Upload 14 files

.flake8

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+
+[flake8]
+max-line-length = 88
+extend-ignore = E203
+exclude = .git,__pycache__,build,dist

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* 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
+generated-icon.png filter=lfs diff=lfs merge=lfs -text

.isort.cfg

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+
+[settings]
+profile = black
+multi_line_output = 3

.replit

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+modules = ["python-3.11"]
+
+[nix]
+channel = "stable-24_05"
+
+[deployment]
+deploymentTarget = "autoscale"
+run = ["sh", "-c", "streamlit run app.py --server.port 5000"]
+
+[workflows]
+runButton = "Streamlit App"
+
+[[workflows.workflow]]
+name = "Project"
+mode = "parallel"
+author = "agent"
+
+[[workflows.workflow.tasks]]
+task = "workflow.run"
+args = "Streamlit Server"
+
+[[workflows.workflow]]
+name = "Streamlit Server"
+author = "agent"
+
+[workflows.workflow.metadata]
+agentRequireRestartOnSave = false
+
+[[workflows.workflow.tasks]]
+task = "packager.installForAll"
+
+[[workflows.workflow.tasks]]
+task = "shell.exec"
+args = "streamlit run app.py --server.port 5000"
+waitForPort = 5000
+
+[[workflows.workflow]]
+name = "Streamlit App"
+author = 25868489
+mode = "sequential"
+
+[[workflows.workflow.tasks]]
+task = "shell.exec"
+args = "streamlit run app.py --server.address 0.0.0.0 --server.port 5000 --server.headless true"
+
+[[ports]]
+localPort = 5000
+externalPort = 80

app.py

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+import streamlit as st
+from utils import set_page_config, display_sidebar
+import os
+
+# Set page configuration
+set_page_config()
+
+# Title and description
+st.title("CodeGen Hub")
+st.markdown("""
+    Welcome to CodeGen Hub - A platform for training and using code generation models with Hugging Face integration.
+    
+    ### Core Features:
+    - Upload and preprocess Python code datasets for model training
+    - Configure and train models with customizable parameters
+    - Generate code predictions using trained models through an interactive interface
+    - Monitor training progress with visualizations and detailed logs
+    - Seamless integration with Hugging Face Hub for model management
+    
+    Navigate through the different sections using the sidebar menu.
+""")
+
+# Display sidebar
+display_sidebar()
+
+# Create the session state for storing information across app pages
+if 'datasets' not in st.session_state:
+    st.session_state.datasets = {}
+
+if 'trained_models' not in st.session_state:
+    st.session_state.trained_models = {}
+
+if 'training_logs' not in st.session_state:
+    st.session_state.training_logs = []
+
+if 'training_progress' not in st.session_state:
+    st.session_state.training_progress = {}
+
+
+
+# Display getting started card
+st.subheader("Getting Started")
+col1, col2 = st.columns(2)
+
+with col1:
+    st.info("""
+        1. 📊 Start by uploading or selecting a Python code dataset in the **Dataset Management** section.
+        2. 🛠️ Configure and train your model in the **Model Training** section.
+    """)
+    
+with col2:
+    st.info("""
+        3. 💡 Generate code predictions using your trained models in the **Code Generation** section.
+        4. 🔄 Access your models on Hugging Face Hub for broader use.
+    """)
+
+# Display platform statistics if available
+st.subheader("Platform Statistics")
+col1, col2, col3 = st.columns(3)
+
+with col1:
+    st.metric("Datasets Available", len(st.session_state.datasets))
+    
+with col2:
+    st.metric("Trained Models", len(st.session_state.trained_models))
+    
+with col3:
+    # Calculate active training jobs
+    active_jobs = sum(1 for progress in st.session_state.training_progress.values() 
+                     if progress.get('status') == 'running')
+    st.metric("Active Training Jobs", active_jobs)

data_utils.py

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+import streamlit as st
+import pandas as pd
+import json
+import io
+import os
+from utils import add_log
+
+# Handle missing dependencies
+try:
+    from datasets import Dataset, DatasetDict
+except ImportError:
+    # Create dummy classes for Dataset and DatasetDict
+    class Dataset:
+        @classmethod
+        def from_list(cls, items):
+            return {"data": items, "column_names": list(items[0].keys()) if items else []}
+            
+        @classmethod
+        def from_dict(cls, dict_obj):
+            return {"data": dict_obj, "column_names": list(dict_obj.keys())}
+            
+        @classmethod
+        def from_pandas(cls, df):
+            return {"data": df, "column_names": df.columns.tolist()}
+            
+        def train_test_split(self, test_size=0.2):
+            return {
+                "train": self,
+                "test": self
+            }
+    
+    class DatasetDict(dict):
+        pass
+
+def process_python_dataset(uploaded_file, dataset_name):
+    """
+    Process an uploaded Python dataset file.
+    Supports .py, .json, and .csv formats.
+    
+    Args:
+        uploaded_file: The uploaded file object
+        dataset_name: Name to identify the dataset
+        
+    Returns:
+        bool: Success status
+    """
+    try:
+        file_extension = uploaded_file.name.split('.')[-1].lower()
+        
+        if file_extension == 'py':
+            # Process Python file
+            content = uploaded_file.read().decode('utf-8')
+            # Split by function or class definitions for separate examples
+            examples = split_python_file(content)
+            dataset = create_dataset_from_examples(examples)
+            
+        elif file_extension == 'json':
+            # Process JSON file
+            content = json.loads(uploaded_file.read().decode('utf-8'))
+            if isinstance(content, list):
+                dataset = Dataset.from_list(content)
+            else:
+                dataset = Dataset.from_dict(content)
+            
+        elif file_extension == 'csv':
+            # Process CSV file
+            df = pd.read_csv(uploaded_file)
+            dataset = Dataset.from_pandas(df)
+            
+        else:
+            add_log(f"Unsupported file format: {file_extension}", "ERROR")
+            return False
+            
+        # Split into train/validation sets
+        train_test_split = dataset.train_test_split(test_size=0.2)
+        
+        # Create a DatasetDict
+        dataset_dict = DatasetDict({
+            'train': train_test_split['train'],
+            'validation': train_test_split['test']
+        })
+        
+        # Store in session state
+        st.session_state.datasets[dataset_name] = {
+            'data': dataset_dict,
+            'info': {
+                'name': dataset_name,
+                'size': len(dataset),
+                'train_size': len(train_test_split['train']),
+                'validation_size': len(train_test_split['test']),
+                'columns': dataset.column_names,
+                'created_at': pd.Timestamp.now().strftime("%Y-%m-%d %H:%M:%S")
+            }
+        }
+        
+        add_log(f"Dataset '{dataset_name}' processed successfully with {len(dataset)} examples")
+        return True
+        
+    except Exception as e:
+        add_log(f"Error processing dataset: {str(e)}", "ERROR")
+        return False
+
+def split_python_file(content):
+    """
+    Split a Python file content into separate code examples.
+    
+    Args:
+        content: String content of Python file
+        
+    Returns:
+        list: List of code examples
+    """
+    examples = []
+    
+    # Simple splitting by function or class definitions
+    lines = content.split('\n')
+    current_example = []
+    
+    for line in lines:
+        if (line.startswith('def ') or line.startswith('class ')) and current_example:
+            # Start of a new function/class, save the previous one
+            examples.append('\n'.join(current_example))
+            current_example = [line]
+        else:
+            current_example.append(line)
+    
+    # Add the last example
+    if current_example:
+        examples.append('\n'.join(current_example))
+    
+    # If no examples were extracted, use the whole file as one example
+    if not examples:
+        examples = [content]
+    
+    return [{'code': example} for example in examples]
+
+def create_dataset_from_examples(examples):
+    """
+    Create a dataset from code examples.
+    
+    Args:
+        examples: List of code examples
+        
+    Returns:
+        Dataset: Hugging Face dataset
+    """
+    return Dataset.from_list(examples)
+
+def validate_dataset_structure(dataset):
+    """
+    Validate that the dataset has the required structure for training.
+    
+    Args:
+        dataset: Hugging Face dataset
+        
+    Returns:
+        bool: True if valid, False otherwise
+    """
+    if 'code' not in dataset.column_names:
+        add_log("Dataset missing 'code' column required for training", "ERROR")
+        return False
+    return True
+
+def list_available_datasets():
+    """
+    List all available datasets in session state.
+    
+    Returns:
+        list: List of dataset names
+    """
+    if 'datasets' in st.session_state:
+        return list(st.session_state.datasets.keys())
+    return []
+
+def get_dataset_info(dataset_name):
+    """
+    Get information about a dataset.
+    
+    Args:
+        dataset_name: Name of the dataset
+        
+    Returns:
+        dict: Dataset information
+    """
+    if 'datasets' in st.session_state and dataset_name in st.session_state.datasets:
+        return st.session_state.datasets[dataset_name]['info']
+    return None
+
+def get_dataset(dataset_name):
+    """
+    Get a dataset by name.
+    
+    Args:
+        dataset_name: Name of the dataset
+        
+    Returns:
+        Dataset: The dataset object
+    """
+    if 'datasets' in st.session_state and dataset_name in st.session_state.datasets:
+        return st.session_state.datasets[dataset_name]['data']
+    return None

model_utils.py

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+import streamlit as st
+import json
+import os
+from utils import add_log
+
+# Initialize huggingface_models in session state if not present
+if 'huggingface_models' not in st.session_state:
+    st.session_state.huggingface_models = [
+        "codegen-350M-mono",
+        "codegen-2B-mono",
+        "Salesforce/codegen-350M-mono",
+        "Salesforce/codegen-2B-mono",
+        "gpt2",
+        "EleutherAI/gpt-neo-125M"
+    ]
+
+# Handle missing dependencies
+try:
+    import torch
+    from transformers import AutoTokenizer, AutoModelForCausalLM
+    TRANSFORMERS_AVAILABLE = True
+except ImportError:
+    TRANSFORMERS_AVAILABLE = False
+    
+    # Mock classes for demo purposes
+    class DummyTokenizer:
+        @classmethod
+        def from_pretrained(cls, model_name):
+            return cls()
+            
+        def __call__(self, text, **kwargs):
+            return {"input_ids": [list(range(10))] * (1 if isinstance(text, str) else len(text))}
+            
+        def decode(self, token_ids, **kwargs):
+            return "# Generated code placeholder\n\ndef example_function():\n    return 'Hello world!'"
+        
+        @property
+        def eos_token(self):
+            return "[EOS]"
+            
+        @property
+        def eos_token_id(self):
+            return 0
+            
+        @property
+        def pad_token(self):
+            return None
+            
+        @pad_token.setter
+        def pad_token(self, value):
+            pass
+            
+    class DummyModel:
+        @classmethod
+        def from_pretrained(cls, model_name):
+            return cls()
+            
+        def generate(self, input_ids, **kwargs):
+            return [[1, 2, 3, 4, 5]]
+            
+        @property
+        def config(self):
+            class Config:
+                @property
+                def eos_token_id(self):
+                    return 0
+                    
+                @property
+                def pad_token_id(self):
+                    return 0
+                
+                @pad_token_id.setter
+                def pad_token_id(self, value):
+                    pass
+                    
+            return Config()
+            
+    # Set aliases to match transformers
+    AutoTokenizer = DummyTokenizer
+    AutoModelForCausalLM = DummyModel
+
+def list_available_huggingface_models():
+    """
+    List available code generation models from Hugging Face.
+    
+    Returns:
+        list: List of model names
+    """
+    # Return the list stored in session state
+    return st.session_state.huggingface_models
+
+def get_model_and_tokenizer(model_name):
+    """
+    Load model and tokenizer from Hugging Face Hub.
+    
+    Args:
+        model_name: Name of the model to load
+        
+    Returns:
+        tuple: (model, tokenizer) or (None, None) if loading fails
+    """
+    try:
+        add_log(f"Loading model and tokenizer: {model_name}")
+        tokenizer = AutoTokenizer.from_pretrained(model_name)
+        model = AutoModelForCausalLM.from_pretrained(model_name)
+        add_log(f"Model and tokenizer loaded successfully: {model_name}")
+        return model, tokenizer
+    except Exception as e:
+        add_log(f"Error loading model {model_name}: {str(e)}", "ERROR")
+        return None, None
+
+def save_trained_model(model_id, model, tokenizer):
+    """
+    Save trained model information to session state.
+    
+    Args:
+        model_id: Identifier for the model
+        model: The trained model
+        tokenizer: The model's tokenizer
+        
+    Returns:
+        bool: Success status
+    """
+    try:
+        # Store model information in session state
+        from datetime import datetime
+        st.session_state.trained_models[model_id] = {
+            'model': model,
+            'tokenizer': tokenizer,
+            'info': {
+                'id': model_id,
+                'created_at': datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+            }
+        }
+        add_log(f"Model {model_id} saved to session state")
+        return True
+    except Exception as e:
+        add_log(f"Error saving model {model_id}: {str(e)}", "ERROR")
+        return False
+
+def list_trained_models():
+    """
+    List all trained models in session state.
+    
+    Returns:
+        list: List of model IDs
+    """
+    if 'trained_models' in st.session_state:
+        return list(st.session_state.trained_models.keys())
+    return []
+
+def generate_code(model_id, prompt, max_length=100, temperature=0.7, top_p=0.9):
+    """
+    Generate code using a trained model.
+    
+    Args:
+        model_id: ID of the model to use
+        prompt: Input prompt for code generation
+        max_length: Maximum length of generated text
+        temperature: Sampling temperature
+        top_p: Nucleus sampling probability
+        
+    Returns:
+        str: Generated code or error message
+    """
+    try:
+        if model_id not in st.session_state.trained_models:
+            return "Error: Model not found. Please select a valid model."
+        
+        model_data = st.session_state.trained_models[model_id]
+        model = model_data['model']
+        tokenizer = model_data['tokenizer']
+        
+        if TRANSFORMERS_AVAILABLE:
+            # Tokenize the prompt
+            inputs = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True)
+            
+            # Generate text
+            with torch.no_grad():
+                outputs = model.generate(
+                    inputs.input_ids,
+                    max_length=max_length,
+                    temperature=temperature,
+                    top_p=top_p,
+                    num_return_sequences=1,
+                    pad_token_id=tokenizer.eos_token_id
+                )
+            
+            # Decode the generated text
+            generated_code = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        else:
+            # Demo mode - return dummy generated code
+            inputs = tokenizer(prompt)
+            outputs = model.generate(inputs["input_ids"])
+            generated_code = tokenizer.decode(outputs[0])
+            
+            # Add some context to the generated code based on the prompt
+            if "fibonacci" in prompt.lower():
+                generated_code = "def fibonacci(n):\n    if n <= 0:\n        return 0\n    elif n == 1:\n        return 1\n    else:\n        return fibonacci(n-1) + fibonacci(n-2)\n"
+            elif "sort" in prompt.lower():
+                generated_code = "def bubble_sort(arr):\n    n = len(arr)\n    for i in range(n):\n        for j in range(0, n-i-1):\n            if arr[j] > arr[j+1]:\n                arr[j], arr[j+1] = arr[j+1], arr[j]\n    return arr\n"
+        
+        # If the prompt is included in the output, remove it to get only the generated code
+        if generated_code.startswith(prompt):
+            generated_code = generated_code[len(prompt):]
+            
+        return generated_code
+        
+    except Exception as e:
+        add_log(f"Error generating code: {str(e)}", "ERROR")
+        return f"Error generating code: {str(e)}"
+
+def get_model_info(model_id):
+    """
+    Get information about a model.
+    
+    Args:
+        model_id: ID of the model
+        
+    Returns:
+        dict: Model information
+    """
+    if 'trained_models' in st.session_state and model_id in st.session_state.trained_models:
+        return st.session_state.trained_models[model_id]['info']
+    return None

mypy.ini

@@ -0,0 +1,5 @@
+
+[mypy]
+python_version = 3.11
+disallow_untyped_defs = True
+ignore_missing_imports = True

pyproject.toml

@@ -0,0 +1,1135 @@
+[project]
+name = "repl-nix-workspace"
+version = "0.1.0"
+description = "Add your description here"
+requires-python = ">=3.11"
+dependencies = [
+    "streamlit>=1.43.2",
+    "pandas>=2.0.0",
+    "torch>=2.0.0",
+    "plotly>=5.0.0",
+    "transformers>=4.30.0",
+    "datasets>=2.10.0",
+]
+
+[[tool.uv.index]]
+explicit = true
+name = "pytorch-cpu"
+url = "https://download.pytorch.org/whl/cpu"
+
+[tool.uv.sources]
+AA-module = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+ABlooper = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+AnalysisG = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+AutoRAG = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+BERTeam = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+BxTorch = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+Byaldi = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+CALM-Pytorch = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+COPEX-high-rate-compression-quality-metrics = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+CityLearn = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+CoCa-pytorch = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+CoLT5-attention = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+ComfyUI-EasyNodes = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+Crawl4AI = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+DALL-E = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+DI-toolkit = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+DatasetRising = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+DeepCache = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+DeepMatter = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+Draugr = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+ESRNN = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+En-transformer = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+ExpoSeq = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+FLAML = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+FSRS-Optimizer = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+GANDLF = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+GQLAlchemy = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+GhostScan = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+GraKeL = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+HEBO = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+IOPaint = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+ISLP = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+InvokeAI = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+JAEN = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+KapoorLabs-Lightning = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+LightAutoML = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+LingerGRN = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+MMEdu = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+MRzeroCore = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+Modeva = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+NeuralFoil = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+NiMARE = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+NinjaTools = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+OpenHosta = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+OpenNMT-py = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+POT = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+PVNet = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+PaLM-rlhf-pytorch = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+PepperPepper = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+PiML = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+Poutyne = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+QNCP = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+RAGatouille = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+RareGO = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+RealtimeSTT = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+RelevanceAI-Workflows-Core = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+Resemblyzer = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+ScandEval = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+Simba-UW-tf-dev = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+SwissArmyTransformer = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+TPOT = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
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+x-clip = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+x-transformers = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+xaitk_saliency = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+xformers = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+xgrammar = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+xinference = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+xtts-api-server = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+yolo-poser = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+yolov5 = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+yolov7-package = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+yta-general-utils = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+zensvi = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+zetascale = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+zuko = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]

pytest.ini

@@ -0,0 +1,5 @@
+
+[pytest]
+testpaths = tests
+python_files = test_*.py
+addopts = -v -ra

replit.nix

@@ -0,0 +1,5 @@
+{pkgs}: {
+  deps = [
+    pkgs.glibcLocales
+  ];
+}

training_utils.py

@@ -0,0 +1,381 @@
+import streamlit as st
+import threading
+import random
+import time
+from datetime import datetime
+from utils import add_log, timestamp
+
+# Handle missing dependencies
+try:
+    import torch
+    import pandas as pd
+    from transformers import TrainingArguments as HFTrainingArguments
+    from transformers import Trainer, AutoModelForCausalLM, AutoTokenizer
+    from datasets import Dataset, DatasetDict
+    TRANSFORMERS_AVAILABLE = True
+except ImportError:
+    TRANSFORMERS_AVAILABLE = False
+    HFTrainingArguments = None
+    
+    # For demo purposes
+    class DummyTrainer:
+        def __init__(self, **kwargs):
+            self.callback = type('obj', (object,), {'__init__': lambda self: None})
+            
+        def train(self):
+            pass
+
+def initialize_training_progress(model_id):
+    """
+    Initialize training progress tracking for a model.
+    
+    Args:
+        model_id: Identifier for the model
+    """
+    if 'training_progress' not in st.session_state:
+        st.session_state.training_progress = {}
+        
+    st.session_state.training_progress[model_id] = {
+        'status': 'initialized',
+        'current_epoch': 0,
+        'total_epochs': 0,
+        'loss_history': [],
+        'started_at': timestamp(),
+        'completed_at': None,
+        'progress': 0.0
+    }
+
+def update_training_progress(model_id, epoch=None, loss=None, status=None, progress=None, total_epochs=None):
+    """
+    Update training progress for a model.
+    
+    Args:
+        model_id: Identifier for the model
+        epoch: Current epoch
+        loss: Current loss value
+        status: Training status
+        progress: Progress percentage (0-100)
+        total_epochs: Total number of epochs
+    """
+    if 'training_progress' not in st.session_state or model_id not in st.session_state.training_progress:
+        initialize_training_progress(model_id)
+        
+    progress_data = st.session_state.training_progress[model_id]
+    
+    if epoch is not None:
+        progress_data['current_epoch'] = epoch
+    
+    if loss is not None:
+        progress_data['loss_history'].append(loss)
+    
+    if status is not None:
+        progress_data['status'] = status
+        if status == 'completed':
+            progress_data['completed_at'] = timestamp()
+            progress_data['progress'] = 100.0
+    
+    if progress is not None:
+        progress_data['progress'] = progress
+        
+    if total_epochs is not None:
+        progress_data['total_epochs'] = total_epochs
+
+def tokenize_dataset(dataset, tokenizer, max_length=512):
+    """
+    Tokenize a dataset for model training.
+    
+    Args:
+        dataset: The dataset to tokenize
+        tokenizer: The tokenizer to use
+        max_length: Maximum sequence length
+        
+    Returns:
+        Dataset: Tokenized dataset
+    """
+    def tokenize_function(examples):
+        return tokenizer(examples['code'], padding='max_length', truncation=True, max_length=max_length)
+    
+    tokenized_dataset = dataset.map(tokenize_function, batched=True)
+    return tokenized_dataset
+
+def train_model_thread(model_id, dataset_name, base_model_name, training_args, device, stop_event):
+    """
+    Thread function for training a model.
+    
+    Args:
+        model_id: Identifier for the model
+        dataset_name: Name of the dataset to use
+        base_model_name: Base model from Hugging Face
+        training_args: Training arguments
+        device: Device to use for training (cpu/cuda)
+        stop_event: Threading event to signal stopping
+    """
+    try:
+        # Get dataset
+        dataset = st.session_state.datasets[dataset_name]['data']
+        
+        # Initialize model and tokenizer
+        add_log(f"Initializing model {base_model_name} for training")
+        tokenizer = AutoTokenizer.from_pretrained(base_model_name)
+        model = AutoModelForCausalLM.from_pretrained(base_model_name)
+        
+        # Check if tokenizer has padding token
+        if tokenizer.pad_token is None:
+            tokenizer.pad_token = tokenizer.eos_token
+            model.config.pad_token_id = model.config.eos_token_id
+        
+        # Tokenize dataset
+        add_log(f"Tokenizing dataset {dataset_name}")
+        train_dataset = tokenize_dataset(dataset['train'], tokenizer)
+        val_dataset = tokenize_dataset(dataset['validation'], tokenizer)
+        
+        # Update training progress
+        update_training_progress(
+            model_id, 
+            status='running',
+            total_epochs=training_args.num_train_epochs
+        )
+        
+        # Define custom callback to track progress
+        class CustomCallback(Trainer.callback):
+            def on_epoch_end(self, args, state, control, **kwargs):
+                current_epoch = state.epoch
+                epoch_loss = state.log_history[-1].get('loss', 0)
+                update_training_progress(
+                    model_id, 
+                    epoch=current_epoch, 
+                    loss=epoch_loss,
+                    progress=(current_epoch / training_args.num_train_epochs) * 100
+                )
+                add_log(f"Epoch {current_epoch}/{training_args.num_train_epochs} completed. Loss: {epoch_loss:.4f}")
+                
+                # Check if training should be stopped
+                if stop_event.is_set():
+                    add_log(f"Training for model {model_id} was manually stopped")
+                    control.should_training_stop = True
+        
+        # Configure training arguments
+        args = HFTrainingArguments(
+            output_dir=f"./results/{model_id}",
+            evaluation_strategy="epoch",
+            learning_rate=training_args.learning_rate,
+            per_device_train_batch_size=training_args.batch_size,
+            per_device_eval_batch_size=training_args.batch_size,
+            num_train_epochs=training_args.num_train_epochs,
+            weight_decay=0.01,
+            save_total_limit=1,
+        )
+        
+        # Initialize trainer
+        trainer = Trainer(
+            model=model,
+            args=args,
+            train_dataset=train_dataset,
+            eval_dataset=val_dataset,
+            tokenizer=tokenizer,
+            callbacks=[CustomCallback]
+        )
+        
+        # Train the model
+        add_log(f"Starting training for model {model_id}")
+        trainer.train()
+        
+        # Save the model
+        if not stop_event.is_set():
+            add_log(f"Training completed for model {model_id}")
+            update_training_progress(model_id, status='completed')
+            
+            # Save to session state
+            st.session_state.trained_models[model_id] = {
+                'model': model,
+                'tokenizer': tokenizer,
+                'info': {
+                    'id': model_id,
+                    'base_model': base_model_name,
+                    'dataset': dataset_name,
+                    'created_at': timestamp(),
+                    'epochs': training_args.num_train_epochs,
+                    'learning_rate': training_args.learning_rate,
+                    'batch_size': training_args.batch_size
+                }
+            }
+        
+    except Exception as e:
+        add_log(f"Error during training model {model_id}: {str(e)}", "ERROR")
+        update_training_progress(model_id, status='failed')
+
+class TrainingArguments:
+    def __init__(self, learning_rate, batch_size, num_train_epochs):
+        self.learning_rate = learning_rate
+        self.batch_size = batch_size
+        self.num_train_epochs = num_train_epochs
+
+def start_model_training(model_id, dataset_name, base_model_name, learning_rate, batch_size, epochs):
+    """
+    Start model training in a separate thread.
+    
+    Args:
+        model_id: Identifier for the model
+        dataset_name: Name of the dataset to use
+        base_model_name: Base model from Hugging Face
+        learning_rate: Learning rate for training
+        batch_size: Batch size for training
+        epochs: Number of training epochs
+        
+    Returns:
+        threading.Event: Event to signal stopping the training
+    """
+    # Use simulate_training instead if transformers isn't available
+    if not TRANSFORMERS_AVAILABLE:
+        add_log("No transformers library available, using simulation mode")
+        return simulate_training(model_id, dataset_name, base_model_name, epochs)
+        
+    # Create training arguments
+    training_args = TrainingArguments(
+        learning_rate=learning_rate,
+        batch_size=batch_size,
+        num_train_epochs=epochs
+    )
+    
+    # Determine device
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    add_log(f"Using device: {device}")
+    
+    # Initialize training progress
+    initialize_training_progress(model_id)
+    
+    # Create stop event
+    stop_event = threading.Event()
+    
+    # Start training thread
+    training_thread = threading.Thread(
+        target=train_model_thread,
+        args=(model_id, dataset_name, base_model_name, training_args, device, stop_event)
+    )
+    training_thread.start()
+    
+    return stop_event
+
+def stop_model_training(model_id, stop_event):
+    """
+    Stop model training.
+    
+    Args:
+        model_id: Identifier for the model
+        stop_event: Threading event to signal stopping
+    """
+    if stop_event.is_set():
+        return
+        
+    add_log(f"Stopping training for model {model_id}")
+    stop_event.set()
+    
+    # Update training progress
+    if 'training_progress' in st.session_state and model_id in st.session_state.training_progress:
+        progress_data = st.session_state.training_progress[model_id]
+        if progress_data['status'] == 'running':
+            progress_data['status'] = 'stopped'
+            progress_data['completed_at'] = timestamp()
+
+def get_running_training_jobs():
+    """
+    Get list of currently running training jobs.
+    
+    Returns:
+        list: List of model IDs with running training jobs
+    """
+    running_jobs = []
+    
+    if 'training_progress' in st.session_state:
+        for model_id, progress in st.session_state.training_progress.items():
+            if progress['status'] == 'running':
+                running_jobs.append(model_id)
+                
+    return running_jobs
+
+# For demo purposes - Simulate training progress without actual model training
+def simulate_training_thread(model_id, dataset_name, base_model_name, epochs, stop_event):
+    """
+    Simulate training progress for demonstration purposes.
+    
+    Args:
+        model_id: Identifier for the model
+        dataset_name: Name of the dataset to use
+        base_model_name: Base model from Hugging Face
+        epochs: Number of training epochs
+        stop_event: Threading event to signal stopping
+    """
+    add_log(f"Starting simulated training for model {model_id}")
+    update_training_progress(model_id, status='running', total_epochs=epochs)
+    
+    for epoch in range(1, epochs + 1):
+        if stop_event.is_set():
+            add_log(f"Simulated training for model {model_id} was manually stopped")
+            update_training_progress(model_id, status='stopped')
+            return
+            
+        # Simulate epoch time
+        time.sleep(2)
+        
+        # Generate random loss that decreases over time
+        loss = max(0.1, 2.0 - (epoch / epochs) * 1.5 + random.uniform(-0.1, 0.1))
+        
+        # Update progress
+        update_training_progress(
+            model_id,
+            epoch=epoch,
+            loss=loss,
+            progress=(epoch / epochs) * 100
+        )
+        
+        add_log(f"Epoch {epoch}/{epochs} completed. Loss: {loss:.4f}")
+    
+    # Training completed
+    add_log(f"Simulated training completed for model {model_id}")
+    update_training_progress(model_id, status='completed')
+    
+    # Create dummy model and tokenizer
+    tokenizer = AutoTokenizer.from_pretrained(base_model_name)
+    model = AutoModelForCausalLM.from_pretrained(base_model_name)
+    
+    # Save to session state
+    st.session_state.trained_models[model_id] = {
+        'model': model,
+        'tokenizer': tokenizer,
+        'info': {
+            'id': model_id,
+            'base_model': base_model_name,
+            'dataset': dataset_name,
+            'created_at': timestamp(),
+            'epochs': epochs,
+            'simulated': True
+        }
+    }
+
+def simulate_training(model_id, dataset_name, base_model_name, epochs):
+    """
+    Start simulated training in a separate thread.
+    
+    Args:
+        model_id: Identifier for the model
+        dataset_name: Name of the dataset to use
+        base_model_name: Base model from Hugging Face
+        epochs: Number of training epochs
+        
+    Returns:
+        threading.Event: Event to signal stopping the training
+    """
+    # Initialize training progress
+    initialize_training_progress(model_id)
+    
+    # Create stop event
+    stop_event = threading.Event()
+    
+    # Start training thread
+    training_thread = threading.Thread(
+        target=simulate_training_thread,
+        args=(model_id, dataset_name, base_model_name, epochs, stop_event)
+    )
+    training_thread.start()
+    
+    return stop_event

utils.py

@@ -0,0 +1,102 @@
+import streamlit as st
+import os
+import time
+from datetime import datetime
+import pandas as pd
+
+# Handle missing plotly dependency
+try:
+    import plotly.express as px
+except ImportError:
+    px = None
+
+def set_page_config():
+    """Set the page configuration with title and layout."""
+    st.set_page_config(
+        page_title="CodeGen Hub",
+        page_icon="💻",
+        layout="wide",
+        initial_sidebar_state="expanded"
+    )
+
+def display_sidebar():
+    """Display sidebar with navigation options."""
+    st.sidebar.title("CodeGen Hub")
+    st.sidebar.markdown("---")
+    
+    st.sidebar.subheader("Navigation")
+    st.sidebar.markdown("""
+    - [Home](/)
+    - [Dataset Management](/Dataset_Management)
+    - [Model Training](/Model_Training)
+    - [Code Generation](/Code_Generation)
+    """)
+    
+    st.sidebar.markdown("---")
+    st.sidebar.subheader("Resources")
+    st.sidebar.markdown("""
+    - [Hugging Face Hub](https://huggingface.co/models)
+    - [Transformers Documentation](https://huggingface.co/docs/transformers/index)
+    - [Streamlit Documentation](https://docs.streamlit.io/)
+    """)
+    
+    st.sidebar.markdown("---")
+    st.sidebar.caption("© 2023 CodeGen Hub")
+
+def timestamp():
+    """Return a formatted timestamp for logging."""
+    return datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+
+def add_log(message, log_type="INFO"):
+    """Add a log message to the training logs."""
+    log_entry = f"[{timestamp()}] [{log_type}] {message}"
+    if 'training_logs' not in st.session_state:
+        st.session_state.training_logs = []
+    st.session_state.training_logs.append(log_entry)
+    return log_entry
+
+def display_logs():
+    """Display the training logs in a text area."""
+    if 'training_logs' in st.session_state and st.session_state.training_logs:
+        logs = '\n'.join(st.session_state.training_logs)
+        st.text_area('Training Logs', logs, height=300)
+    else:
+        st.text_area('Training Logs', 'No logs available.', height=300)
+
+def plot_training_progress(model_id):
+    """Plot training progress for a model."""
+    if 'training_progress' in st.session_state and model_id in st.session_state.training_progress:
+        progress = st.session_state.training_progress[model_id]
+        
+        if 'loss_history' in progress and progress['loss_history']:
+            # Create DataFrame for plotting
+            df = pd.DataFrame({
+                'Step': list(range(1, len(progress['loss_history']) + 1)),
+                'Loss': progress['loss_history']
+            })
+            
+            if px is not None:
+                # Create plot with plotly if available
+                fig = px.line(df, x='Step', y='Loss', title=f'Training Loss for {model_id}')
+                fig.update_layout(height=400)
+                st.plotly_chart(fig, use_container_width=True)
+            else:
+                # Fallback to simple display without plotly
+                st.subheader(f"Training Loss for {model_id}")
+                st.dataframe(df)
+                st.info("Install plotly for interactive charts")
+        else:
+            st.info("No training progress data available yet.")
+    else:
+        st.info("No training progress data available for this model.")
+
+def format_code(code):
+    """Format code for display."""
+    return code.strip()
+
+def create_folder_if_not_exists(folder_path):
+    """Create folder if it doesn't exist."""
+    if not os.path.exists(folder_path):
+        os.makedirs(folder_path)
+        return True
+    return False