Update app.py

app.py

@@ -1,446 +1,446 @@
-
-
-
-import os
-import streamlit as st
-import numpy as np
-import fitz  # PyMuPDF
-from ultralytics import YOLO
-from sklearn.cluster import KMeans
-from sklearn.metrics.pairwise import cosine_similarity
-from langchain_core.output_parsers import StrOutputParser
-from langchain_community.document_loaders import PyMuPDFLoader
-from langchain_openai import OpenAIEmbeddings
-from langchain_text_splitters import RecursiveCharacterTextSplitter
-from langchain_core.prompts import ChatPromptTemplate
-from sklearn.decomposition import PCA
-from langchain_openai import ChatOpenAI
-import string
-import re
-from termcolor import colored
-
-# Load the trained model
-model = YOLO("runs\\detect\\train7\\weights\\best.pt")
-openai_api_key = os.environ.get("openai_api_key")
-
-# Define the class indices for figures, tables, and text
-figure_class_index = 4  # class index for figures
-table_class_index = 3   # class index for tables
-
-# Global variables to store embeddings and contents
-global_embeddings = None
-global_split_contents = None
-
-def clean_text(text):
-    text = re.sub(r'\s+', ' ', text).strip()
-    return text
-
-def remove_references(text):
-    reference_patterns = [
-        r'\bReferences\b', r'\breferences\b', r'\bBibliography\b', r'\bCitations\b',
-        r'\bWorks Cited\b', r'\bReference\b', r'\breference\b'
-    ]
-    lines = text.split('\n')
-    for i, line in enumerate(lines):
-        if any(re.search(pattern, line, re.IGNORECASE) for pattern in reference_patterns):
-            return '\n'.join(lines[:i])
-    return text
-
-def save_uploaded_file(uploaded_file):
-    with open(uploaded_file.name, 'wb') as f:
-        f.write(uploaded_file.getbuffer())
-    return uploaded_file.name
-
-def summarize_pdf(pdf_file_path, num_clusters=10):
-    embeddings_model = OpenAIEmbeddings(model="text-embedding-3-small", api_key=openai_api_key)
-    llm = ChatOpenAI(model="gpt-3.5-turbo", api_key=openai_api_key, temperature=0.3)
-    prompt = ChatPromptTemplate.from_template(
-        """Could you please provide a concise and comprehensive summary of the given Contexts? 
-        The summary should capture the main points and key details of the text while conveying the author's intended meaning accurately. 
-        Please ensure that the summary is well-organized and easy to read, with clear headings and subheadings to guide the reader through each section. 
-        The length of the summary should be appropriate to capture the main points and key details of the text, without including unnecessary information or becoming overly long. 
-        example of summary:
-        ## Summary:
-        ## Key points:
-        Contexts: {topic}"""
-    )
-    output_parser = StrOutputParser()
-    chain = prompt | llm | output_parser
-
-    loader = PyMuPDFLoader(pdf_file_path)
-    docs = loader.load()
-    full_text = "\n".join(doc.page_content for doc in docs)
-    cleaned_full_text = remove_references(full_text)
-    cleaned_full_text = clean_text(cleaned_full_text)
-
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0,separators=["\n\n", "\n",".", " "])
-    split_contents = text_splitter.split_text(cleaned_full_text)
-    embeddings = embeddings_model.embed_documents(split_contents)
-
-    X = np.array(embeddings)
-    kmeans = KMeans(n_clusters=num_clusters, init='k-means++', random_state=0).fit(embeddings)
-    cluster_centers = kmeans.cluster_centers_
-
-    closest_point_indices = []
-    for center in cluster_centers:
-        distances = np.linalg.norm(embeddings - center, axis=1)
-        closest_point_indices.append(np.argmin(distances))
-    
-    extracted_contents = [split_contents[idx] for idx in closest_point_indices]
-    results = chain.invoke({"topic": ' '.join(extracted_contents)})
-
-    summary_sentences = re.split(r'(?<!\w\.\w.)(?<![A-Z][a-z]\.)(?<=\.|\?)\s', results)
-    summary_embeddings = embeddings_model.embed_documents(summary_sentences)
-    extracted_embeddings = embeddings_model.embed_documents(extracted_contents)
-    similarity_matrix = cosine_similarity(summary_embeddings, extracted_embeddings)
-
-    cited_results = results
-    relevant_sources = []
-    source_mapping = {}
-    sentence_to_source = {}
-    similarity_threshold = 0.6
-
-    for i, sentence in enumerate(summary_sentences):
-        if sentence in sentence_to_source:
-            continue
-        max_similarity = max(similarity_matrix[i])
-        if max_similarity >= similarity_threshold:
-            most_similar_idx = np.argmax(similarity_matrix[i])
-            if most_similar_idx not in source_mapping:
-                source_mapping[most_similar_idx] = len(relevant_sources) + 1
-                relevant_sources.append((most_similar_idx, extracted_contents[most_similar_idx]))
-            citation_idx = source_mapping[most_similar_idx]
-            citation = f"([Source {citation_idx}](#source-{citation_idx}))"
-            cited_sentence = re.sub(r'([.!?])$', f" {citation}\\1", sentence)
-            sentence_to_source[sentence] = citation_idx
-            cited_results = cited_results.replace(sentence, cited_sentence)
-    
-    sources_list = "\n\n## Sources:\n"
-    for idx, (original_idx, content) in enumerate(relevant_sources):
-        sources_list += f"""
-<details style="margin: 10px 0; padding: 10px; border: 1px solid #ccc; border-radius: 5px; background-color: #f9f9f9;">
-<summary style="font-weight: bold; cursor: pointer;">Source {idx + 1}</summary>
-<pre style="white-space: pre-wrap; word-wrap: break-word; margin-top: 10px;">{content}</pre>
-</details>
-"""
-    cited_results += sources_list
-    return cited_results
-
-def qa_pdf(pdf_file_path, query, num_clusters=5, similarity_threshold=0.6):
-    global global_embeddings, global_split_contents
-
-    # Initialize models and embeddings
-    embeddings_model = OpenAIEmbeddings(model="text-embedding-3-small", api_key=openai_api_key)
-    llm = ChatOpenAI(model="gpt-3.5-turbo", api_key=openai_api_key, temperature=0.3)
-    prompt = ChatPromptTemplate.from_template(
-        """Please provide a detailed and accurate answer to the given question based on the provided contexts. 
-        Ensure that the answer is comprehensive and directly addresses the query. 
-        If necessary, include relevant examples or details from the text.
-        Question: {question}
-        Contexts: {contexts}"""
-    )
-    output_parser = StrOutputParser()
-    chain = prompt | llm | output_parser
-
-    # Load and process the PDF if not already loaded
-    if global_embeddings is None or global_split_contents is None:
-        loader = PyMuPDFLoader(pdf_file_path)
-        docs = loader.load()
-        full_text = "\n".join(doc.page_content for doc in docs)
-        cleaned_full_text = remove_references(full_text)
-        cleaned_full_text = clean_text(cleaned_full_text)
-
-        text_splitter = RecursiveCharacterTextSplitter(chunk_size=800, chunk_overlap=0, separators=["\n\n", "\n", ".", " "])
-        global_split_contents = text_splitter.split_text(cleaned_full_text)
-        global_embeddings = embeddings_model.embed_documents(global_split_contents)
-
-    # Embed the query and find the most relevant contexts
-    query_embedding = embeddings_model.embed_query(query)
-    similarity_scores = cosine_similarity([query_embedding], global_embeddings)[0]
-    top_indices = np.argsort(similarity_scores)[-num_clusters:]
-    relevant_contents = [global_split_contents[i] for i in top_indices]
-
-    # Generate the answer using the LLM chain
-    results = chain.invoke({"question": query, "contexts": ' '.join(relevant_contents)})
-
-    # Split the answer into sentences and embed them
-    answer_sentences = re.split(r'(?<!\w\.\w.)(?<![A-Z][a-z]\.)(?<=\.|\?)\s', results)
-    answer_embeddings = embeddings_model.embed_documents(answer_sentences)
-    relevant_embeddings = embeddings_model.embed_documents(relevant_contents)
-    similarity_matrix = cosine_similarity(answer_embeddings, relevant_embeddings)
-
-    # Map sentences to sources and create citations
-    cited_results = results
-    relevant_sources = []
-    source_mapping = {}
-    sentence_to_source = {}
-
-    for i, sentence in enumerate(answer_sentences):
-        if sentence in sentence_to_source:
-            continue
-        max_similarity = max(similarity_matrix[i])
-        if max_similarity >= similarity_threshold:
-            most_similar_idx = np.argmax(similarity_matrix[i])
-            if most_similar_idx not in source_mapping:
-                source_mapping[most_similar_idx] = len(relevant_sources) + 1
-                relevant_sources.append((most_similar_idx, relevant_contents[most_similar_idx]))
-            citation_idx = source_mapping[most_similar_idx]
-            citation = f"<strong style='color:blue;'>[Source {citation_idx}]</strong>"            
-            cited_sentence = re.sub(r'([.!?])$', f" {citation}\\1", sentence)
-            sentence_to_source[sentence] = citation_idx
-            cited_results = cited_results.replace(sentence, cited_sentence)
-
-    # Format the sources for markdown rendering
-    sources_list = "\n\n## Sources:\n"
-    for idx, (original_idx, content) in enumerate(relevant_sources):
-        sources_list += f"""
-<details style="margin: 10px 0; padding: 10px; border: 1px solid #ccc; border-radius: 5px; background-color: #f9f9f9;">
-<summary style="font-weight: bold; cursor: pointer;">Source {idx + 1}</summary>
-<pre style="white-space: pre-wrap; word-wrap: break-word; margin-top: 10px;">{content}</pre>
-</details>
-"""
-    cited_results += sources_list
-    return cited_results
-
-
-def infer_image_and_get_boxes(image, confidence_threshold=0.6):
-    results = model.predict(image)
-    boxes = [
-        (int(box.xyxy[0][0]), int(box.xyxy[0][1]), int(box.xyxy[0][2]), int(box.xyxy[0][3]), int(box.cls[0]))
-        for result in results for box in result.boxes
-        if int(box.cls[0]) in {figure_class_index, table_class_index} and box.conf[0] > confidence_threshold
-    ]
-    return boxes
-
-def crop_images_from_boxes(image, boxes, scale_factor):
-    figures = []
-    tables = []
-    for (x1, y1, x2, y2, cls) in boxes:
-        cropped_img = image[int(y1 * scale_factor):int(y2 * scale_factor), int(x1 * scale_factor):int(x2 * scale_factor)]
-        if cls == figure_class_index:
-            figures.append(cropped_img)
-        elif cls == table_class_index:
-            tables.append(cropped_img)
-    return figures, tables
-
-
-def process_pdf(pdf_file_path):
-    doc = fitz.open(pdf_file_path)
-    all_figures = []
-    all_tables = []
-    low_dpi = 50
-    high_dpi = 300
-    scale_factor = high_dpi / low_dpi
-    low_res_pixmaps = [page.get_pixmap(dpi=low_dpi) for page in doc]
-    
-    for page_num, low_res_pix in enumerate(low_res_pixmaps):
-        low_res_img = np.frombuffer(low_res_pix.samples, dtype=np.uint8).reshape(low_res_pix.height, low_res_pix.width, 3)
-        boxes = infer_image_and_get_boxes(low_res_img)
-        
-        if boxes:
-            high_res_pix = doc[page_num].get_pixmap(dpi=high_dpi)
-            high_res_img = np.frombuffer(high_res_pix.samples, dtype=np.uint8).reshape(high_res_pix.height, high_res_pix.width, 3)
-            figures, tables = crop_images_from_boxes(high_res_img, boxes, scale_factor)
-            all_figures.extend(figures)
-            all_tables.extend(tables)
-    
-    return all_figures, all_tables
-
-# Set the page configuration for a modern look
-
-# Set the page configuration for a modern look
-# Set the page configuration for a modern look
-st.set_page_config(page_title="PDF Reading Assistant", page_icon="📄", layout="wide")
-
-# Add some custom CSS for a modern look
-st.markdown("""
-    <style>
-        /* Main background and padding */
-        .main {
-            background-color: #f8f9fa;
-            padding: 2rem;
-            font-family: 'Arial', sans-serif;
-        }
-        
-        /* Section headers */
-        .section-header {
-            font-size: 2rem;
-            font-weight: bold;
-            color: #343a40;
-            margin-top: 2rem;
-            margin-bottom: 1rem;
-            text-align: center;
-            font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
-        }
-
-        /* Containers */
-        .uploaded-file-container, .chat-container, .summary-container, .extract-container {
-            padding: 2rem;
-            background-color: #ffffff;
-            border-radius: 10px;
-            margin-bottom: 2rem;
-            box-shadow: 0 4px 12px rgba(0, 0, 0, 0.1);
-        }
-
-        /* Buttons */
-        .stButton>button {
-            background-color: #007bff;
-            color: white;
-            padding: 0.6rem 1.2rem;
-            border-radius: 5px;
-            border: none;
-            cursor: pointer;
-            font-size: 1rem;
-            transition: background-color 0.3s ease, transform 0.3s ease;
-        }
-        .stButton>button:hover {
-            background-color: #0056b3;
-            transform: translateY(-2px);
-        }
-
-        /* Chat messages */
-        .chat-message {
-            padding: 1rem;
-            border-radius: 10px;
-            margin-bottom: 1rem;
-            font-size: 1rem;
-            transition: all 0.3s ease;
-            box-shadow: 0 2px 8px rgba(0, 0, 0, 0.1);
-        }
-        .chat-message.user {
-            background-color: #e6f7ff;
-            border-left: 5px solid #007bff;
-            text-align: left;
-        }
-        .chat-message.bot {
-            background-color: #fff0f1;
-            border-left: 5px solid #dc3545;
-            text-align: left;
-        }
-
-        /* Input area */
-        .input-container {
-            display: flex;
-            align-items: center;
-            gap: 10px;
-            margin-top: 1rem;
-        }
-        .input-container textarea {
-            border: 2px solid #ccc;
-            border-radius: 10px;
-            padding: 10px;
-            width: 100%;
-            background-color: #fff;
-            transition: border-color 0.3s ease;
-            margin: 0;
-            font-size: 1rem;
-        }
-        .input-container textarea:focus {
-            border-color: #007bff;
-            outline: none;
-        }
-        .input-container button {
-            background-color: #007bff;
-            color: white;
-            padding: 0.6rem 1.2rem;
-            border-radius: 5px;
-            border: none;
-            cursor: pointer;
-            font-size: 1rem;
-            transition: background-color 0.3s ease, transform 0.3s ease;
-        }
-        .input-container button:hover {
-            background-color: #0056b3;
-            transform: translateY(-2px);
-        }
-
-        /* Expander */
-        .st-expander {
-            border: none;
-            box-shadow: 0 4px 12px rgba(0, 0, 0, 0.1);
-            margin-bottom: 2rem;
-        }
-
-        /* Markdown elements */
-        .stMarkdown {
-            font-size: 1rem;
-            color: #343a40;
-            line-height: 1.6;
-        }
-        
-        /* Titles and subtitles */
-        .stTitle {
-            color: #343a40;
-            text-align: center;
-            margin-bottom: 1rem;
-            font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
-        }
-        .stSubtitle {
-            color: #6c757d;
-            text-align: center;
-            margin-bottom: 1rem;
-            font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
-        }
-    </style>
-""", unsafe_allow_html=True)
-
-# Streamlit interface
-st.title("📄 PDF Reading Assistant")
-st.markdown("### Extract tables, figures, summaries, and answers from your PDF files easily.")
-
-uploaded_file = st.file_uploader("Upload a PDF", type="pdf")
-if uploaded_file:
-    file_path = save_uploaded_file(uploaded_file)
-    with st.container():
-        st.markdown("<div class='section-header'>Extract Tables and Figures</div>", unsafe_allow_html=True)
-        with st.expander("Click to Extract Tables and Figures", expanded=True):
-            with st.container():
-                extract_button = st.button("Extract")
-                if extract_button:
-                    figures, tables = process_pdf(file_path)
-                    col1, col2 = st.columns(2)
-                    with col1:
-                        st.write("### Figures")
-                        if figures:
-                            for figure in figures:
-                                st.image(figure, use_column_width=True)
-                        else:
-                            st.write("No figures found.")
-                    with col2:
-                        st.write("### Tables")
-                        if tables:
-                            for table in tables:
-                                st.image(table, use_column_width=True)
-                        else:
-                            st.write("No tables found.")
-    
-    with st.container():
-        st.markdown("<div class='section-header'>Get Summary</div>", unsafe_allow_html=True)
-        with st.expander("Click to Generate Summary", expanded=True):
-            with st.container():
-                summary_button = st.button("Generate Summary")
-                if summary_button:
-                    summary = summarize_pdf(file_path)
-                    st.markdown(summary, unsafe_allow_html=True)
-
-    with st.container():
-        st.markdown("<div class='section-header'>Chat with your PDF</div>", unsafe_allow_html=True)
-        st.write("### Chat with your PDF")
-        if 'chat_history' not in st.session_state:
-            st.session_state['chat_history'] = []
-
-        for chat in st.session_state['chat_history']:
-            chat_user_class = "user" if chat["user"] else ""
-            chat_bot_class = "bot" if chat["bot"] else ""
-            st.markdown(f"<div class='chat-message {chat_user_class}'>{chat['user']}</div>", unsafe_allow_html=True)
-            st.markdown(f"<div class='chat-message {chat_bot_class}'>{chat['bot']}</div>", unsafe_allow_html=True)
-
-        with st.form(key="chat_form", clear_on_submit=True):
-            user_input = st.text_area("Ask a question about the PDF:", key="user_input")
-            submit_button = st.form_submit_button(label="Send")
-
-            if submit_button and user_input:
-                st.session_state['chat_history'].append({"user": user_input, "bot": None})
-                answer = qa_pdf(file_path, user_input)
-                st.session_state['chat_history'][-1]["bot"] = answer
+
+
+
+import os
+import streamlit as st
+import numpy as np
+import fitz  # PyMuPDF
+from ultralytics import YOLO
+from sklearn.cluster import KMeans
+from sklearn.metrics.pairwise import cosine_similarity
+from langchain_core.output_parsers import StrOutputParser
+from langchain_community.document_loaders import PyMuPDFLoader
+from langchain_openai import OpenAIEmbeddings
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_core.prompts import ChatPromptTemplate
+from sklearn.decomposition import PCA
+from langchain_openai import ChatOpenAI
+import string
+import re
+
+
+# Load the trained model
+model = YOLO("runs\\detect\\train7\\weights\\best.pt")
+openai_api_key = os.environ.get("openai_api_key")
+
+# Define the class indices for figures, tables, and text
+figure_class_index = 4  # class index for figures
+table_class_index = 3   # class index for tables
+
+# Global variables to store embeddings and contents
+global_embeddings = None
+global_split_contents = None
+
+def clean_text(text):
+    text = re.sub(r'\s+', ' ', text).strip()
+    return text
+
+def remove_references(text):
+    reference_patterns = [
+        r'\bReferences\b', r'\breferences\b', r'\bBibliography\b', r'\bCitations\b',
+        r'\bWorks Cited\b', r'\bReference\b', r'\breference\b'
+    ]
+    lines = text.split('\n')
+    for i, line in enumerate(lines):
+        if any(re.search(pattern, line, re.IGNORECASE) for pattern in reference_patterns):
+            return '\n'.join(lines[:i])
+    return text
+
+def save_uploaded_file(uploaded_file):
+    with open(uploaded_file.name, 'wb') as f:
+        f.write(uploaded_file.getbuffer())
+    return uploaded_file.name
+
+def summarize_pdf(pdf_file_path, num_clusters=10):
+    embeddings_model = OpenAIEmbeddings(model="text-embedding-3-small", api_key=openai_api_key)
+    llm = ChatOpenAI(model="gpt-3.5-turbo", api_key=openai_api_key, temperature=0.3)
+    prompt = ChatPromptTemplate.from_template(
+        """Could you please provide a concise and comprehensive summary of the given Contexts? 
+        The summary should capture the main points and key details of the text while conveying the author's intended meaning accurately. 
+        Please ensure that the summary is well-organized and easy to read, with clear headings and subheadings to guide the reader through each section. 
+        The length of the summary should be appropriate to capture the main points and key details of the text, without including unnecessary information or becoming overly long. 
+        example of summary:
+        ## Summary:
+        ## Key points:
+        Contexts: {topic}"""
+    )
+    output_parser = StrOutputParser()
+    chain = prompt | llm | output_parser
+
+    loader = PyMuPDFLoader(pdf_file_path)
+    docs = loader.load()
+    full_text = "\n".join(doc.page_content for doc in docs)
+    cleaned_full_text = remove_references(full_text)
+    cleaned_full_text = clean_text(cleaned_full_text)
+
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0,separators=["\n\n", "\n",".", " "])
+    split_contents = text_splitter.split_text(cleaned_full_text)
+    embeddings = embeddings_model.embed_documents(split_contents)
+
+    X = np.array(embeddings)
+    kmeans = KMeans(n_clusters=num_clusters, init='k-means++', random_state=0).fit(embeddings)
+    cluster_centers = kmeans.cluster_centers_
+
+    closest_point_indices = []
+    for center in cluster_centers:
+        distances = np.linalg.norm(embeddings - center, axis=1)
+        closest_point_indices.append(np.argmin(distances))
+    
+    extracted_contents = [split_contents[idx] for idx in closest_point_indices]
+    results = chain.invoke({"topic": ' '.join(extracted_contents)})
+
+    summary_sentences = re.split(r'(?<!\w\.\w.)(?<![A-Z][a-z]\.)(?<=\.|\?)\s', results)
+    summary_embeddings = embeddings_model.embed_documents(summary_sentences)
+    extracted_embeddings = embeddings_model.embed_documents(extracted_contents)
+    similarity_matrix = cosine_similarity(summary_embeddings, extracted_embeddings)
+
+    cited_results = results
+    relevant_sources = []
+    source_mapping = {}
+    sentence_to_source = {}
+    similarity_threshold = 0.6
+
+    for i, sentence in enumerate(summary_sentences):
+        if sentence in sentence_to_source:
+            continue
+        max_similarity = max(similarity_matrix[i])
+        if max_similarity >= similarity_threshold:
+            most_similar_idx = np.argmax(similarity_matrix[i])
+            if most_similar_idx not in source_mapping:
+                source_mapping[most_similar_idx] = len(relevant_sources) + 1
+                relevant_sources.append((most_similar_idx, extracted_contents[most_similar_idx]))
+            citation_idx = source_mapping[most_similar_idx]
+            citation = f"([Source {citation_idx}](#source-{citation_idx}))"
+            cited_sentence = re.sub(r'([.!?])$', f" {citation}\\1", sentence)
+            sentence_to_source[sentence] = citation_idx
+            cited_results = cited_results.replace(sentence, cited_sentence)
+    
+    sources_list = "\n\n## Sources:\n"
+    for idx, (original_idx, content) in enumerate(relevant_sources):
+        sources_list += f"""
+<details style="margin: 10px 0; padding: 10px; border: 1px solid #ccc; border-radius: 5px; background-color: #f9f9f9;">
+<summary style="font-weight: bold; cursor: pointer;">Source {idx + 1}</summary>
+<pre style="white-space: pre-wrap; word-wrap: break-word; margin-top: 10px;">{content}</pre>
+</details>
+"""
+    cited_results += sources_list
+    return cited_results
+
+def qa_pdf(pdf_file_path, query, num_clusters=5, similarity_threshold=0.6):
+    global global_embeddings, global_split_contents
+
+    # Initialize models and embeddings
+    embeddings_model = OpenAIEmbeddings(model="text-embedding-3-small", api_key=openai_api_key)
+    llm = ChatOpenAI(model="gpt-3.5-turbo", api_key=openai_api_key, temperature=0.3)
+    prompt = ChatPromptTemplate.from_template(
+        """Please provide a detailed and accurate answer to the given question based on the provided contexts. 
+        Ensure that the answer is comprehensive and directly addresses the query. 
+        If necessary, include relevant examples or details from the text.
+        Question: {question}
+        Contexts: {contexts}"""
+    )
+    output_parser = StrOutputParser()
+    chain = prompt | llm | output_parser
+
+    # Load and process the PDF if not already loaded
+    if global_embeddings is None or global_split_contents is None:
+        loader = PyMuPDFLoader(pdf_file_path)
+        docs = loader.load()
+        full_text = "\n".join(doc.page_content for doc in docs)
+        cleaned_full_text = remove_references(full_text)
+        cleaned_full_text = clean_text(cleaned_full_text)
+
+        text_splitter = RecursiveCharacterTextSplitter(chunk_size=800, chunk_overlap=0, separators=["\n\n", "\n", ".", " "])
+        global_split_contents = text_splitter.split_text(cleaned_full_text)
+        global_embeddings = embeddings_model.embed_documents(global_split_contents)
+
+    # Embed the query and find the most relevant contexts
+    query_embedding = embeddings_model.embed_query(query)
+    similarity_scores = cosine_similarity([query_embedding], global_embeddings)[0]
+    top_indices = np.argsort(similarity_scores)[-num_clusters:]
+    relevant_contents = [global_split_contents[i] for i in top_indices]
+
+    # Generate the answer using the LLM chain
+    results = chain.invoke({"question": query, "contexts": ' '.join(relevant_contents)})
+
+    # Split the answer into sentences and embed them
+    answer_sentences = re.split(r'(?<!\w\.\w.)(?<![A-Z][a-z]\.)(?<=\.|\?)\s', results)
+    answer_embeddings = embeddings_model.embed_documents(answer_sentences)
+    relevant_embeddings = embeddings_model.embed_documents(relevant_contents)
+    similarity_matrix = cosine_similarity(answer_embeddings, relevant_embeddings)
+
+    # Map sentences to sources and create citations
+    cited_results = results
+    relevant_sources = []
+    source_mapping = {}
+    sentence_to_source = {}
+
+    for i, sentence in enumerate(answer_sentences):
+        if sentence in sentence_to_source:
+            continue
+        max_similarity = max(similarity_matrix[i])
+        if max_similarity >= similarity_threshold:
+            most_similar_idx = np.argmax(similarity_matrix[i])
+            if most_similar_idx not in source_mapping:
+                source_mapping[most_similar_idx] = len(relevant_sources) + 1
+                relevant_sources.append((most_similar_idx, relevant_contents[most_similar_idx]))
+            citation_idx = source_mapping[most_similar_idx]
+            citation = f"<strong style='color:blue;'>[Source {citation_idx}]</strong>"            
+            cited_sentence = re.sub(r'([.!?])$', f" {citation}\\1", sentence)
+            sentence_to_source[sentence] = citation_idx
+            cited_results = cited_results.replace(sentence, cited_sentence)
+
+    # Format the sources for markdown rendering
+    sources_list = "\n\n## Sources:\n"
+    for idx, (original_idx, content) in enumerate(relevant_sources):
+        sources_list += f"""
+<details style="margin: 10px 0; padding: 10px; border: 1px solid #ccc; border-radius: 5px; background-color: #f9f9f9;">
+<summary style="font-weight: bold; cursor: pointer;">Source {idx + 1}</summary>
+<pre style="white-space: pre-wrap; word-wrap: break-word; margin-top: 10px;">{content}</pre>
+</details>
+"""
+    cited_results += sources_list
+    return cited_results
+
+
+def infer_image_and_get_boxes(image, confidence_threshold=0.6):
+    results = model.predict(image)
+    boxes = [
+        (int(box.xyxy[0][0]), int(box.xyxy[0][1]), int(box.xyxy[0][2]), int(box.xyxy[0][3]), int(box.cls[0]))
+        for result in results for box in result.boxes
+        if int(box.cls[0]) in {figure_class_index, table_class_index} and box.conf[0] > confidence_threshold
+    ]
+    return boxes
+
+def crop_images_from_boxes(image, boxes, scale_factor):
+    figures = []
+    tables = []
+    for (x1, y1, x2, y2, cls) in boxes:
+        cropped_img = image[int(y1 * scale_factor):int(y2 * scale_factor), int(x1 * scale_factor):int(x2 * scale_factor)]
+        if cls == figure_class_index:
+            figures.append(cropped_img)
+        elif cls == table_class_index:
+            tables.append(cropped_img)
+    return figures, tables
+
+
+def process_pdf(pdf_file_path):
+    doc = fitz.open(pdf_file_path)
+    all_figures = []
+    all_tables = []
+    low_dpi = 50
+    high_dpi = 300
+    scale_factor = high_dpi / low_dpi
+    low_res_pixmaps = [page.get_pixmap(dpi=low_dpi) for page in doc]
+    
+    for page_num, low_res_pix in enumerate(low_res_pixmaps):
+        low_res_img = np.frombuffer(low_res_pix.samples, dtype=np.uint8).reshape(low_res_pix.height, low_res_pix.width, 3)
+        boxes = infer_image_and_get_boxes(low_res_img)
+        
+        if boxes:
+            high_res_pix = doc[page_num].get_pixmap(dpi=high_dpi)
+            high_res_img = np.frombuffer(high_res_pix.samples, dtype=np.uint8).reshape(high_res_pix.height, high_res_pix.width, 3)
+            figures, tables = crop_images_from_boxes(high_res_img, boxes, scale_factor)
+            all_figures.extend(figures)
+            all_tables.extend(tables)
+    
+    return all_figures, all_tables
+
+# Set the page configuration for a modern look
+
+# Set the page configuration for a modern look
+# Set the page configuration for a modern look
+st.set_page_config(page_title="PDF Reading Assistant", page_icon="📄", layout="wide")
+
+# Add some custom CSS for a modern look
+st.markdown("""
+    <style>
+        /* Main background and padding */
+        .main {
+            background-color: #f8f9fa;
+            padding: 2rem;
+            font-family: 'Arial', sans-serif;
+        }
+        
+        /* Section headers */
+        .section-header {
+            font-size: 2rem;
+            font-weight: bold;
+            color: #343a40;
+            margin-top: 2rem;
+            margin-bottom: 1rem;
+            text-align: center;
+            font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+        }
+
+        /* Containers */
+        .uploaded-file-container, .chat-container, .summary-container, .extract-container {
+            padding: 2rem;
+            background-color: #ffffff;
+            border-radius: 10px;
+            margin-bottom: 2rem;
+            box-shadow: 0 4px 12px rgba(0, 0, 0, 0.1);
+        }
+
+        /* Buttons */
+        .stButton>button {
+            background-color: #007bff;
+            color: white;
+            padding: 0.6rem 1.2rem;
+            border-radius: 5px;
+            border: none;
+            cursor: pointer;
+            font-size: 1rem;
+            transition: background-color 0.3s ease, transform 0.3s ease;
+        }
+        .stButton>button:hover {
+            background-color: #0056b3;
+            transform: translateY(-2px);
+        }
+
+        /* Chat messages */
+        .chat-message {
+            padding: 1rem;
+            border-radius: 10px;
+            margin-bottom: 1rem;
+            font-size: 1rem;
+            transition: all 0.3s ease;
+            box-shadow: 0 2px 8px rgba(0, 0, 0, 0.1);
+        }
+        .chat-message.user {
+            background-color: #e6f7ff;
+            border-left: 5px solid #007bff;
+            text-align: left;
+        }
+        .chat-message.bot {
+            background-color: #fff0f1;
+            border-left: 5px solid #dc3545;
+            text-align: left;
+        }
+
+        /* Input area */
+        .input-container {
+            display: flex;
+            align-items: center;
+            gap: 10px;
+            margin-top: 1rem;
+        }
+        .input-container textarea {
+            border: 2px solid #ccc;
+            border-radius: 10px;
+            padding: 10px;
+            width: 100%;
+            background-color: #fff;
+            transition: border-color 0.3s ease;
+            margin: 0;
+            font-size: 1rem;
+        }
+        .input-container textarea:focus {
+            border-color: #007bff;
+            outline: none;
+        }
+        .input-container button {
+            background-color: #007bff;
+            color: white;
+            padding: 0.6rem 1.2rem;
+            border-radius: 5px;
+            border: none;
+            cursor: pointer;
+            font-size: 1rem;
+            transition: background-color 0.3s ease, transform 0.3s ease;
+        }
+        .input-container button:hover {
+            background-color: #0056b3;
+            transform: translateY(-2px);
+        }
+
+        /* Expander */
+        .st-expander {
+            border: none;
+            box-shadow: 0 4px 12px rgba(0, 0, 0, 0.1);
+            margin-bottom: 2rem;
+        }
+
+        /* Markdown elements */
+        .stMarkdown {
+            font-size: 1rem;
+            color: #343a40;
+            line-height: 1.6;
+        }
+        
+        /* Titles and subtitles */
+        .stTitle {
+            color: #343a40;
+            text-align: center;
+            margin-bottom: 1rem;
+            font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+        }
+        .stSubtitle {
+            color: #6c757d;
+            text-align: center;
+            margin-bottom: 1rem;
+            font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+        }
+    </style>
+""", unsafe_allow_html=True)
+
+# Streamlit interface
+st.title("📄 PDF Reading Assistant")
+st.markdown("### Extract tables, figures, summaries, and answers from your PDF files easily.")
+
+uploaded_file = st.file_uploader("Upload a PDF", type="pdf")
+if uploaded_file:
+    file_path = save_uploaded_file(uploaded_file)
+    with st.container():
+        st.markdown("<div class='section-header'>Extract Tables and Figures</div>", unsafe_allow_html=True)
+        with st.expander("Click to Extract Tables and Figures", expanded=True):
+            with st.container():
+                extract_button = st.button("Extract")
+                if extract_button:
+                    figures, tables = process_pdf(file_path)
+                    col1, col2 = st.columns(2)
+                    with col1:
+                        st.write("### Figures")
+                        if figures:
+                            for figure in figures:
+                                st.image(figure, use_column_width=True)
+                        else:
+                            st.write("No figures found.")
+                    with col2:
+                        st.write("### Tables")
+                        if tables:
+                            for table in tables:
+                                st.image(table, use_column_width=True)
+                        else:
+                            st.write("No tables found.")
+    
+    with st.container():
+        st.markdown("<div class='section-header'>Get Summary</div>", unsafe_allow_html=True)
+        with st.expander("Click to Generate Summary", expanded=True):
+            with st.container():
+                summary_button = st.button("Generate Summary")
+                if summary_button:
+                    summary = summarize_pdf(file_path)
+                    st.markdown(summary, unsafe_allow_html=True)
+
+    with st.container():
+        st.markdown("<div class='section-header'>Chat with your PDF</div>", unsafe_allow_html=True)
+        st.write("### Chat with your PDF")
+        if 'chat_history' not in st.session_state:
+            st.session_state['chat_history'] = []
+
+        for chat in st.session_state['chat_history']:
+            chat_user_class = "user" if chat["user"] else ""
+            chat_bot_class = "bot" if chat["bot"] else ""
+            st.markdown(f"<div class='chat-message {chat_user_class}'>{chat['user']}</div>", unsafe_allow_html=True)
+            st.markdown(f"<div class='chat-message {chat_bot_class}'>{chat['bot']}</div>", unsafe_allow_html=True)
+
+        with st.form(key="chat_form", clear_on_submit=True):
+            user_input = st.text_area("Ask a question about the PDF:", key="user_input")
+            submit_button = st.form_submit_button(label="Send")
+
+            if submit_button and user_input:
+                st.session_state['chat_history'].append({"user": user_input, "bot": None})
+                answer = qa_pdf(file_path, user_input)
+                st.session_state['chat_history'][-1]["bot"] = answer
                 st.experimental_rerun()