Upload ai-project-1756522506833.txt

ai-project-1756522506833.txt

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+AI PROJECT ARCHIVE
+Generated by Arch1tech - Or4cl3 AI Solutions
+Archive Date: 2025-08-30T02:55:06.788Z
+Files Count: 8
+
+============================================================
+INSTALLATION INSTRUCTIONS
+============================================================
+
+1. Extract all files to your project directory
+2. Install dependencies: pip install -r requirements.txt
+3. Follow the README.md for specific setup instructions
+4. Run the main script or start the training process
+
+============================================================
+PROJECT FILES
+============================================================
+
+============================================================
+FILE: train.py
+TYPE: python
+DESCRIPTION: Training script using Hugging Face Transformers.
+============================================================
+
+import torch
+from transformers import AutoModelForSequenceClassification, Trainer, TrainingArguments, AutoTokenizer
+from datasets import load_dataset
+
+# Load dataset
+dataset = load_dataset('glue', 'mrpc')
+
+# Load pre-trained model and tokenizer
+model_name = 'bert-base-uncased'
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Tokenize the dataset
+def tokenize_function(examples):
+    return tokenizer(examples['sentence1'], examples['sentence2'], truncation=True)
+
+tokenized_datasets = dataset.map(tokenize_function, batched=True)
+
+# Training arguments
+training_args = TrainingArguments(
+    output_dir='./results',
+    evaluation_strategy="epoch",
+    learning_rate=2e-5,
+    per_device_train_batch_size=16,
+    per_device_eval_batch_size=16,
+    num_train_epochs=3,
+    weight_decay=0.01,
+)
+
+# Trainer
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=tokenized_datasets['train'],
+    eval_dataset=tokenized_datasets['validation'],
+)
+
+# Train
+trainer.train()
+
+
+============================================================
+FILE: model_config.json
+TYPE: json
+DESCRIPTION: Model configuration for training and inference.
+============================================================
+
+{ "model_type": "BERT", "pretrained": "bert-base-uncased", "num_labels": 2, "output_dir": "./results/" }
+
+
+============================================================
+FILE: requirements.txt
+TYPE: text
+DESCRIPTION: Python package dependencies.
+============================================================
+
+torch==1.12.1
+transformers==4.12.3
+datasets==1.14.1
+fastapi==0.78.0
+uvicorn==0.18.1
+pydantic==1.9.0
+numpy==1.21.2
+pandas==1.3.3
+
+
+
+============================================================
+FILE: README.md
+TYPE: markdown
+DESCRIPTION: Comprehensive documentation with setup instructions.
+============================================================
+
+# CognoSphere Unified Multimodal Language Model (CSUMLM)
+This repository provides the implementation of CSUMLM, a Python-based AI system for multimodal language tasks.
+
+## Setup Instructions
+1. Clone this repository.
+2. Install the required packages using the command:
+   ```bash
+   pip install -r requirements.txt
+   ```
+3. Run the training script:
+   ```bash
+   python train.py
+   ```
+
+## Inference
+To deploy the model for inference, run:
+```bash
+uvicorn api:app --reload
+```
+
+
+============================================================
+FILE: data_processing.py
+TYPE: python
+DESCRIPTION: Script for data preparation and cleaning.
+============================================================
+
+import pandas as pd
+from sklearn.model_selection import train_test_split
+
+def preprocess_data(file_path):
+    # Load data
+    df = pd.read_csv(file_path)
+    # Data cleaning steps
+    df = df.dropna()
+    
+    # Split into train and test sets
+    train, test = train_test_split(df, test_size=0.2)
+    train.to_csv('train.csv', index=False)
+    test.to_csv('test.csv', index=False)
+
+
+
+============================================================
+FILE: api.py
+TYPE: python
+DESCRIPTION: FastAPI deployment endpoint for inference.
+============================================================
+
+from fastapi import FastAPI
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+
+app = FastAPI()
+
+# Load model and tokenizer
+model = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased')
+tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
+
+@app.post('/predict/')
+async def predict(text: str):
+    inputs = tokenizer(text, return_tensors='pt')
+    outputs = model(**inputs)
+    predictions = torch.argmax(outputs.logits, dim=1)
+    return {'predictions': predictions.tolist()}
+
+
+============================================================
+FILE: Dockerfile
+TYPE: dockerfile
+DESCRIPTION: Docker container configuration.
+============================================================
+
+FROM python:3.8-slim
+
+WORKDIR /app
+
+COPY requirements.txt ./
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . ./
+
+CMD ["uvicorn", "api:app", "--host", "0.0.0.0", "--port", "8000", "--reload"]
+
+
+============================================================
+FILE: test_model.py
+TYPE: python
+DESCRIPTION: Unit tests for model validation.
+============================================================
+
+import pytest
+from fastapi.testclient import TestClient
+from api import app
+
+def test_prediction():
+    client = TestClient(app)
+    response = client.post('/predict/', json={'text': 'Hello, world!'})
+    assert response.status_code == 200
+    assert 'predictions' in response.json()
+
+