app.py

# ---------------------------------------------------------------------------------------
# Imports and Options
# ---------------------------------------------------------------------------------------
import streamlit as st
import pandas as pd
import requests
import re
import fitz  # PyMuPDF
import io
import matplotlib.pyplot as plt
from PIL import Image
from transformers import AutoProcessor, AutoModelForVision2Seq
from docling_core.types.doc import DoclingDocument
from docling_core.types.doc.document import DocTagsDocument
import torch
import os
from huggingface_hub import InferenceClient

# ---------------------------------------------------------------------------------------
# Streamlit Page Configuration
# ---------------------------------------------------------------------------------------
st.set_page_config(
    page_title="Choose Your Own Adventure (Topic Extraction) PDF Analysis App",
    page_icon=":bar_chart:",
    layout="centered",
    initial_sidebar_state="auto",
    menu_items={
        'Get Help': 'mailto:support@mtss.ai',
        'About': "This app is built to support PDF analysis"
    }
)

# ---------------------------------------------------------------------------------------
# Streamlit Sidebar
# ---------------------------------------------------------------------------------------

st.sidebar.title("📌 About This App")

st.sidebar.markdown("""
#### ⚠️ **Important Note on Processing Time**

This app uses the **SmolDocling** model (`ds4sd/SmolDocling-256M-preview`) to convert PDF pages into markdown text. Currently, the model is running on a CPU-based environment (**CPU basic | 2 vCPU - 16 GB RAM**), and therefore processing each page can take a significant amount of time (approximately **6 minutes per page**).

**Note: It is recommended that you upload single-page PDFs, as testing showed approximately 6 minutes of processing time per page.**

This setup is suitable for testing and demonstration purposes, but **not efficient for real-world usage**.

For faster processing, consider running the optimized version `ds4sd/SmolDocling-256M-preview-mlx-bf16` locally on a MacBook, where it performs significantly faster.

---

#### 🛠️ **How This App Works**

Here's a quick overview of the workflow:

1. **Upload PDF**: You upload a PDF document using the uploader provided.
2. **Convert PDF to Images**: The PDF is converted into individual images (one per page).
3. **Extract Markdown from Images**: Each image is processed by the SmolDocling model to extract markdown-formatted text.
4. **Enter Topics and Descriptions**: You provide specific topics and their descriptions you'd like to extract from the document.
5. **Extract Excerpts**: The app uses the **meta-llama/Llama-3.1-70B-Instruct** model to extract exact quotes relevant to your provided topics.
6. **Results in a DataFrame**: All extracted quotes and their topics are compiled into a structured DataFrame that you can preview and download.

---

Please proceed by uploading your PDF file to begin the analysis.
""")

# ---------------------------------------------------------------------------------------
# Session State Initialization
# ---------------------------------------------------------------------------------------
for key in ['pdf_processed', 'markdown_texts', 'df']:
    if key not in st.session_state:
        st.session_state[key] = False if key == 'pdf_processed' else []

# ---------------------------------------------------------------------------------------
# API Configuration
# ---------------------------------------------------------------------------------------
hf_api_key = os.getenv('HF_API_KEY')
if not hf_api_key:
    raise ValueError("HF_API_KEY not set in environment variables")

client = InferenceClient(api_key=hf_api_key)

# ---------------------------------------------------------------------------------------
# Survey Analysis Class
# ---------------------------------------------------------------------------------------
class AIAnalysis:
    def __init__(self, client):
        self.client = client

    def prepare_llm_input(self, document_content, topics):
        topic_descriptions = "\n".join([f"- **{t}**: {d}" for t, d in topics.items()])
        return f"""Extract and summarize PDF notes based on topics:
{topic_descriptions}

Instructions:
- Extract exact quotes per topic.
- Ignore irrelevant topics.
- Strictly follow this format:

[Topic]
- "Exact quote"

Document Content:
{document_content} 
"""

    def prompt_response_from_hf_llm(self, llm_input):
        system_prompt = """
        You are an expert assistant tasked with extracting exact quotes from provided meeting notes based on given topics.

        Instructions:
        - Only extract exact quotes relevant to provided topics.
        - Ignore irrelevant content.
        - Strictly follow this format:

        [Topic]
        - "Exact quote"
        """

        response = self.client.chat.completions.create(
            model="meta-llama/Llama-3.1-70B-Instruct",
            messages=[
                {"role": "system", "content": system_prompt},
                {"role": "user", "content": llm_input}
            ],
            stream=True,
            temperature=0.5,
            max_tokens=1024,
            top_p=0.7
        )

        response_content = ""
        for message in response:
            # Correctly handle streaming response
            response_content += message.choices[0].delta.content

        print("Full AI Response:", response_content)  # Debugging
        return response_content.strip()

    def extract_text(self, response):
        return response

    def process_dataframe(self, df, topics):
        results = []
        for _, row in df.iterrows():
            llm_input = self.prepare_llm_input(row['Document_Text'], topics)
            response = self.prompt_response_from_hf_llm(llm_input)
            notes = self.extract_text(response)
            results.append({'Document_Text': row['Document_Text'], 'Topic_Summary': notes})
        return pd.concat([df.reset_index(drop=True), pd.DataFrame(results)['Topic_Summary']], axis=1)

# ---------------------------------------------------------------------------------------
# Helper Functions
# ---------------------------------------------------------------------------------------
@st.cache_resource
def load_smol_docling():
    device = "cuda" if torch.cuda.is_available() else "cpu"
    processor = AutoProcessor.from_pretrained("ds4sd/SmolDocling-256M-preview")
    model = AutoModelForVision2Seq.from_pretrained(
        "ds4sd/SmolDocling-256M-preview", torch_dtype=torch.float32
    ).to(device)
    return model, processor

model, processor = load_smol_docling()

def convert_pdf_to_images(pdf_file, dpi=150, max_size=1600):
    images = []
    doc = fitz.open(stream=pdf_file.read(), filetype="pdf")
    for page in doc:
        pix = page.get_pixmap(dpi=dpi)
        img = Image.open(io.BytesIO(pix.tobytes("png"))).convert("RGB")
        img.thumbnail((max_size, max_size), Image.LANCZOS)
        images.append(img)
    return images

def extract_markdown_from_image(image):
    device = "cuda" if torch.cuda.is_available() else "cpu"
    prompt = processor.apply_chat_template([{"role": "user", "content": [{"type": "image"}, {"type": "text", "text": "Convert this page to docling."}]}], add_generation_prompt=True)
    inputs = processor(text=prompt, images=[image], return_tensors="pt").to(device)
    with torch.no_grad():
        generated_ids = model.generate(**inputs, max_new_tokens=1024)
    doctags = processor.batch_decode(generated_ids[:, inputs.input_ids.shape[1]:], skip_special_tokens=False)[0].replace("<end_of_utterance>", "").strip()
    doctags_doc = DocTagsDocument.from_doctags_and_image_pairs([doctags], [image])
    doc = DoclingDocument(name="ExtractedDocument")
    doc.load_from_doctags(doctags_doc)
    return doc.export_to_markdown()

# Revised extract_excerpts function with improved robustness
def extract_excerpts(processed_df):
    rows = []
    for _, r in processed_df.iterrows():
        sections = re.split(r'\n(?=(?:\*\*|\[)?[A-Za-z/ ]+(?:\*\*|\])?\n- )', r['Topic_Summary'])
        for sec in sections:
            topic_match = re.match(r'(?:\*\*|\[)?([A-Za-z/ ]+)(?:\*\*|\])?', sec.strip())
            if topic_match:
                topic = topic_match.group(1).strip()
                excerpts = re.findall(r'- "?([^"\n]+)"?', sec)
                for excerpt in excerpts:
                    rows.append({
                        'Document_Text': r['Document_Text'],
                        'Topic_Summary': r['Topic_Summary'],
                        'Excerpt': excerpt.strip(),
                        'Topic': topic
                    })
    print("Extracted Rows:", rows)  # Debugging
    return pd.DataFrame(rows)

# ---------------------------------------------------------------------------------------
# Streamlit UI
# ---------------------------------------------------------------------------------------
st.title("Choose Your Own Adventure (Topic Extraction) PDF Analysis App")

uploaded_file = st.file_uploader("Upload PDF file", type=["pdf"])

if uploaded_file and not st.session_state['pdf_processed']:
    with st.spinner("Processing PDF..."):
        images = convert_pdf_to_images(uploaded_file)
        markdown_texts = [extract_markdown_from_image(img) for img in images]
        st.session_state['df'] = pd.DataFrame({'Document_Text': markdown_texts})
        st.session_state['pdf_processed'] = True
    st.success("PDF processed successfully!")

if st.session_state['pdf_processed']:
    st.markdown("### Extracted Text Preview")
    st.write(st.session_state['df'].head())

    st.markdown("### Enter Topics and Descriptions")
    num_topics = st.number_input("Number of topics", 1, 10, 1)
    topics = {}
    for i in range(num_topics):
        topic = st.text_input(f"Topic {i+1} Name", key=f"topic_{i}")
        desc = st.text_area(f"Topic {i+1} Description", key=f"description_{i}")
        if topic and desc:
            topics[topic] = desc

    if st.button("Run Analysis"):
        if not topics:
            st.warning("Please enter at least one topic and description.")
            st.stop()

        analyzer = AIAnalysis(client)
        processed_df = analyzer.process_dataframe(st.session_state['df'], topics)
        extracted_df = extract_excerpts(processed_df)

        st.markdown("### Extracted Excerpts")
        st.dataframe(extracted_df)

        csv = extracted_df.to_csv(index=False)
        st.download_button("Download CSV", csv, "extracted_notes.csv", "text/csv")

        if not extracted_df.empty:
            topic_counts = extracted_df['Topic'].value_counts()
            fig, ax = plt.subplots()
            topic_counts.plot.bar(ax=ax, color='#3d9aa1')
            st.pyplot(fig)
        else:
            st.warning("No topics were extracted. Please check the input data and topics.")

if not uploaded_file:
    st.info("Please upload a PDF file to begin.")