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{
"cells": [
{
"cell_type": "code",
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"id": "EpuzgeBWEYWR",
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},
"executionInfo": {
"elapsed": 29790,
"status": "ok",
"timestamp": 1729514059235,
"user": {
"displayName": "Ayush kumar",
"userId": "09471472999674147959"
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"user_tz": -330
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"id": "EpuzgeBWEYWR",
"outputId": "3f0077f2-6be0-498e-c8ea-ea605099759a"
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"outputs": [],
"source": []
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{
"cell_type": "code",
"execution_count": 2,
"id": "522d1bb0-d1df-4811-bc4e-1062e1ee4515",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 609
},
"executionInfo": {
"elapsed": 29742,
"status": "ok",
"timestamp": 1729514088953,
"user": {
"displayName": "Ayush kumar",
"userId": "09471472999674147959"
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"name": "stdout",
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"text": [
"* Running on local URL: http://127.0.0.1:7869\n",
"* Running on public URL: https://c4db01568dfdb26c7a.gradio.live\n",
"\n",
"This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"https://c4db01568dfdb26c7a.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
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"source": [
"!pip install -q gradio\n",
"import warnings\n",
"warnings.filterwarnings('ignore')\n",
"\n",
"# Import necessary libraries\n",
"import gradio as gr\n",
"import torch\n",
"from transformers import (\n",
" BertTokenizerFast,\n",
" BertForQuestionAnswering,\n",
" AutoTokenizer,\n",
" BartForQuestionAnswering,\n",
" DistilBertTokenizerFast,\n",
" DistilBertForQuestionAnswering\n",
")\n",
"import gc\n",
"\n",
"# Create a context store\n",
"context_store = []\n",
"selected_model = None # To track the selected model\n",
"\n",
"# Define models and tokenizers\n",
"def load_bert_model_and_tokenizer():\n",
" device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
" model_save_path = \"LivisLiquoro/BERT_Model_Squad1.1\"\n",
" model = BertForQuestionAnswering.from_pretrained(model_save_path)\n",
" tokenizer = BertTokenizerFast.from_pretrained(model_save_path)\n",
" model.eval().to(device)\n",
" gc.collect()\n",
" torch.cuda.empty_cache()\n",
" return tokenizer, model, device\n",
"\n",
"def load_bart_model_and_tokenizer():\n",
" device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
" model = BartForQuestionAnswering.from_pretrained(\"valhalla/bart-large-finetuned-squadv1\")\n",
" tokenizer = AutoTokenizer.from_pretrained(\"valhalla/bart-large-finetuned-squadv1\")\n",
" model.eval().to(device)\n",
" gc.collect()\n",
" torch.cuda.empty_cache()\n",
" return tokenizer, model, device\n",
"\n",
"def load_distilbert_model_and_tokenizer():\n",
" device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
" model_save_path = \"LivisLiquoro/DistilBert_model_squad1.1\"\n",
" model = DistilBertForQuestionAnswering.from_pretrained(model_save_path)\n",
" tokenizer = DistilBertTokenizerFast.from_pretrained(model_save_path)\n",
" model.eval().to(device)\n",
" gc.collect()\n",
" torch.cuda.empty_cache()\n",
" return tokenizer, model, device\n",
"\n",
"def clean_answer(tokens):\n",
" \"\"\"\n",
" Clean the tokens by removing special tokens like [SEP], [CLS], and fixing token fragments.\n",
" \"\"\"\n",
" cleaned_tokens = []\n",
" for token in tokens:\n",
" if token in ['[SEP]', '[CLS]']:\n",
" continue # Skip special tokens\n",
" token = token.replace('##', '') # Remove '##' prefix\n",
" if token: # Only add non-empty tokens\n",
" cleaned_tokens.append(token)\n",
"\n",
" return tokenizer.convert_tokens_to_string(cleaned_tokens).strip() or None\n",
"\n",
"def generate_answer(context, question):\n",
" max_attempts = 50 # Set maximum attempts for generating answers\n",
" attempts = 0\n",
" best_answer = None\n",
" \n",
" # Adjusting the context chunking method\n",
" max_length = 512\n",
" chunks = [context[i:i + max_length] for i in range(0, len(context), max_length)]\n",
"\n",
" while attempts < max_attempts:\n",
" attempts += 1\n",
" for chunk in chunks:\n",
" inputs = tokenizer(chunk, question, return_tensors='pt', truncation=True, max_length=max_length).to(device)\n",
"\n",
" with torch.no_grad():\n",
" outputs = model(**inputs)\n",
" answer_start = torch.argmax(outputs.start_logits)\n",
" answer_end = torch.argmax(outputs.end_logits) + 1\n",
"\n",
" if answer_start < answer_end:\n",
" answer = clean_answer(tokenizer.convert_ids_to_tokens(inputs['input_ids'][0][answer_start:answer_end]))\n",
"\n",
" # Validate answer and ensure it's direct\n",
" if answer and answer.lower() != \"no valid answer found\":\n",
" best_answer = answer.capitalize()\n",
" break # Exit the chunk loop if a valid answer is found\n",
" if best_answer: # If an answer is found, no need to keep trying\n",
" break\n",
" \n",
" if best_answer: # If a valid answer was found, exit the attempts loop\n",
" break\n",
" \n",
" if best_answer:\n",
" return best_answer\n",
" else:\n",
" return \"❌ No valid answer found.\"\n",
"\n",
"# Define the Gradio interface with light theme and organized layout\n",
"def chatbot_interface():\n",
" with gr.Blocks() as demo:\n",
" # Custom CSS for light theme and layout\n",
" gr.Markdown(\"\"\"\n",
" <style>\n",
" body { background-color: #f9f9f9; }\n",
" .chatbot-container { background-color: #ffffff; border-radius: 10px; padding: 20px; color: #333; font-family: Arial, sans-serif; }\n",
" .gr-button { background-color: #4CAF50; color: white; border: none; border-radius: 5px; padding: 10px 20px; font-size: 14px; cursor: pointer; }\n",
" .gr-button:hover { background-color: #45a049; }\n",
" .gr-textbox { background-color: #ffffff; color: #333; border-radius: 5px; border: 1px solid #ddd; padding: 10px; }\n",
" .gr-chatbot { background-color: #e6e6e6; border-radius: 10px; padding: 15px; color: #333; }\n",
" .footer { text-align: right; font-size: 12px; color: #777; font-style: italic; }\n",
" .note { text-align: right; font-size: 10px; color: #777; font-style: italic; position: absolute; bottom: 10px; right: 10px; }\n",
" </style>\n",
" \"\"\")\n",
"\n",
" # Header\n",
" gr.Markdown(\"<h1 style='text-align: center; color: #4CAF50;'>EDITH: Multi-Model Question Answering Platform</h1>\")\n",
" gr.Markdown(\"<p style='text-align: center; color: #777;'>Switch between BERT, BART, and DistilBERT models and ask questions based on the context.</p>\")\n",
"\n",
" context_state = gr.State()\n",
" model_choice_state = gr.State(value=\"BERT\") # Default model is BERT\n",
"\n",
" with gr.Row():\n",
" with gr.Column(scale=11): # Left panel for chatbot and question input (45%)\n",
" chatbot = gr.Chatbot(label=\"Chatbot\")\n",
" question_input = gr.Textbox(label=\"Ask a Question\", placeholder=\"Enter your question here...\", lines=1)\n",
" submit_btn = gr.Button(\"Submit Question\")\n",
"\n",
" with gr.Column(scale=9): # Right panel for setting context and instructions (55%)\n",
" context_input = gr.Textbox(label=\"Set Context\", placeholder=\"Enter the context here...\", lines=4)\n",
" set_context_btn = gr.Button(\"Set Context\")\n",
" clear_context_btn = gr.Button(\"Clear Context\")\n",
"\n",
" # Model selection buttons\n",
" model_selection = gr.Radio(choices=[\"BERT\", \"BART\", \"DistilBERT\"], label=\"Select Model\", value=\"BERT\")\n",
" status_message = gr.Markdown(\"\")\n",
"\n",
" gr.Markdown(\"<strong>Instructions:</strong><br>1. Set a context.<br>2. Select the model (BERT, BART, or DistilBERT).<br>3. Ask questions based on the context.<br><br><strong>Note:</strong> <span class='note'>The BART model is pre-trained from Hugging Face. Credits to Hugging Face and the person who fine-tuned this model ('valhalla/bart-large-finetuned-squadv1')</span>\")\n",
"\n",
" footer = gr.Markdown(\"<div class='footer'>Prepared by: Team EDITH</div>\")\n",
"\n",
" def set_context(context):\n",
" if not context.strip():\n",
" return gr.update(), \"Please enter a valid context.\", None\n",
" return gr.update(visible=False), \"Context has been set. You can now ask questions.\", context\n",
"\n",
" def clear_context():\n",
" return gr.update(visible=True), \"Context has been cleared. Please set a new context.\", None\n",
"\n",
" def handle_question(question, history, context, model_choice):\n",
" global tokenizer, model, device\n",
"\n",
" if not context:\n",
" return history, \"Please set the context before asking questions.\"\n",
" if not question.strip():\n",
" return history, \"Please enter a valid question.\"\n",
"\n",
" # Load the selected model and tokenizer\n",
" if model_choice == \"BERT\":\n",
" tokenizer, model, device = load_bert_model_and_tokenizer()\n",
" model_name = \"BERT\"\n",
" elif model_choice == \"BART\":\n",
" tokenizer, model, device = load_bart_model_and_tokenizer()\n",
" model_name = \"BART\"\n",
" elif model_choice == \"DistilBERT\":\n",
" tokenizer, model, device = load_distilbert_model_and_tokenizer()\n",
" model_name = \"DistilBERT\"\n",
"\n",
" answer = generate_answer(context, question)\n",
" history = history + [[f\"👤: {question}\", f\"🤖 ({model_name}): {answer}\"]] # Show the selected model with the answer\n",
" return history, \"\"\n",
"\n",
" set_context_btn.click(set_context, inputs=context_input, outputs=[context_input, status_message, context_state])\n",
" clear_context_btn.click(clear_context, inputs=None, outputs=[context_input, status_message, context_state])\n",
" submit_btn.click(handle_question, inputs=[question_input, chatbot, context_state, model_selection], outputs=[chatbot, question_input])\n",
"\n",
" # Enable \"Enter\" key to trigger the \"Submit\" button\n",
" question_input.submit(handle_question, inputs=[question_input, chatbot, context_state, model_selection], outputs=[chatbot, question_input])\n",
"\n",
" return demo\n",
"\n",
"# Run the Gradio interface\n",
"interface = chatbot_interface()\n",
"interface.launch(share=True)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ed897183",
"metadata": {
"id": "ed897183"
},
"outputs": [],
"source": [
"|"
]
}
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