Create app.py

app.py

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+# ---------------------------------------------------------------------------------------
+# 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 mlx_vlm import load, generate
+from mlx_vlm.prompt_utils import apply_chat_template
+from mlx_vlm.utils import load_config, stream_generate
+from docling_core.types.doc.document import DocTagsDocument, DoclingDocument
+
+# Set Streamlit to wide mode
+# st.set_page_config(layout="wide")
+
+# ---------------------------------------------------------------------------------------
+# API Configuration
+# ---------------------------------------------------------------------------------------
+API_URL = "https://api.stack-ai.com/inference/v0/run/2df89a6c-a4af-4576-880e-27058e498f02/67acad8b0603ba4631db38e7"
+headers = {
+    'Authorization': 'Bearer a9e4979e-cdbe-49ea-a193-53562a784805',
+    'Content-Type': 'application/json'
+}
+
+# ---------------------------------------------------------------------------------------
+# Survey Analysis Class
+# ---------------------------------------------------------------------------------------
+class SurveyAnalysis:
+    def __init__(self, api_key=None):
+        self.api_key = api_key
+
+    def prepare_llm_input(self, survey_response, topics):
+        # Create topic description string from user input
+        topic_descriptions = "\n".join([f"- **{topic}**: {description}" for topic, description in topics.items()])
+        
+        llm_input = f"""
+Your task is to review PDF docling and extract information related to the provided topics. Here are the topic descriptions:
+
+{topic_descriptions}
+
+**Instructions:**
+- Extract and summarize the PDF focusing only on the provided topics.
+- If a topic is not mentioned in the notes, it should not be included in the Topic_Summary.
+- Use **exact quotes** from the original text for each point in your Topic_Summary.
+- Exclude erroneous content.
+- Do not add additional explanations or instructions.
+
+**Format your response as follows:**
+[Topic]
+- "Exact quote"
+- "Exact quote"
+- "Exact quote"
+
+**Meeting Notes:**
+{survey_response}
+"""
+        return llm_input
+
+    def query_api(self, payload):
+        response = requests.post(API_URL, headers=headers, json=payload)
+        return response.json()
+
+    def extract_meeting_notes(self, response):
+        output = response.get('outputs', {}).get('out-0', '')
+        return output
+
+    def process_dataframe(self, df, topics):
+        results = []
+        for _, row in df.iterrows():
+            llm_input = self.prepare_llm_input(row['Document_Text'], topics)
+            payload = {
+                "user_id": "<USER or Conversation ID>",  
+                "in-0": llm_input
+            }
+            response = self.query_api(payload)
+            meeting_notes = self.extract_meeting_notes(response)
+            results.append({
+                'Document_Text': row['Document_Text'],
+                'Topic_Summary': meeting_notes
+            })
+        
+        result_df = pd.DataFrame(results)
+        df = df.reset_index(drop=True)
+        return pd.concat([df, result_df[['Topic_Summary']]], axis=1)
+
+# ---------------------------------------------------------------------------------------
+# Function to Extract Excerpts
+# ---------------------------------------------------------------------------------------
+def extract_excerpts(processed_df):
+    new_rows = []
+    
+    for _, row in processed_df.iterrows():
+        Topic_Summary = row['Topic_Summary']
+        
+        # Split the Topic_Summary by topic
+        sections = re.split(r'\n(?=\[)', Topic_Summary)
+        
+        for section in sections:
+            # Extract the topic
+            topic_match = re.match(r'\[([^\]]+)\]', section)
+            if topic_match:
+                topic = topic_match.group(1)
+                
+                # Extract all excerpts within the section
+                excerpts = re.findall(r'- "([^"]+)"', section)
+                
+                for excerpt in excerpts:
+                    new_rows.append({
+                        'Document_Text': row['Document_Text'],
+                        'Topic_Summary': row['Topic_Summary'],
+                        'Excerpt': excerpt,
+                        'Topic': topic
+                    })
+    
+    return pd.DataFrame(new_rows)
+
+#------------------------------------------------------------------------
+# Streamlit Configuration
+#------------------------------------------------------------------------
+
+# Set 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:clevesse@steelcase.com',
+        'About': "This app is built to support PDF analysis"
+    }
+)
+
+#------------------------------------------------------------------------
+# Sidebar
+#------------------------------------------------------------------------
+
+# Sidebar with image
+with st.sidebar:
+    # Set the desired width in pixels
+    image_width = 300  
+    # Define the path to the image
+    # image_path = "steelcase_small.png"
+    image_path = "/Users/clevesse/Documents/VSC_Code/PDF_Extraction/PDF_Extraction_streamlit/steelcase_small.png"
+    # Display the image
+    st.image(image_path, width=image_width)
+
+    # Additional sidebar content
+
+    with st.expander("**WorkSpace Futures**", expanded=True):
+        st.write("""
+        Strategic Market Intelligence  
+        Director: Amy Willard
+
+        - **Support**: Cheyne LeVesseur PhD  
+        - **Email**: clevesse@steelcase.com
+        """)
+    st.divider()
+    st.subheader('Instructions')
+
+    Instructions = """
+    - **Step 1**: Upload your PDF file.
+    - **Step 2**: Review the processed meeting notes with extracted excerpts and classifications.
+    - **Step 3**: Review topic descriptions.
+    - **Step 4**: Review topic distribution and frequency.
+    - **Step 5**: Review bar charts of topics.
+    - **Step 6**: Download the processed data as a CSV file.
+    """
+    st.markdown(Instructions)
+
+# Load SmolDocling model (mlx_vlm version)
+@st.cache_resource
+def load_smol_docling():
+    model_path = "ds4sd/SmolDocling-256M-preview-mlx-bf16"
+    model, processor = load(model_path)
+    config = load_config(model_path)
+    return model, processor, config
+
+model, processor, config = load_smol_docling()
+
+# Convert PDF to images
+def convert_pdf_to_images(pdf_file):
+    images = []
+    doc = fitz.open(stream=pdf_file.read(), filetype="pdf")
+    for page_number in range(len(doc)):
+        page = doc.load_page(page_number)
+        pix = page.get_pixmap(dpi=300)  # Higher DPI for clarity
+        img_data = pix.tobytes("png")
+        image = Image.open(io.BytesIO(img_data))
+        images.append(image)
+    return images
+
+# Extract structured markdown text using SmolDocling (mlx_vlm)
+def extract_markdown_from_image(image):
+    prompt = "Convert this page to docling."
+    formatted_prompt = apply_chat_template(processor, config, prompt, num_images=1)
+    output = ""
+
+    for token in stream_generate(
+        model, processor, formatted_prompt, [image], max_tokens=4096, verbose=False):
+        output += token.text
+        if "</doctag>" in token.text:
+            break
+
+    # Convert DocTags to Markdown
+    doctags_doc = DocTagsDocument.from_doctags_and_image_pairs([output], [image])
+    doc = DoclingDocument(name="ExtractedDocument")
+    doc.load_from_doctags(doctags_doc)
+    markdown_text = doc.export_to_markdown()
+    return markdown_text
+
+# 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:
+    with st.spinner("Processing PDF..."):
+        images = convert_pdf_to_images(uploaded_file)
+
+        markdown_texts = []
+        for idx, image in enumerate(images):
+            markdown_text = extract_markdown_from_image(image)
+            markdown_texts.append(markdown_text)
+
+        df = pd.DataFrame({'Document_Text': markdown_texts})
+
+    st.success("PDF processed successfully!")
+
+    # Check if extraction was successful
+    if df.empty or df['Document_Text'].isnull().all():
+        st.error("No meaningful text extracted from the PDF.")
+        st.stop()
+
+    st.markdown("### Extracted Markdown Preview")
+    st.write(df.head())
+
+    # ---------------------------------------------------------------------------------------
+    # User Input for Topics
+    # ---------------------------------------------------------------------------------------
+    st.markdown("### Enter Topics and Descriptions")
+    num_topics = st.number_input("Number of topics", min_value=1, max_value=10, value=1, step=1)
+
+    topics = {}
+    for i in range(num_topics):
+        topic = st.text_input(f"Topic {i+1} Name", key=f"topic_{i}")
+        description = st.text_area(f"Topic {i+1} Description", key=f"description_{i}")
+        if topic and description:
+            topics[topic] = description
+
+    # Add a button to execute the analysis
+    if st.button("Run Analysis"):
+        if not topics:
+            st.warning("Please enter at least one topic and description.")
+            st.stop()
+
+        # ---------------------------------------------------------------------------------------
+        # Your existing SurveyAnalysis and extract_excerpts functions remain unchanged here:
+        # ---------------------------------------------------------------------------------------
+        analyzer = SurveyAnalysis()
+        processed_df = analyzer.process_dataframe(df, topics)
+        df_VIP_extracted = extract_excerpts(processed_df)
+
+        required_columns = ['Document_Text', 'Topic_Summary', 'Excerpt', 'Topic']
+        missing_columns = [col for col in required_columns if col not in df_VIP_extracted.columns]
+
+        if missing_columns:
+            st.error(f"Missing columns after processing: {missing_columns}")
+            st.stop()
+
+        df_VIP_extracted = df_VIP_extracted[required_columns]
+
+        st.markdown("### Processed Meeting Notes")
+        st.dataframe(df_VIP_extracted)
+
+        st.write(f"**Number of meeting notes analyzed:** {len(df)}")
+        st.write(f"**Number of excerpts extracted:** {len(df_VIP_extracted)}")
+
+        # CSV download
+        csv = df_VIP_extracted.to_csv(index=False)
+        st.download_button(
+            "Download data as CSV",
+            data=csv,
+            file_name='extracted_meeting_notes.csv',
+            mime='text/csv'
+        )
+
+        # Topic distribution visualization
+        topic_counts = df_VIP_extracted['Topic'].value_counts()
+        frequency_table = pd.DataFrame({'Topic': topic_counts.index, 'Count': topic_counts.values})
+        frequency_table['Percentage'] = (frequency_table['Count'] / frequency_table['Count'].sum() * 100).round(0)
+
+        st.markdown("### Topic Distribution")
+        st.dataframe(frequency_table)
+
+        fig, ax = plt.subplots(figsize=(10, 5))
+        ax.bar(frequency_table['Topic'], frequency_table['Count'], color='#3d9aa1')
+        ax.set_ylabel('Count')
+        ax.set_title('Frequency of Topics')
+        st.pyplot(fig)
+
+else:
+    st.info("Please upload a PDF file to begin.")