Queue-Tip/PLAI

1import pandas as pd2from datasets import Dataset3from transformers import pipeline, GPT2Tokenizer4from sentence_transformers import SentenceTransformer, util5 6# Define paths and models7filename = "output_topic_details.txt"8retrieval_model_name = 'output/sentence-transformer-finetuned/'       #using a prefine-tuned model9gpt2_model_name = "gpt2"10csv_file_path = "train_dataset.csv"11output_csv_file_path = "updated_train_dataset.csv"12val_csv_file_path = "val_dataset.csv"13output_val_csv_file_path = "updated_val_csv.csv"14 15tokenizer = GPT2Tokenizer.from_pretrained(gpt2_model_name)16 17# Initialize models18try:19    retrieval_model = SentenceTransformer(retrieval_model_name)20    gpt_model = pipeline("text-generation", model=gpt2_model_name)21    print("Models loaded successfully.")22except Exception as e:23    print(f"Failed to load models: {e}")24 25def load_and_preprocess_text(filename):26    """27    Load and preprocess text data from a file.28 29    Parameters:30    - filename (str): Path to the text file.31 32    Returns:33    - list[str]: A list of preprocessed text segments.34    """35    try:36        with open(filename, 'r', encoding='utf-8') as file:37            segments = [line.strip() for line in file if line.strip()]38        print("Text loaded and preprocessed successfully.")39        return segments40    except Exception as e:41        print(f"Failed to load or preprocess text: {e}")42        return []43 44segments = load_and_preprocess_text(filename)45 46def find_relevant_segment(user_query, segments):47    """48    Find the most relevant text segment based on a user query.49 50    Parameters:51    - user_query (str): The user's query.52    - segments (list[str]): List of text segments to search within.53 54    Returns:55    - str: The most relevant text segment.56    """57    try:58        query_embedding = retrieval_model.encode(user_query)59        segment_embeddings = retrieval_model.encode(segments)60        similarities = util.pytorch_cos_sim(query_embedding, segment_embeddings)[0]61        best_idx = similarities.argmax()62        return segments[best_idx]63    except Exception as e:64        print(f"Error finding relevant segment: {e}")65        return ""66 67def generate_response(question):68    """69    Generate a response to a given question by finding a relevant text segment and70    using it to generate a more complete answer.71 72    Parameters:73    - question (str): The user's question.74 75    Returns:76    - str: Generated response.77    """78    relevant_segment = find_relevant_segment(question, segments)79    return generate_response_with_context(question, relevant_segment)80 81def generate_response_with_context(user_query, relevant_segment):82    """83    Generate a response based on a user query and a relevant segment.84 85    Parameters:86    - user_query (str): The user's query.87    - relevant_segment (str): A relevant fact or detail.88 89    Returns:90    - str: Formatted response incorporating the relevant segment.91    """92    try:93        prompt = f"Thank you for your question! Here is an additional fact about your topic: {relevant_segment}"94        max_tokens = len(tokenizer(prompt)['input_ids']) + 5095        response = gpt_model(prompt, max_length=max_tokens, temperature=0.25)[0]['generated_text']96        return clean_up_response(response, relevant_segment)97    except Exception as e:98        print(f"Error generating response: {e}")99        return ""100 101def clean_up_response(response, segment):102    """103    Clean up the generated response to ensure it is tidy and presentable.104 105    Parameters:106    - response (str): The initial response generated by the model.107    - segment (str): The segment used to generate the response.108 109    Returns:110    - str: A cleaned and formatted response.111    """112    sentences = response.split('.')113    cleaned_sentences = [sentence.strip() for sentence in sentences if sentence.strip() and sentence.strip() not in segment]114    cleaned_response = '. '.join(cleaned_sentences).strip()115    if cleaned_response and not cleaned_response.endswith((".", "!", "?")):116        cleaned_response += "."117    return cleaned_response118 119def process_dataset(csv_file_path, output_csv_file_path):120    """121    Process the dataset by generating responses and evaluating their similarities.122 123    Parameters:124    - csv_file_path (str): Path to the CSV file containing the dataset.125    - output_csv_file_path (str): Path where the updated dataset will be saved.126 127    Prints:128    - Path to the saved results and the average similarity score.129    """130    df = pd.read_csv(csv_file_path)131    dataset = Dataset.from_pandas(df)132    updated_dataset = add_model_answers(dataset)133    similarities = evaluate_similarity(updated_dataset)134    updated_dataset = updated_dataset.add_column("similarity", similarities)135    results_df = updated_dataset.to_pandas()136    results_df.to_csv(output_csv_file_path, index=False)137    average_similarity = sum(similarities) / len(similarities) if similarities else 0138    print(f"Results saved to {output_csv_file_path}")139    print(f"Average Similarity Score: {average_similarity:.3f}")140 141def add_model_answers(dataset):142    """143    Add generated answers to the dataset.144 145    Parameters:146    - dataset (datasets.Dataset): The Hugging Face dataset object.147 148    Returns:149    - datasets.Dataset: Updated dataset with added answers.150    """151    answers = [generate_response(q) for q in dataset['Question']]152    dataset = dataset.add_column("Answer", answers)153    return dataset154 155def evaluate_similarity(dataset):156    """157    Evaluate the similarity of generated answers against ground truth answers.158 159    Parameters:160    - dataset (datasets.Dataset): The dataset containing both answers and ground truths.161 162    Returns:163    - list[float]: List of similarity scores.164    """165    similarities = [util.pytorch_cos_sim(retrieval_model.encode(ans), retrieval_model.encode(gt))[0][0].item()166                    for ans, gt in zip(dataset['Answer'], dataset['GroundTruth'])]167    return similarities168 169# Process datasets170process_dataset(csv_file_path, output_csv_file_path)171process_dataset(val_csv_file_path, output_val_csv_file_path)172