Merge pull request #46 from dsba6010-llm-applications/Preprocess-Updates

Preprocess updates

Preprocessing/.streamlit/config.toml

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+[server]
+maxUploadSize = 1000  # Set the upload size limit in MB

Preprocessing/App/main.py

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+# Standard Python imports
+import os
+import sys
+from datetime import datetime
+
+# Load environment variables and set Python path
+from dotenv import load_dotenv
+load_dotenv()
+
+# Set PYTHONPATH from .env if available
+python_path = os.getenv("PYTHONPATH")
+if python_path:
+    sys.path.append(python_path)
+
+# Import dependencies
+import streamlit as st
+import weaviate  # Import Weaviate client
+from preprocessing_pipeline.pdf_conversion import convert_pdf_to_text
+from preprocessing_pipeline.audio_transcription import transcribe_audio
+from preprocessing_pipeline.text_cleaning import clean_text
+from preprocessing_pipeline.chunking_vector_embedding import tokenize_and_embed_text
+from utils.azure_blob_utils import (
+    upload_to_azure,
+    download_from_azure,
+    list_blobs_in_folder
+)
+
+# Set up Weaviate client
+client = weaviate.Client(
+    url=os.getenv("WEAVIATE_URL"),
+    auth_client_secret=weaviate.AuthApiKey(api_key=os.getenv("WEAVIATE_API_KEY"))
+)
+
+# Helper function: Generate standardized file names
+def generate_file_name(metadata, stage):
+    meeting_date = metadata["meeting_date"].strftime("%Y_%m_%d")
+    meeting_type = "BOC" if metadata["meeting_type"] == "Board of Commissioners" else "PB"
+    file_type = metadata["file_type"]
+    return f"{meeting_date}_{meeting_type}_{file_type}_{stage}"
+
+# Helper function: Check and overwrite files in local storage
+def save_file_with_overwrite(file_path, content):
+    if os.path.exists(file_path):
+        os.remove(file_path)  # Overwrite existing file
+    with open(file_path, "w") as f:
+        f.write(content)
+
+# Helper function: Fetch documents from Weaviate
+def fetch_uploaded_documents():
+    query = """
+    {
+      Get {
+        Documents {
+          file_name
+          file_type
+          meeting_date
+          meeting_type
+          clean_text
+          chunks
+        }
+      }
+    }
+    """
+    response = client.query.raw(query)
+    documents = response.get("data", {}).get("Get", {}).get("Documents", [])
+    return documents
+
+# Home Page
+def home_page():
+    # Custom styling with IBM Plex Mono
+    st.markdown("""
+    <style>
+    .main {
+        background: #f0f2e9;
+        font-family: 'IBM Plex Mono', monospace;
+    }
+    .title-container {
+        display: flex;
+        align-items: center;
+        justify-content: center;
+        gap: 50px;
+        height: 50vh;
+        flex-direction: column;
+    }
+    .main-text {
+        font-size: 150px;
+        color: #0D6051;
+        opacity: 0.9;
+        font-weight: 700;
+        font-family: 'IBM Plex Mono', monospace;
+        line-height: 1;
+        text-align: center;
+    }
+    .description {
+        font-family: 'IBM Plex Mono', monospace;
+        font-size: 18px;
+        color: #263d36;
+        text-align: center;
+        margin-top: 20px;
+    }
+    .stButton>button {
+        background-color: #0D6051;
+        color: white;
+        font-size: 25px;
+        font-weight: bold;
+        padding: 15px 30px;
+        border-radius: 10px;
+        border: none;
+        cursor: pointer;
+    }
+    .stButton>button:hover {
+        background-color: #2f8479;
+    }
+    </style>
+    """, unsafe_allow_html=True)
+
+    st.markdown("""
+    <div class="title-container">
+        <h1 class="main-text">Minute Mate</h1>
+        <p class="description">
+            Welcome to Minute Mate; this is a staff-level application to upload meeting audios, minutes, and agendas to provide further context to the front end.
+        </p>
+    </div>
+    """, unsafe_allow_html=True)
+
+    # Navigation buttons
+    col1, col2 = st.columns([1, 1])
+    with col1:
+        if st.button("Upload Files", key="upload", help="Upload meeting documents and audio files"):
+            st.session_state.page = "upload"
+    with col2:
+        if st.button("View Documents", key="view", help="View the documents that have been uploaded"):
+            st.session_state.page = "view"
+
+# Upload Files Page
+def upload_files_page():
+    st.title("Upload Municipal Meeting Documents")
+
+    # Sidebar for metadata and options selection
+    st.sidebar.header("Document Metadata & Transcription Options")
+    meeting_date = st.sidebar.date_input("Select Meeting Date", datetime.today())
+    meeting_type = st.sidebar.selectbox("Meeting Type", ["Planning Board", "Board of Commissioners"])
+    file_type = st.sidebar.radio("File Type", ["Agenda", "Minutes", "Audio"])
+    model_option = st.sidebar.selectbox("Select Transcription Model", ["default", "best", "nano"])
+    speaker_labels = st.sidebar.checkbox("Enable Speaker Diarization")
+
+    # Save metadata
+    if st.sidebar.button("Save Metadata"):
+        st.session_state["metadata"] = {
+            "meeting_date": meeting_date,
+            "meeting_type": meeting_type,
+            "file_type": file_type,
+            "model": model_option,
+            "speaker_labels": speaker_labels
+        }
+
+    st.header("Upload New Document")
+    file = st.file_uploader("Choose a file to upload", type=["pdf", "mp3", "wav"])
+
+    # Initialize progress bar
+    progress_bar = st.progress(0)
+
+    if file and "metadata" in st.session_state:
+        metadata = st.session_state["metadata"]
+
+        # Preserve the original file extension
+        file_extension = os.path.splitext(file.name)[1]
+        raw_file_name = f"{generate_file_name(metadata, 'Raw')}{file_extension}"
+
+        # Stage 1: Upload to Raw
+        upload_to_azure("raw", raw_file_name, file.read())
+        st.write(f"Uploaded file to Azure `raw/` folder: {raw_file_name}")
+        progress_bar.progress(20)
+
+        # Stage 2: Process based on file type
+        if metadata["file_type"] == "Audio" and file_extension in [".mp3", ".wav"]:
+            with st.spinner(f"Transcribing audio using {metadata['model']} model..."):
+                transcribed_text = transcribe_audio(
+                    raw_file_name=raw_file_name,
+                    model=metadata["model"],
+                    speaker_labels=metadata["speaker_labels"]
+                )
+            if transcribed_text:
+                dirty_file_name = generate_file_name(metadata, "Transcription") + ".txt"
+                upload_to_azure("dirty", dirty_file_name, transcribed_text)
+                st.write(f"Uploaded transcription to `dirty/` folder: {dirty_file_name}")
+                st.text_area("Transcribed Audio Text:", transcribed_text, height=200)
+                st.download_button("Download Transcribed Text", data=transcribed_text, file_name=dirty_file_name)
+            else:
+                st.error("Failed to transcribe the audio.")
+
+        elif metadata["file_type"] in ["Agenda", "Minutes"] and file_extension == ".pdf":
+            with st.spinner("Extracting text from PDF..."):
+                extracted_text = convert_pdf_to_text(raw_file_name)
+            if extracted_text:
+                dirty_file_name = generate_file_name(metadata, "TextExtraction") + ".txt"
+                upload_to_azure("dirty", dirty_file_name, extracted_text)
+                st.write(f"Uploaded extracted text to `dirty/` folder: {dirty_file_name}")
+                st.text_area("Extracted PDF Text:", extracted_text, height=200)
+                st.download_button("Download Extracted Text", data=extracted_text, file_name=dirty_file_name)
+            else:
+                st.error("Failed to extract text from the PDF.")
+
+        # Stage 3: Clean Text and Upload to Clean
+        dirty_content = download_from_azure("dirty", dirty_file_name)
+        with st.spinner("Cleaning text using generative AI..."):
+            cleaned_text = clean_text(dirty_file_name)
+        clean_file_name = generate_file_name(metadata, "Cleaned") + ".txt"
+        upload_to_azure("clean", clean_file_name, cleaned_text)
+        st.write(f"Uploaded cleaned text to `clean/` folder: {clean_file_name}")
+
+        # Display cleaned text
+        st.text_area("Cleaned Text:", cleaned_text, height=200)
+        st.download_button("Download Cleaned Text", data=cleaned_text, file_name=clean_file_name)
+
+        # Stage 4: Chunk and Embed into Weaviate
+        with st.spinner("Chunking and embedding text into Weaviate..."):
+            tokenize_and_embed_text(clean_file_name, metadata)
+        st.success("Document processed and embedded successfully!")
+        progress_bar.progress(100)
+
+    # Navigation buttons
+    col1, col2 = st.columns([1, 1])
+    with col1:
+        if st.button("Return Home"):
+            st.session_state.page = "home"
+    with col2:
+        if st.button("View Documents"):
+            st.session_state.page = "view"
+
+# View Documents Page
+def view_documents_page():
+    st.title("Uploaded Documents")
+    try:
+        raw_blobs = list_blobs_in_folder("raw")
+        dirty_blobs = list_blobs_in_folder("dirty")
+        clean_blobs = list_blobs_in_folder("clean")
+
+        # Display documents by category
+        if raw_blobs:
+            st.subheader("Raw Documents")
+            for blob in raw_blobs:
+                st.write(f"- {blob}")
+                if st.button(f"Download {blob}", key=f"download_raw_{blob}"):
+                    file_content = download_from_azure("raw", blob)
+                    st.download_button("Download", data=file_content, file_name=blob)
+
+        if dirty_blobs:
+            st.subheader("Dirty Documents")
+            for blob in dirty_blobs:
+                st.write(f"- {blob}")
+                if st.button(f"Download {blob}", key=f"download_dirty_{blob}"):
+                    file_content = download_from_azure("dirty", blob)
+                    st.download_button("Download", data=file_content, file_name=blob)
+
+        if clean_blobs:
+            st.subheader("Clean Documents")
+            for blob in clean_blobs:
+                st.write(f"- {blob}")
+                if st.button(f"Download {blob}", key=f"download_clean_{blob}"):
+                    file_content = download_from_azure("clean", blob)
+                    st.download_button("Download", data=file_content, file_name=blob)
+
+        if not raw_blobs and not dirty_blobs and not clean_blobs:
+            st.write("No documents found in the Azure Blob Storage.")
+    except Exception as e:
+        st.error(f"Error fetching documents from Azure Blob Storage: {e}")
+
+    # Navigation buttons
+    col1, col2 = st.columns([1, 1])
+    with col1:
+        if st.button("Return Home"):
+            st.session_state.page = "home"
+    with col2:
+        if st.button("Upload Files"):
+            st.session_state.page = "upload"
+
+# Main page selection logic
+if "page" not in st.session_state:
+    st.session_state.page = "home"
+
+if st.session_state.page == "home":
+    home_page()
+elif st.session_state.page == "upload":
+    upload_files_page()
+elif st.session_state.page == "view":
+    view_documents_page()

Preprocessing/docker/Dockerfile

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+# Dockerfile to set up the environment for Streamlit app
+
+# Use Python base image
+FROM python:3.9-slim
+
+# Set working directory
+WORKDIR /app
+
+# Copy requirements file and install dependencies
+COPY docker/requirements.txt .
+RUN pip install -r requirements.txt
+
+# Copy the application files
+COPY . .
+
+# Expose port for Streamlit
+EXPOSE 8501
+
+# Run the Streamlit application
+CMD ["streamlit", "run", "app/main.py", "--server.port=8501", "--server.address=0.0.0.0"]

Preprocessing/docker/requirements.txt

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+# Environment variables management
+python-dotenv==1.0.0
+
+# Audio transcription
+assemblyai==0.35.1
+
+# Vector database (Weaviate client)
+weaviate-client==4.7.1
+
+# Embedding and generation services
+openai==1.54.3
+
+# Streamlit for web UI
+streamlit
+
+# PDF handling (for PDF to text conversion)
+PyMuPDF
+
+# azure portal
+azure.storage.blob
+
+transformers
+
+chardet