0)
-
-    return correct / total
-
-
-# Train for 3 epochs
-best_accuracy = 0
-
-for epoch in range(3):
-    print(f"\n🔄 Epoch {epoch+1}/3")
-    train_loss, train_acc = train_epoch(model, train_loader, optimizer, scheduler, device)
-    test_acc = evaluate(model, test_loader, device)
-
-    print(f"Loss: {train_loss:.4f} | Train Acc: {train_acc*100:.2f}% | Test Acc: {test_acc*100:.2f}%")
-
-    if test_acc > best_accuracy:
-        best_accuracy = test_acc
-        torch.save(model.state_dict(), "best_model.pt")
-        print(f"✅ Best model saved! Accuracy: {best_accuracy*100:.2f}%")
-
-# --- CELL ---
-
-# Train 2 more epochs
-for epoch in range(2):
-    print(f"\n🔄 Epoch {epoch+4}/5")
-    train_loss, train_acc = train_epoch(model, train_loader, optimizer, scheduler, device)
-    test_acc = evaluate(model, test_loader, device)
-
-    print(f"Loss: {train_loss:.4f} | Train Acc: {train_acc*100:.2f}% | Test Acc: {test_acc*100:.2f}%")
-
-    if test_acc > best_accuracy:
-        best_accuracy = test_acc
-        torch.save(model.state_dict(), "best_model.pt")
-        print(f"✅ Best model saved! Accuracy: {best_accuracy*100:.2f}%")
-
-# --- CELL ---
-
-from google.colab import drive
-drive.mount('/content/drive')
-
-# --- CELL ---
-
-import shutil
-
-# Copy files to Google Drive
-shutil.copy("best_model.pt", "/content/drive/MyDrive/best_model.pt")
-shutil.copy("label_encoder.pkl", "/content/drive/MyDrive/label_encoder.pkl")
-
-print("✅ Files saved to Google Drive!")
-
-# --- CELL ---
-
-# Test the model directly in Colab
-test_codes = [
-    "public int findMax(int[] arr) { int max = arr[0]; for (int i = 1; i < arr.length; i++) { if (arr[i] > max) max = arr[i]; } return max; }",
-    "return arr[0];",
-    "for(int i=0;i<n;i++) for(int j=0;j<n;j++) sum+=arr[i][j];",
-]
-
-for code in test_codes:
-    inputs = tokenizer(code, truncation=True, max_length=512, padding='max_length', return_tensors='pt')
-    input_ids = inputs['input_ids'].to(device)
-    attention_mask = inputs['attention_mask'].to(device)
-
-    with torch.no_grad():
-        outputs = model(input_ids=input_ids, attention_mask=attention_mask)
-        pred = torch.argmax(outputs.logits, dim=1).item()
-
-    print(f"Code: {code[:50]}...")
-    print(f"Predicted: {le.inverse_transform([pred])[0]}\n")
-
-# --- CELL ---
-
-import torch.nn as nn
-
-# Count class frequencies
-class_counts = df['label'].value_counts().sort_index().values
-total = sum(class_counts)
-class_weights = torch.tensor([total/c for c in class_counts], dtype=torch.float).to(device)
-
-print("Class weights:", class_weights)
-
-# New training loop with weighted loss
-def train_epoch_weighted(model, loader, optimizer, scheduler, device, weights):
-    model.train()
-    total_loss = 0
-    correct = 0
-    total = 0
-    criterion = nn.CrossEntropyLoss(weight=weights)
-
-    for batch in tqdm(loader, desc="Training"):
-        input_ids = batch['input_ids'].to(device)
-        attention_mask = batch['attention_mask'].to(device)
-        labels = batch['label'].to(device)
-
-        optimizer.zero_grad()
-        outputs = model(input_ids=input_ids, attention_mask=attention_mask)
-        loss = criterion(outputs.logits, labels)
-
-        loss.backward()
-        torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
-        optimizer.step()
-        scheduler.step()
-
-        total_loss += loss.item()
-        preds = torch.argmax(outputs.logits, dim=1)
-        correct += (preds == labels).sum().item()
-        total += labels.size(0)
-
-    return total_loss / len(loader), correct / total
-
-# Retrain with weights
-optimizer3 = AdamW(model.parameters(), lr=5e-6)
-scheduler3 = get_linear_schedule_with_warmup(optimizer3, num_warmup_steps=30, num_training_steps=len(train_loader)*3)
-
-for epoch in range(3):
-    print(f"\n🔄 Epoch {epoch+1}/3")
-    train_loss, train_acc = train_epoch_weighted(model, train_loader, optimizer3, scheduler3, device, class_weights)
-    test_acc = evaluate(model, test_loader, device)
-    print(f"Loss: {train_loss:.4f} | Train Acc: {train_acc*100:.2f}% | Test Acc: {test_acc*100:.2f}%")
-    if test_acc > best_accuracy:
-        best_accuracy = test_acc
-        torch.save(model.state_dict(), "best_model.pt")
-        print(f"✅ Best model saved! Accuracy: {best_accuracy*100:.2f}%")
-
-# --- CELL ---
-
-import shutil
-shutil.copy("best_model.pt", "/content/drive/MyDrive/best_model.pt")
-print("✅ Saved to Google Drive!")