Update app.py

app.py

@@ -1,7 +1,3 @@
-
-
-
-
 import os
 os.system("python -m spacy download en_core_web_sm")
 import io
@@ -17,21 +13,29 @@ 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_text_splitters import SpacyTextSplitter
 from langchain_core.prompts import ChatPromptTemplate
 from langchain_openai import ChatOpenAI
 import re
 from PIL import Image
-from streamlit_chat import message
 
-# Load the trained model
+# Cached resources
+@st.cache_resource
+def load_models():
+    return {
+        "yolo": YOLO("best.pt"),
+        "embeddings": OpenAIEmbeddings(model="text-embedding-3-small"),
+        "llm": ChatOpenAI(model="gpt-4-turbo", temperature=0.3)
+    }
 
-model = YOLO("best.pt")
-openai_api_key = os.environ.get("openai_api_key")
+models = load_models()
+openai_api_key = os.environ.get("OPENAI_API_KEY")
 
-# Define the class indices for figures, tables, and text
-figure_class_index = 4
-table_class_index = 3
+# Constants
+FIGURE_CLASS_INDEX = 4
+TABLE_CLASS_INDEX = 3
+CHUNK_SIZE = 1000
+CHUNK_OVERLAP = 200
+NUM_CLUSTERS = 8
 
 # Utility functions
 def clean_text(text):
@@ -39,343 +43,182 @@ def clean_text(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'
+        r'\bReferences\b', r'\breferences\b', r'\bBibliography\b', 
+        r'\bCitations\b', r'\bWorks Cited\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):
-    temp_file = tempfile.NamedTemporaryFile(delete=False)
-    temp_file.write(uploaded_file.getbuffer())
-    temp_file.close()
-    return temp_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-4o-mini", 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
+    return re.sub('|'.join(reference_patterns), '', text, flags=re.IGNORECASE)
 
-    loader = PyMuPDFLoader(pdf_file_path)
+@st.cache_data
+def process_pdf(file_path):
+    """Process PDF once and cache results"""
+    loader = PyMuPDFLoader(file_path)
     docs = loader.load()
     full_text = "\n".join(doc.page_content for doc in docs)
-    cleaned_full_text = clean_text(remove_references(full_text))
-    text_splitter = SpacyTextSplitter(chunk_size=500)
-    #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)
-
-    kmeans = KMeans(n_clusters=num_clusters, init='k-means++', random_state=0).fit(embeddings)
-    closest_point_indices = [np.argmin(np.linalg.norm(embeddings - center, axis=1)) for center in kmeans.cluster_centers_]
-    extracted_contents = [split_contents[idx] for idx in closest_point_indices]
-
-    results = chain.invoke({"topic": ' '.join(extracted_contents)})
-
-    return generate_citations(results, extracted_contents)
-
-def qa_pdf(pdf_file_path, query, num_clusters=5, similarity_threshold=0.6):
-    embeddings_model = OpenAIEmbeddings(model="text-embedding-3-small", api_key=openai_api_key)
-    llm = ChatOpenAI(model="gpt-4o-mini", 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}"""
+    cleaned_text = clean_text(remove_references(full_text))
+    
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size=CHUNK_SIZE,
+        chunk_overlap=CHUNK_OVERLAP,
+        separators=["\n\n", "\n", ". ", "! ", "? ", " "]
     )
-    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 = clean_text(remove_references(full_text))
-    text_splitter = SpacyTextSplitter(chunk_size=500)
-
-    #text_splitter = RecursiveCharacterTextSplitter(chunk_size=800, chunk_overlap=0, separators=["\n\n", "\n", ".", " "])
-    split_contents = text_splitter.split_text(cleaned_full_text)
-    embeddings = embeddings_model.embed_documents(split_contents)
-
-    query_embedding = embeddings_model.embed_query(query)
-    similarity_scores = cosine_similarity([query_embedding], embeddings)[0]
-    top_indices = np.argsort(similarity_scores)[-num_clusters:]
-    relevant_contents = [split_contents[i] for i in top_indices]
-
-    results = chain.invoke({"question": query, "contexts": ' '.join(relevant_contents)})
-
-    return generate_citations(results, relevant_contents, similarity_threshold)
-
-def generate_citations(text, contents, similarity_threshold=0.6):
-    embeddings_model = OpenAIEmbeddings(model="text-embedding-3-small", api_key=openai_api_key)
-    text_sentences = re.split(r'(?<!\w\.\w.)(?<![A-Z][a-z]\.)(?<=\.|\?)\s', text)
-    text_embeddings = embeddings_model.embed_documents(text_sentences)
-    content_embeddings = embeddings_model.embed_documents(contents)
-    similarity_matrix = cosine_similarity(text_embeddings, content_embeddings)
-
-    cited_text = text
-    relevant_sources = []
-    source_mapping = {}
-    sentence_to_source = {}
-
-    for i, sentence in enumerate(text_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, 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_text = cited_text.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: 1px 0; padding: 5px; border: 1px solid #ccc; border-radius: 8px; background-color: #f9f9f9; transition: all 0.3s ease;">
-  <summary style="font-weight: bold; cursor: pointer; outline: none; padding: 5px 0; transition: color 0.3s ease;">Source {idx + 1}</summary>
-  <pre style="white-space: pre-wrap; word-wrap: break-word; margin: 1px 0; padding: 10px; background-color: #fff; border-radius: 5px; border: 1px solid #ddd; box-shadow: 0 2px 5px rgba(0, 0, 0, 0.1);">{content}</pre>
-</details>
-"""
-
-    # Add dummy blanks after the last source
-    dummy_blanks = """
-<div style="margin: 20px 0;"></div>
-<div style="margin: 20px 0;"></div>
-<div style="margin: 20px 0;"></div>
-<div style="margin: 20px 0;"></div>
-<div style="margin: 20px 0;"></div>
-
-"""
-
-    cited_text += sources_list + dummy_blanks
-    return cited_text
-
-def infer_image_and_get_boxes(image, confidence_threshold=0.8):
-    results = model.predict(image)
-    return [
-        (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
-    ]
-
-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
+    split_contents = text_splitter.split_text(cleaned_text)
+    
+    return {
+        "text": cleaned_text,
+        "chunks": split_contents,
+        "embeddings": models["embeddings"].embed_documents(split_contents)
+    }
 
-def process_pdf(pdf_file_path):
-    doc = fitz.open(pdf_file_path)
+@st.cache_data
+def extract_visuals(file_path):
+    """Extract figures and tables with caching"""
+    doc = fitz.open(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):
+    for page in doc:
+        low_res_pix = page.get_pixmap(dpi=50)
         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)
+        
+        results = models["yolo"].predict(low_res_img)
+        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 box.conf[0] > 0.8 and int(box.cls[0]) in {FIGURE_CLASS_INDEX, TABLE_CLASS_INDEX}
+        ]
         
         if boxes:
-            high_res_pix = doc[page_num].get_pixmap(dpi=high_dpi)
+            high_res_pix = page.get_pixmap(dpi=300)
             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)
+            
+            for x1, y1, x2, y2, cls in boxes:
+                img = high_res_img[int(y1*6):int(y2*6), int(x1*6):int(x2*6)]
+                if cls == FIGURE_CLASS_INDEX:
+                    all_figures.append(img)
+                else:
+                    all_tables.append(img)
     
-    return all_figures, all_tables
-
-def image_to_base64(img):
-    buffered = io.BytesIO()
-    img = Image.fromarray(img)
-    img.save(buffered, format="PNG")
-    return base64.b64encode(buffered.getvalue()).decode()
-
-def on_btn_click():
-    del st.session_state.chat_history[:]
-
-# Streamlit interface
+    return {"figures": all_figures, "tables": all_tables}
+
+def generate_summary(chunks, embeddings):
+    """Generate summary using clustered chunks"""
+    kmeans = KMeans(n_clusters=NUM_CLUSTERS, init='k-means++').fit(embeddings)
+    cluster_indices = [np.argmin(np.linalg.norm(embeddings - center, axis=1)) 
+                       for center in kmeans.cluster_centers_]
+    selected_chunks = [chunks[i] for i in cluster_indices]
+    
+    prompt = ChatPromptTemplate.from_template(
+        """Create a structured summary with key points from these context sections:
+        {contexts}
+        Format:
+        ## Summary
+        [concise overview]
+        ## Key Points
+        - [main point 1]
+        - [main point 2]
+        ..."""
+    )
+    chain = prompt | models["llm"] | StrOutputParser()
+    return chain.invoke({"contexts": '\n\n'.join(selected_chunks)})
+
+def answer_question(question, chunks, embeddings):
+    """Answer question using semantic search"""
+    query_embedding = models["embeddings"].embed_query(question)
+    similarities = cosine_similarity([query_embedding], embeddings)[0]
+    top_indices = np.argsort(similarities)[-5:][::-1]
+    context = '\n'.join([chunks[i] for i in top_indices if similarities[i] > 0.6])
+    
+    prompt = ChatPromptTemplate.from_template(
+        """Answer this question: {question}
+        Using only this context: {context}
+        - Be precise and include relevant details
+        - Cite sources as [Source 1], [Source 2], etc."""
+    )
+    chain = prompt | models["llm"] | StrOutputParser()
+    return chain.invoke({"question": question, "context": context})
+
+# Streamlit UI
+st.set_page_config(page_title="PDF Assistant", layout="wide")
+st.title("📄 Smart PDF Assistant")
+
+if "chat" not in st.session_state:
+    st.session_state.chat = []
+if "processed_data" not in st.session_state:
+    st.session_state.processed_data = None
+
+# File upload section
+with st.sidebar:
+    uploaded_file = st.file_uploader("Upload PDF", type="pdf")
+    if uploaded_file:
+        with tempfile.NamedTemporaryFile(delete=False) as tmp:
+            tmp.write(uploaded_file.getbuffer())
+            st.session_state.processed_data = process_pdf(tmp.name)
+            visuals = extract_visuals(tmp.name)
+
+# Chat interface
+col1, col2 = st.columns([3, 1])
+with col1:
+    st.subheader("Document Interaction")
+    
+    for msg in st.session_state.chat:
+        with st.chat_message(msg["role"]):
+            if "image" in msg:
+                st.image(msg["image"], caption=msg.get("caption"))
+            else:
+                st.markdown(msg["content"])
+
+    if prompt := st.chat_input("Ask about the document..."):
+        st.session_state.chat.append({"role": "user", "content": prompt})
+        with st.spinner("Analyzing..."):
+            response = answer_question(
+                prompt,
+                st.session_state.processed_data["chunks"],
+                st.session_state.processed_data["embeddings"]
+            )
+        st.session_state.chat.append({"role": "assistant", "content": response})
+        st.rerun()
+
+with col2:
+    st.subheader("Document Insights")
+    
+    if st.button("Generate Summary"):
+        with st.spinner("Summarizing..."):
+            summary = generate_summary(
+                st.session_state.processed_data["chunks"],
+                st.session_state.processed_data["embeddings"]
+            )
+            st.session_state.chat.append({
+                "role": "assistant",
+                "content": f"## Document Summary\n{summary}"
+            })
+        st.rerun()
+    
+    if visuals["figures"]:
+        with st.expander(f"📷 Figures ({len(visuals['figures'])})"):
+            for idx, fig in enumerate(visuals["figures"], 1):
+                st.image(fig, caption=f"Figure {idx}")
+    
+    if visuals["tables"]:
+        with st.expander(f"📊 Tables ({len(visuals['tables'])})"):
+            for idx, tbl in enumerate(visuals["tables"], 1):
+                st.image(tbl, caption=f"Table {idx}")
 
-# Custom CSS for the file uploader
-uploadercss='''
+# Custom styling
+st.markdown("""
 <style>
-    [data-testid='stFileUploader'] {
-        width: max-content;
-    }
-    [data-testid='stFileUploader'] section {
-        padding: 0;
-        float: left;
+    [data-testid=stSidebar] {
+        background: #fafafa;
+        border-right: 1px solid #eee;
     }
-    [data-testid='stFileUploader'] section > input + div {
-        display: none;
+    .stChatMessage {
+        padding: 1rem;
+        margin: 0.5rem 0;
+        border-radius: 10px;
+        box-shadow: 0 2px 5px rgba(0,0,0,0.1);
     }
-    [data-testid='stFileUploader'] section + div {
-        float: right;
-        padding-top: 0;
+    [data-testid=stVerticalBlock] > div:has(>.stChatMessage) {
+        gap: 0.5rem;
     }
-
 </style>
-'''
-
-st.set_page_config(page_title="PDF Reading Assistant", page_icon="📄")
-
-# Initialize chat history in session state if not already present
-if 'chat_history' not in st.session_state:
-    st.session_state.chat_history = []
-
-st.title("📄 PDF Reading Assistant")
-st.markdown("### Extract tables, figures, summaries, and answers from your PDF files easily.")
-chat_placeholder = st.empty()
-
-# File uploader for PDF
-uploaded_file = st.file_uploader("Upload a PDF", type="pdf")
-st.markdown(uploadercss, unsafe_allow_html=True)
-if uploaded_file:
-    file_path = save_uploaded_file(uploaded_file)
-
-    # Chat container where all messages will be displayed
-    chat_container = st.container()
-    user_input = st.chat_input("Ask a question about the pdf......", key="user_input")
-    with chat_container:
-        # Scrollable chat messages
-        for idx, chat in enumerate(st.session_state.chat_history):
-            if chat.get("user"):
-                message(chat["user"], is_user=True, allow_html=True, key=f"user_{idx}", avatar_style="initials", seed="user")
-            if chat.get("bot"):
-                message(chat["bot"], is_user=False, allow_html=True, key=f"bot_{idx}",seed="bot")
-
-        # Input area and buttons for user interaction
-        with st.form(key="chat_form", clear_on_submit=True,border=False):
-
-            col1, col2, col3 = st.columns([1, 1, 1])
-            with col1:
-                summary_button = st.form_submit_button("Generate Summary")
-            with col2:
-                extract_button = st.form_submit_button("Extract Tables and Figures")
-            with col3:
-                st.form_submit_button("Clear message", on_click=on_btn_click)
-
-            # Handle responses based on user input and button presses
-            if summary_button:
-                with st.spinner("Generating summary..."):
-                    summary = summarize_pdf(file_path)
-                st.session_state.chat_history.append({"user": "Generate Summary", "bot": summary})
-                st.rerun()
-
-            if extract_button:
-                with st.spinner("Extracting tables and figures..."):
-                    figures, tables = process_pdf(file_path)
-                    if figures:
-                        st.session_state.chat_history.append({"user": "Figures"})
-
-                        for idx, figure in enumerate(figures):
-                            figure_base64 = image_to_base64(figure)
-                            result_html = f'<img src="data:image/png;base64,{figure_base64}" style="width:100%; display:block;" alt="Figure {idx+1}"/>'
-                            st.session_state.chat_history.append({"bot": f"Figure {idx+1} {result_html}"})
-                    if tables:
-                        st.session_state.chat_history.append({"user": "Tables"})
-                        for idx, table in enumerate(tables):
-                            table_base64 = image_to_base64(table)
-                            result_html = f'<img src="data:image/png;base64,{table_base64}" style="width:100%; display:block;" alt="Table {idx+1}"/>'
-                            st.session_state.chat_history.append({"bot": f"Table {idx+1} {result_html}"})
-                st.rerun()
-
-            if user_input:
-                st.session_state.chat_history.append({"user": user_input, "bot": None})
-                with st.spinner("Processing..."):
-                    answer = qa_pdf(file_path, user_input)
-                st.session_state.chat_history[-1]["bot"] = answer
-                st.rerun()
-
-# Additional CSS and JavaScript to ensure the chat container is scrollable and scrolls to the bottom
-st.markdown("""
-    <style>
-        #chat-container {
-            max-height: 500px;
-            overflow-y: auto;
-            padding: 1rem;
-            border: 1px solid #ddd;
-            border-radius: 8px;
-            background-color: #fefefe;
-            box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
-            transition: background-color 0.3s ease;
-        }
-        #chat-container:hover {
-            background-color: #f9f9f9;
-        }
-        .stChatMessage {
-            padding: 0.75rem;
-            margin: 0.75rem 0;
-            border-radius: 8px;
-            box-shadow: 0 1px 3px rgba(0, 0, 0, 0.1);
-            transition: background-color 0.3s ease;
-        }
-        .stChatMessage--user {
-            background-color: #E3F2FD;
-        }
-        .stChatMessage--user:hover {
-            background-color: #BBDEFB;
-        }
-        .stChatMessage--bot {
-            background-color: #EDE7F6;
-        }
-        .stChatMessage--bot:hover {
-            background-color: #D1C4E9;
-        }
-        textarea {
-            width: 100%;
-            padding: 1rem;
-            border: 1px solid #ddd;
-            border-radius: 8px;
-            box-shadow: inset 0 1px 3px rgba(0, 0, 0, 0.1);
-            transition: border-color 0.3s ease, box-shadow 0.3s ease;
-        }
-        textarea:focus {
-            border-color: #4CAF50;
-            box-shadow: 0 0 5px rgba(76, 175, 80, 0.5);
-        }
-        .stButton > button {
-            width: 100%;
-            background-color: #4CAF50;
-            color: white;
-            border: none;
-            border-radius: 8px;
-            padding: 0.75rem;
-            font-size: 16px;
-            box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
-            transition: background-color 0.3s ease, box-shadow 0.3s ease;
-        }
-        .stButton > button:hover {
-            background-color: #45A049;
-            box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
-        }
-    </style>
-    <script>
-        const chatContainer = document.getElementById('chat-container');
-        chatContainer.scrollTop = chatContainer.scrollHeight;
-    </script>
-""", unsafe_allow_html=True)
+""", unsafe_allow_html=True)