app.py

# -*- coding: utf-8 -*-
"""Fine Tuned Llama 2 for Comment Analysis

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1NX5z-wVpsEp8UigB0q7vZSZMFRa6nnEE

##**Extract Youtube Comments**
"""

# !pip uninstall gradio
# !pip3 install gradio -q
# !pip install --upgrade fastapi -q
# !pip install typing-extensions --upgrade

# import locale
# locale.setlocale(locale.LC_ALL, 'en_US.UTF-8')

# import locale
# locale.getpreferredencoding = lambda: "UTF-8"

# !pip3 install typing-extensions==4.2.0
# !pip3 install gradio -q
# !pip3 install --upgrade tensorflow

import pandas as pd
import gradio as gr
from googleapiclient.discovery import build
import csv
# import gradio as gr
from PIL import Image
import io

api_key = 'AIzaSyANfQYiumNUfJ8_YaDg-Hfr0BRXFhXnbvQ'

def video_comments(video_id):
    # Create a CSV file to store comments
    with open('comments.csv', 'w', newline='', encoding='utf-8') as csvfile:
        fieldnames = ['Comment']
        writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
        writer.writeheader()

        # Counter to limit the number of comments
        comment_count = 0

        # creating youtube resource object
        youtube = build('youtube', 'v3', developerKey=api_key)

        # retrieve youtube video results
        video_response = youtube.commentThreads().list(
            part='snippet,replies',
            videoId=video_id,
            maxResults=100  # Adjust the number of comments per page as needed
        ).execute()

        # iterate video response
        while video_response:

            # extracting required info from each result object
            for item in video_response['items']:

                # Extracting comments
                comment = item['snippet']['topLevelComment']['snippet']['textDisplay']

                # Write the comment to the CSV file
                writer.writerow({'Comment': comment})

                comment_count += 1

                # Check if the maximum comment count is reached
                if comment_count >= 50:
                    return

            # Again repeat
            if 'nextPageToken' in video_response:
                video_response = youtube.commentThreads().list(
                    part='snippet,replies',
                    videoId=video_id,
                    pageToken=video_response['nextPageToken'],
                    maxResults=100  # Adjust the number of comments per page as needed
                ).execute()
            else:
                break

def execution_function(input):
  # Initialize a counter for deleted rows
  deleted_row_count = 0

  video_comments(input)

  # calling the comment file created above
  file_path = "/content/comments.csv"
  df = pd.read_csv(file_path)

  # Rename the column name to 'comments'
  df.rename(columns={'Comment': 'comments'}, inplace=True)

  # Get the first 300 comments for quick analysis
  df = df.head(10)

  return df
  # return_distribution()

# comments_df = execution_function("6ydFDwv-n8w")
# comments_df = comments_df.head(20)

# comments_df.head()

"""##**Fine - tune Llama 2**

IMP: This notebook runs on a T4 GPU.
"""

# !pip install -q accelerate==0.21.0 peft==0.4.0 bitsandbytes==0.40.2 transformers==4.31.0 trl==0.4.7

import os
import torch
from datasets import load_dataset
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    BitsAndBytesConfig,
    HfArgumentParser,
    TrainingArguments,
    pipeline,
    logging,
)
from peft import LoraConfig, PeftModel
from trl import SFTTrainer

# The model that you want to train from the Hugging Face hub
model_name = "NousResearch/Llama-2-7b-chat-hf"

# The instruction dataset to use
# dataset_name = "mlabonne/guanaco-llama2-1k"

# Fine-tuned model name
# new_model = "llama-2-7b-miniguanaco"

################################################################################
# QLoRA parameters
################################################################################

# LoRA attention dimension
lora_r = 64

# Alpha parameter for LoRA scaling
lora_alpha = 16

# Dropout probability for LoRA layers
lora_dropout = 0.1

################################################################################
# bitsandbytes parameters
################################################################################

# Activate 4-bit precision base model loading
use_4bit = True

# Compute dtype for 4-bit base models
bnb_4bit_compute_dtype = "float16"

# Quantization type (fp4 or nf4)
bnb_4bit_quant_type = "nf4"

# Activate nested quantization for 4-bit base models (double quantization)
use_nested_quant = False

################################################################################
# TrainingArguments parameters
################################################################################

# Output directory where the model predictions and checkpoints will be stored
output_dir = "./results"

# Number of training epochs
num_train_epochs = 1

# Enable fp16/bf16 training (set bf16 to True with an A100)
fp16 = False
bf16 = False

# Batch size per GPU for training
per_device_train_batch_size = 4

# Batch size per GPU for evaluation
per_device_eval_batch_size = 4

# Number of update steps to accumulate the gradients for
gradient_accumulation_steps = 1

# Enable gradient checkpointing
gradient_checkpointing = True

# Maximum gradient normal (gradient clipping)
max_grad_norm = 0.3

# Initial learning rate (AdamW optimizer)
learning_rate = 2e-4

# Weight decay to apply to all layers except bias/LayerNorm weights
weight_decay = 0.001

# Optimizer to use
optim = "paged_adamw_32bit"

# Learning rate schedule
lr_scheduler_type = "cosine"

# Number of training steps (overrides num_train_epochs)
max_steps = -1

# Ratio of steps for a linear warmup (from 0 to learning rate)
warmup_ratio = 0.03

# Group sequences into batches with same length
# Saves memory and speeds up training considerably
group_by_length = True

# Save checkpoint every X updates steps
save_steps = 0

# Log every X updates steps
logging_steps = 25

################################################################################
# SFT parameters
################################################################################

# Maximum sequence length to use
max_seq_length = None

# Pack multiple short examples in the same input sequence to increase efficiency
packing = False

# Load the entire model on the GPU 0
device_map = {"": 0}

# Load dataset (you can process it here)
# dataset = load_dataset(dataset_name, split="train")

# Load tokenizer and model with QLoRA configuration
compute_dtype = getattr(torch, bnb_4bit_compute_dtype)

bnb_config = BitsAndBytesConfig(
    load_in_4bit=use_4bit,
    bnb_4bit_quant_type=bnb_4bit_quant_type,
    bnb_4bit_compute_dtype=compute_dtype,
    bnb_4bit_use_double_quant=use_nested_quant,
)

# Check GPU compatibility with bfloat16
if compute_dtype == torch.float16 and use_4bit:
    major, _ = torch.cuda.get_device_capability()
    if major >= 8:
        print("=" * 80)
        print("Your GPU supports bfloat16: accelerate training with bf16=True")
        print("=" * 80)

# Load base model
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    quantization_config=bnb_config,
    device_map=device_map
)
model.config.use_cache = False
model.config.pretraining_tp = 1

# Load LLaMA tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "right" # Fix weird overflow issue with fp16 training

# Load LoRA configuration
peft_config = LoraConfig(
    lora_alpha=lora_alpha,
    lora_dropout=lora_dropout,
    r=lora_r,
    bias="none",
    task_type="CAUSAL_LM",
)

# Set training parameters
training_arguments = TrainingArguments(
    output_dir=output_dir,
    num_train_epochs=num_train_epochs,
    per_device_train_batch_size=per_device_train_batch_size,
    gradient_accumulation_steps=gradient_accumulation_steps,
    optim=optim,
    save_steps=save_steps,
    logging_steps=logging_steps,
    learning_rate=learning_rate,
    weight_decay=weight_decay,
    fp16=fp16,
    bf16=bf16,
    max_grad_norm=max_grad_norm,
    max_steps=max_steps,
    warmup_ratio=warmup_ratio,
    group_by_length=group_by_length,
    lr_scheduler_type=lr_scheduler_type,
    report_to="tensorboard"
)

def extract_between_inst_and_newline(text):
    start_tag = "[/INST]"
    end_char = "\n"

    start_index = text.find(start_tag)

    if start_index != -1:
        end_index = text.find(end_char, start_index)
        if end_index != -1:
            extracted_text = text[start_index + len(start_tag):end_index]
            return extracted_text.strip()

    return None

import re
from functools import lru_cache

@lru_cache
def extract_classification_and_remark(output):
    classification_match = re.search(r'Classification: (.*?)\n', output)
    remark_match = re.search(r'Remark: (.*?)$', output)

    classification = classification_match.group(1) if classification_match else None
    remark = remark_match.group(1) if remark_match else None

    return classification, remark

# Ignore warnings
logging.set_verbosity(logging.CRITICAL)

# Run text generation pipeline with our next model
prompt = '''Can you classify the human input as either happy, sad, angry, surprised, confused or neutral and tell me why it was classified as such in one short sentence.
Don't reply anything besides the classification and the remark. Separate the classificaion and remark with :
Human input: {}'''

def process_comment(comment):
    formatted_prompt = prompt.format(comment)
    pipe = pipeline(task="text2text-generation", model=model, tokenizer=tokenizer, max_length=150)
    result = pipe(f"<s>[INST] {formatted_prompt} [/INST]")
    extract_output = result[0]['generated_text']
    classification, remark = extract_classification_and_remark(extract_output)
    return comment, classification, remark

import matplotlib.pyplot as plt
import seaborn as sns

def return_distribution(new_formatted_df):
  # Assuming your DataFrame is named 'df'
  sentiment_counts = new_formatted_df['classification'].value_counts()
  fig = plt.figure()
  sns.barplot(x=sentiment_counts.index, y=sentiment_counts.values)
  plt.xlabel('Sentiment')
  plt.ylabel('Count')
  plt.title('Sentiment Distribution')
  return fig

from wordcloud import WordCloud

def return_highest_sentiment_worldcloud(new_formatted_df, sentiment):
  # Create a word cloud for a specific sentiment, e.g., 'happy'
  happy_comments = new_formatted_df[new_formatted_df['classification'] == sentiment]['comments']
  wordcloud = WordCloud(width=800, height=400, background_color='white').generate(' '.join(happy_comments))
  fig = plt.figure(figsize=(10, 5))
  plt.imshow(wordcloud, interpolation='bilinear')
  plt.axis('off')
  plt.title('Word Cloud for the Strongest Sentiment')
  return fig

import pandas as pd

def concatenate_remarks_based_on_classification(dataset):

    # Create an empty dictionary to store concatenated remarks for each classification type.
    concatenated_remarks = {}

    # Iterate through the dataset to concatenate remarks.
    for index, row in dataset.iterrows():
        classification = row['classification']
        remarks = row['remark']

        # Check if the classification exists in the dictionary.
        if classification in concatenated_remarks:
            if remarks is not None:
                concatenated_remarks[classification] += ' ' + str(remarks)
        else:
            if remarks is not None:
                concatenated_remarks[classification] = str(remarks)

    # Create a new DataFrame with the concatenated remarks.
    concatenated_remarks_df = pd.DataFrame(list(concatenated_remarks.items()), columns=['classification', 'concatenated_remarks'])

    return concatenated_remarks_df

# !pip install dask -q

# Run text generation pipeline with our next model
prompt1 = '''Can you summarize the following text in a paragraph of no more than 100 words. Don't respond with anything besides the summary.
Human input: {}'''

def summarize_text(comment):
    formatted_prompt = prompt1.format(comment)
    new_pipe = pipeline(task="text2text-generation", model=model, tokenizer=tokenizer, max_length=3000)
    new_result = new_pipe(f"<s>[INST] {formatted_prompt} [/INST]")
    return new_result

## Function for first tab

import numpy as np
from concurrent.futures import ThreadPoolExecutor
import dask.dataframe as dd
from dask.distributed import Client, LocalCluster
# from multiprocessing import Pool
# num_processes = 4


# Import necessary libraries and functions here
# return_df = pd.DataFrame()
# final_analysed_df = pd.DataFrame()  # Initialize as None at the global scope

# Define a Gradio interface
def sentiment_distribution_interface(video_id):
    # global final_analysed_df
    # global unique_classifications


    return_df = pd.DataFrame()
    # Call the execution function with the video_id
    return_df = execution_function(video_id)
    print(return_df.head())

    from concurrent.futures import ThreadPoolExecutor

    def process_row(row):     #3.9s
      comment, classification, remark = process_comment(row.comments)
      return comment, classification, remark

    with ThreadPoolExecutor(max_workers=4) as executor:         # Adjust the number of workers as needed
      results = list(executor.map(process_row, return_df.itertuples()))

    print(type(results))
    print(results)

    print("__________________________________________________________________")

    comments, classification, remark = zip(*results)

    # Create a DataFrame from the separated data
    df = pd.DataFrame({'comments': comments, 'classification': classification, 'remark': remark})

    print(df.head())

    print("__________________________________________________________________")

    plot = return_distribution(df)  # Modify this line to capture the plot

    word_cloud = return_highest_sentiment_worldcloud(df, df['classification'].value_counts().idxmax())

    df.to_csv('processed_comments.csv', index=False)  # index=False prevents writing the row numbers as a column

    #concatinating remarks for different sentiments
    # concatenated_remarks_df = concatenate_remarks_based_on_classification(df)
    # print(concatenated_remarks_df)

    # final_analysed_df = df

    return plot , word_cloud  # Return the plot

# Function for Second Tab

def function_for_second_tab(input_val):

  final_analysed_df = pd.read_csv('processed_comments.csv')
  final_analysed_df = pd.DataFrame(final_analysed_df)
  print(final_analysed_df.head())

  word_cloud  = return_highest_sentiment_worldcloud(final_analysed_df, input_val)

  concatenated_remarks_df = concatenate_remarks_based_on_classification(final_analysed_df)

  comments = concatenated_remarks_df.loc[concatenated_remarks_df['classification'] == 'Happy', 'concatenated_remarks'].values[0]

  summarized_text = summarize_text(comments)

  extract_output_summary = summarized_text[0]['generated_text']

  final_extract = extract_output_summary.split('[/INST]')[1].strip()

  return word_cloud, final_extract

# # Define the first tab
outputs = [gr.Plot(), gr.Plot()]
iface = gr.Interface(fn=sentiment_distribution_interface, inputs="text", outputs=outputs)


# # Define the second tab
output_second_tab = [gr.Plot(), "text"]
inputs = "text"

description = ("Enter the sentiment for which you want a detailed report")
app2 = gr.Interface(fn=function_for_second_tab, inputs=inputs, outputs=output_second_tab, description = description)

# launch the app
demo = gr.TabbedInterface([iface, app2], ["Welcome page", "Visualization page"])

if __name__ == "__main__":
    demo.queue().launch()