Merge pull request #132 from VishwamAI/feature/generative_ai_updates

Add generative AI model and evaluation scripts

NeuroFlex/Transformers/evaluate_model.py

@@ -0,0 +1,43 @@
+import torch
+from generative_ai_model import T5Model, DevInAI, ChainOfThought, MultiPromptHandler
+
+def evaluate_model(devin_ai, chain_of_thought, multi_prompt_handler):
+    print("Evaluating Generative AI Text-to-Text Model")
+    print("===========================================")
+
+    # Test basic text generation
+    prompt = "Translate the following English text to French: 'Hello, how are you?'"
+    response = devin_ai.generate_multiple_texts([prompt])[0]
+    print(f"\nBasic Text Generation:")
+    print(f"Prompt: {prompt}")
+    print(f"Response: {response}")
+
+    # Test chain-of-thought reasoning
+    initial_prompt = "Explain the process of photosynthesis"
+    reasoning_steps = chain_of_thought.reason_through_steps(initial_prompt, num_steps=3)
+    print("\nChain-of-Thought Reasoning:")
+    for i, step in enumerate(reasoning_steps):
+        print(f"Step {i}: {step}")
+
+    # Test multi-prompt development
+    prompts = [
+        "What is the capital of France?",
+        "Explain quantum physics in simple terms.",
+        "Describe the water cycle."
+    ]
+    multi_prompt_response = multi_prompt_handler.generate_with_multi_prompt(prompts)
+    print("\nMulti-Prompt Development:")
+    print(f"Prompts: {prompts}")
+    print(f"Combined Response: {multi_prompt_response}")
+
+if __name__ == "__main__":
+    # Initialize model and components
+    model = T5Model()
+    devin_ai = DevInAI(model)
+    chain_of_thought = ChainOfThought(devin_ai)
+    multi_prompt_handler = MultiPromptHandler(devin_ai)
+
+    # Evaluate the model
+    evaluate_model(devin_ai, chain_of_thought, multi_prompt_handler)
+
+    print("\nEvaluation complete. Please review the outputs to assess the model's performance.")

NeuroFlex/Transformers/generative_ai_model.py

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+import torch
+from torch import nn
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+
+class T5Model:
+    def __init__(self, model_name="t5-base"):
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        self.model = T5ForConditionalGeneration.from_pretrained(model_name).to(self.device)
+        self.tokenizer = T5Tokenizer.from_pretrained(model_name)
+
+    def generate_text(self, input_text, max_length=100):
+        input_ids = self.tokenizer.encode(input_text, return_tensors="pt").to(self.device)
+        output_ids = self.model.generate(input_ids, max_length=max_length)
+        return self.tokenizer.decode(output_ids[0], skip_special_tokens=True)
+
+class DevInAI:
+    def __init__(self, model):
+        self.model = model
+
+    def generate_multiple_texts(self, input_texts):
+        outputs = []
+        for input_text in input_texts:
+            outputs.append(self.model.generate_text(input_text))
+        return outputs
+
+class ChainOfThought:
+    def __init__(self, devin_ai):
+        self.devin_ai = devin_ai
+
+    def reason_through_steps(self, initial_prompt, num_steps):
+        reasoning_steps = [initial_prompt]
+        context = ""
+        for step in range(num_steps):
+            prompt = f"Context: {context}\nGiven the previous step: '{reasoning_steps[-1]}', provide the next logical step in explaining this concept. Be specific, detailed, and ensure continuity with previous steps. Focus on explaining one aspect of the concept in depth."
+            next_step = self.devin_ai.generate_multiple_texts([prompt])[0]
+            reasoning_steps.append(next_step)
+            context += f"Step {step + 1}: {next_step}\n"
+        return reasoning_steps
+
+class MultiPromptHandler:
+    def __init__(self, devin_ai):
+        self.devin_ai = devin_ai
+
+    def generate_with_multi_prompt(self, prompts):
+        responses = self.devin_ai.generate_multiple_texts(prompts)
+        combined_prompt = "Synthesize the following information into a coherent and detailed response. Ensure logical flow and connections between ideas:\n"
+        for i, response in enumerate(responses):
+            combined_prompt += f"{i+1}. {response}\n"
+        combined_prompt += "Provide a comprehensive answer that addresses all points while maintaining a clear narrative structure. Use transitional phrases to connect ideas."
+        final_response = self.devin_ai.generate_multiple_texts([combined_prompt])[0]
+        return final_response
+
+def integrate_huggingface_dataset(dataset_name, tokenizer, max_length=512):
+    from datasets import load_dataset
+    dataset = load_dataset(dataset_name)
+
+    def preprocess_function(examples):
+        return tokenizer(examples["text"], truncation=True, padding="max_length", max_length=max_length)
+
+    tokenized_dataset = dataset.map(preprocess_function, batched=True, remove_columns=dataset["train"].column_names)
+    return tokenized_dataset
+
+# Instructions for training the model on Hugging Face
+
+
+
+# Instructions for training the model on Hugging Face
+
+def prepare_huggingface_dataset(dataset_name, tokenizer, max_length=512):
+    from datasets import load_dataset
+    dataset = load_dataset(dataset_name)
+
+    def tokenize_function(examples):
+        return tokenizer(examples["text"], truncation=True, padding="max_length", max_length=max_length)
+
+    tokenized_dataset = dataset.map(tokenize_function, batched=True)
+    return tokenized_dataset
+
+def initialize_model():
+    return T5Model()
+
+# Example usage
+if __name__ == "__main__":
+    model = initialize_model()
+    devin_ai = DevInAI(model)
+    chain_of_thought = ChainOfThought(devin_ai)
+    multi_prompt_handler = MultiPromptHandler(devin_ai)
+
+    # Test chain-of-thought reasoning
+    initial_prompt = "Explain the process of photosynthesis"
+    reasoning_steps = chain_of_thought.reason_through_steps(initial_prompt, num_steps=3)
+    print("Chain-of-Thought Reasoning:")
+    for i, step in enumerate(reasoning_steps):
+        print(f"Step {i}: {step}")
+
+    # Test multi-prompt development
+    prompts = [
+        "What is the capital of France?",
+        "Explain quantum physics in simple terms.",
+        "Describe the water cycle."
+    ]
+    multi_prompt_response = multi_prompt_handler.generate_with_multi_prompt(prompts)
+    print("\nMulti-Prompt Response:")
+    print(multi_prompt_response)
+
+# Instructions for integrating Hugging Face datasets and training the model
+def load_dataset(dataset_name):
+    from datasets import load_dataset
+    return load_dataset(dataset_name)
+
+def prepare_data(dataset, tokenizer, max_length=512):
+    def tokenize_function(examples):
+        return tokenizer(examples["text"], truncation=True, padding="max_length", max_length=max_length)
+    return dataset.map(tokenize_function, batched=True)
+
+def train_model(model, dataset, tokenizer, num_epochs=3, batch_size=8):
+    from transformers import Trainer, TrainingArguments
+
+    training_args = TrainingArguments(
+        output_dir="./results",
+        num_train_epochs=num_epochs,
+        per_device_train_batch_size=batch_size,
+        save_steps=10_000,
+        save_total_limit=2,
+    )
+
+    trainer = Trainer(
+        model=model,
+        args=training_args,
+        train_dataset=dataset["train"],
+        tokenizer=tokenizer,
+    )
+
+    trainer.train()
+
+# Example usage for training
+if __name__ == "__main__":
+    model = initialize_model()
+    dataset = load_dataset("your_dataset_name")
+    tokenized_dataset = prepare_data(dataset, model.tokenizer)
+    train_model(model.model, tokenized_dataset, model.tokenizer)