Update app.py

app.py

@@ -1,125 +1,158 @@
-import os
-from rank_bm25 import BM25Okapi
-from transformers import pipeline, AutoTokenizer, AutoModelForQuestionAnswering
-import torch
-import gradio as gr
-from docx import Document
-import pdfplumber
-
-# Load the fine-tuned BERT-based QA model and tokenizer
-model_name = "IProject-10/roberta-base-finetuned-squad2"  # Replace with your model name
-qa_model = AutoModelForQuestionAnswering.from_pretrained(model_name)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-# Set up the device for BERT
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-qa_model.to(device)
-qa_model.eval()
-
-# Create a pipeline for retrieval-augmented QA
-retrieval_qa_pipeline = pipeline(
-    "question-answering",
-    model=qa_model,
-    tokenizer=tokenizer,
-    device=device.index if torch.cuda.is_available() else -1
-)
-
-def extract_text_from_file(file):
-    # Determine the file extension
-    file_extension = os.path.splitext(file.name)[1].lower()
-    text = ""
-
-    try:
-        if file_extension == ".txt":
-            with open(file.name, "r") as f:
-                text = f.read()
-        elif file_extension == ".docx":
-            doc = Document(file.name)
-            for para in doc.paragraphs:
-                text += para.text + "\n"
-        elif file_extension == ".pdf":
-            with pdfplumber.open(file.name) as pdf:
-                for page in pdf.pages:
-                    text += page.extract_text() + "\n"
-        else:
-            raise ValueError("Unsupported file format: {}".format(file_extension))
-    except Exception as e:
-        text = str(e)
-    return text
-
-def load_passages(files):
-    passages = []
-    for file in files:
-        passage = extract_text_from_file(file)
-        passages.append(passage)
-    return passages
-
-def highlight_answer(context, answer):
-    start_index = context.find(answer)
-    if start_index != -1:
-        end_index = start_index + len(answer)
-        highlighted_context = f"{context[:start_index]}_________<<{context[start_index:end_index]}>>_________{context[end_index:]}"
-        return highlighted_context
-    else:
-        return context
-
-def answer_question(question, files):
-    try:
-        # Load passages from the uploaded files
-        passages = load_passages(files)
-
-        # Create an index using BM25
-        bm25 = BM25Okapi([passage.split() for passage in passages])
-
-        # Retrieve relevant passages using BM25
-        tokenized_query = question.split()
-        candidate_passages = bm25.get_top_n(tokenized_query, passages, n=3)
-        bm25_scores = bm25.get_scores(tokenized_query)
-
-        # Extract answer using the pipeline for each candidate passage
-        answers_with_context = []
-        for passage in candidate_passages:
-            answer = retrieval_qa_pipeline(question=question, context=passage)
-            bm25_score = bm25_scores[passages.index(passage)]
-            answer_with_context = {
-                "context": passage,
-                "answer": answer["answer"],
-                "BM25-score": bm25_score  # BM25 confidence score for this passage
-            }
-            answers_with_context.append(answer_with_context)
-
-        # Choose the answer with the highest model confidence score
-        best_answer = max(answers_with_context, key=lambda x: x["BM25-score"])
-
-        # Highlight the answer in the context
-        highlighted_context = highlight_answer(best_answer["context"], best_answer["answer"])
-
-        return best_answer["answer"], highlighted_context, best_answer["BM25-score"]
-    except Exception as e:
-        return str(e), "", ""
-
-# Define Gradio interface
-iface = gr.Interface(
-    fn=answer_question,
-    inputs=[
-        gr.Textbox(lines=2, placeholder="Enter your question here...", label="Question"),
-        gr.Files(label="Upload text, Word, or PDF files")
-    ],
-    outputs=[
-        gr.Textbox(label="Answer"),
-        gr.Textbox(label="Context"),
-        gr.Textbox(label="BM25 Score")
-    ],
-    title="Question Answering Model",
-    description="Upload a text document and ask a question from the content",
-    css="""
-    .container { max-width: 800px; margin: auto; }
-    .interface-title { font-family: Arial, sans-serif; font-size: 24px; font-weight: bold; }
-    .interface-description { font-family: Arial, sans-serif; font-size: 16px; margin-bottom: 20px; }
-    .input-textbox, .output-textbox { font-family: Arial, sans-serif; font-size: 14px; }
-    .error { color: red; font-family: Arial, sans-serif; font-size: 14px; }
-    """
-)
-
-# Launch the interface
-iface.launch()
+import os
+from rank_bm25 import BM25Okapi
+from transformers import pipeline, AutoTokenizer, AutoModelForQuestionAnswering
+import torch
+import gradio as gr
+from docx import Document
+import pdfplumber
+
+# Load the fine-tuned BERT-based QA model and tokenizer
+model_name = "IProject-10/roberta-base-finetuned-squad2"  # Replace with your model name
+qa_model = AutoModelForQuestionAnswering.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Set up the device for BERT
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+qa_model.to(device)
+qa_model.eval()
+
+# Create a pipeline for retrieval-augmented QA
+retrieval_qa_pipeline = pipeline(
+    "question-answering",
+    model=qa_model,
+    tokenizer=tokenizer,
+    device=device.index if torch.cuda.is_available() else -1
+)
+
+def extract_text_from_file(file):
+    # Determine the file extension
+    file_extension = os.path.splitext(file.name)[1].lower()
+    text = ""
+
+    try:
+        if file_extension == ".txt":
+            with open(file.name, "r") as f:
+                text = f.read()
+        elif file_extension == ".docx":
+            doc = Document(file.name)
+            for para in doc.paragraphs:
+                text += para.text + "\n"
+        elif file_extension == ".pdf":
+            with pdfplumber.open(file.name) as pdf:
+                for page in pdf.pages:
+                    text += page.extract_text() + "\n"
+        else:
+            raise ValueError("Unsupported file format: {}".format(file_extension))
+    except Exception as e:
+        text = str(e)
+    return text
+
+def load_passages(files):
+    passages = []
+    for file in files:
+        passage = extract_text_from_file(file)
+        passages.append(passage)
+    return passages
+
+def highlight_answer(context, answer):
+    start_index = context.find(answer)
+    if start_index != -1:
+        end_index = start_index + len(answer)
+        highlighted_context = f"{context[:start_index]}_________<<{context[start_index:end_index]}>>_________{context[end_index:]}"
+        return highlighted_context
+    else:
+        return context
+
+def answer_question(question, files):
+    try:
+        # Load passages from the uploaded files
+        passages = load_passages(files)
+
+        # Create an index using BM25
+        bm25 = BM25Okapi([passage.split() for passage in passages])
+
+        # Retrieve relevant passages using BM25
+        tokenized_query = question.split()
+        candidate_passages = bm25.get_top_n(tokenized_query, passages, n=3)
+        bm25_scores = bm25.get_scores(tokenized_query)
+
+        # Extract answer using the pipeline for each candidate passage
+        answers_with_context = []
+        for passage in candidate_passages:
+            answer = retrieval_qa_pipeline(question=question, context=passage)
+            bm25_score = bm25_scores[passages.index(passage)]
+            answer_with_context = {
+                "context": passage,
+                "answer": answer["answer"],
+                "BM25-score": bm25_score  # BM25 confidence score for this passage
+            }
+            answers_with_context.append(answer_with_context)
+
+        # Choose the answer with the highest model confidence score
+        best_answer = max(answers_with_context, key=lambda x: x["BM25-score"])
+
+        # Highlight the answer in the context
+        highlighted_context = highlight_answer(best_answer["context"], best_answer["answer"])
+
+        return best_answer["answer"], highlighted_context, best_answer["BM25-score"]
+    except Exception as e:
+        return str(e), "", ""
+
+# Description
+md = """
+### Brief Overview of the project:
+
+A Document-Retrieval QA application built by training **[RoBERTa model](https://arxiv.org/pdf/1907.11692)** on **[SQuAD 2.0](https://rajpurkar.github.io/SQuAD-explorer/)** dataset for efficient answer extraction and
+the system is augmented by using NLP based **[BM25](https://www.researchgate.net/publication/220613776_The_Probabilistic_Relevance_Framework_BM25_and_Beyond)** retriever for information retrieval from a large text corpus.
+The project is a brief enhancement and augmentation to the work done in the research paper **[Encoder-based LLMs: Building QA systems and Comparative Analysis](https://drive.google.com/file/d/1Ztd6x46g21ufoewmKZMoElMxViNfd_2P/view?usp=sharing)**.
+In this paper we study about BERT and its advanced variants and learn to build an efficient answer extraction QA system from scratch.
+The built system can be used in information retrieval system and search engines.
+
+**Objectives of the projects:**
+1. Build a simple Answer Extraction QA system using **RoBERTa-base**: The project is deployed public url objective1.
+2. Building a Information Retrieval system for data augmentation using **BM25**
+3. **Document Retrieval QA** system by merging Answer Extraction QA system and Information retrieval system
+
+### Demonstrating working of the Application:
+
+<div style="text-align: center;">
+    <img src="https://i.imgur.com/oYg8y7N.jpeg" alt="Description Image" style="border: 2px solid #000; border-radius: 5px; width: 600px; height: auto; display: block; margin: 0 auto;">
+</div>
+
+**Key Features:**
+- Fine-tuned **RoBERTa**- Performs **Answer Extraction** from the retrieved document
+- **BM25** Retriever- Performs **Information Retrieval** from the text corpus
+- Provides answers with **highlighted context**.
+- Application displays accurate **answer**, most relevant document **context** and the corresponding **BM25 score** of the passage to the user
+
+**How to Use:**
+1. Upload your corpus document(s).
+2. Enter your question in the text box followed by a question mark(?).
+3. Get the answer with context and corresponding BM25 scores.
+"""
+
+# Define Gradio interface
+iface = gr.Interface(
+    fn=answer_question,
+    inputs=[
+        gr.Textbox(lines=2, placeholder="Enter your question here...", label="Question"),
+        gr.Files(label="Upload text, Word, or PDF files")
+    ],
+    outputs=[
+        gr.Textbox(label="Answer"),
+        gr.Textbox(label="Context"),
+        gr.Textbox(label="BM25 Score")
+    ],
+    title="Document Retrieval Question Answering Application",
+    description=md,
+    css="""
+    .container { max-width: 800px; margin: auto; }
+    .interface-title { font-family: Arial, sans-serif; font-size: 24px; font-weight: bold; }
+    .interface-description { font-family: Arial, sans-serif; font-size: 16px; margin-bottom: 20px; }
+    .input-textbox, .output-textbox { font-family: Arial, sans-serif; font-size: 14px; }
+    .error { color: red; font-family: Arial, sans-serif; font-size: 14px; }
+    """
+)
+
+# Launch the interface
+iface.launch()