app.py

import streamlit as st
import gradio as gr
import numpy as np
import whisper
import os
import streamlit.components.v1 as components
import tempfile
import io
import requests
import json
import openai

# Initialize session state for claims_extraction
if 'claims_extraction' not in st.session_state:
    st.session_state.claims_extraction = ""

# Initialize session state for userinput
if 'userinput' not in st.session_state:
    st.session_state.userinput = ""  # Initialize user input

# Define a function to split text into chunks
def chunk_text(text, chunk_size=2000):
    chunks = []
    start = 0
    while start < len(text):
        end = start + chunk_size
        chunk = text[start:end]
        chunks.append(chunk)
        start = end
    return chunks

# Streamlit Session State
if 'learning_objectives' not in st.session_state:
    st.session_state.learning_objectives = ""

# Initialize the Whisper model outside the button
if 'whisper_model' not in st.session_state:
    st.session_state.whisper_model = whisper.load_model("base")

# Streamlit User Input Form
st.title("Patent Claims Extraction")

# API Key Input
api_key = st.text_input("Enter your OpenAI API Key:", type="password")

# Audio Upload
audio_file = st.file_uploader("Upload an audio file", type=["mp3", "wav", "ogg"])
audio_data = None

if audio_file is not None:
    audio_data = audio_file.read()

# Moved the submit_button check here
if st.button('Start Transcription'):
    model = st.session_state.whisper_model

    if audio_data:
        with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as audio_file:
            audio_file.write(audio_data)
            audio_file_path = audio_file.name
        st.audio(audio_file_path, format="audio/wav")
        st.info("Transcribing...")
        st.success("Transcription complete")
        result = model.transcribe(audio_file_path)
        st.session_state.transcript = result['text']

        with st.expander("See transcript"):
            st.markdown(transcript)

        # Update the user input field with the transcription
        st.session_state.userinput = st.text_area("Input Text:", transcript)

# Model Selection Dropdown
model_choice = st.selectbox(
    "Select the model you want to use:",
    ["gpt-3.5-turbo-0301", "gpt-3.5-turbo-0613", "gpt-3.5-turbo", "gpt-4-0314", "gpt-4-0613", "gpt-4"]
)

# Context, Subject, and Level
context = "You are a patent claims identifier and extractor. You will freeform text, identify any claims contained therein that may be patentable. You identify, extract, print such claims, briefly explain why each claim is patentable."
# userinput = st.text_input("Input Text:", "Freeform text here!")  # Commented out, as it's updated above

# Initialize OpenAI API
if api_key:
    openai.api_key = api_key

# Learning Objectives
st.write("### Patentable Claims:")
# Initialize autogenerated objectives
claims_extraction = ""
# Initialize status placeholder
learning_status_placeholder = st.empty()
disable_button_bool = False

if st.session_state.userinput and api_key and st.button("Extract Claims", key="claims_extraction", disabled=disable_button_bool):
    # Split the user input into chunks
    input_chunks = chunk_text(userinput)

    # Initialize a variable to store the extracted claims
    all_extracted_claims = ""

    for chunk in input_chunks:
        # Display status message for the current chunk
        learning_status_placeholder.text(f"Extracting Patentable Claims for chunk {input_chunks.index(chunk) + 1}...")

        # API call to generate objectives for the current chunk
        claims_extraction_response = openai.ChatCompletion.create(
            model=model_choice,
            messages=[
                {"role": "user", "content": f"Extract any patentable claims from the following: \n {chunk}. \n Extract each claim. Briefly explain why you extracted this word phrase. Exclude any additional commentary."}
            ]
        )

        # Extract the generated objectives from the API response
        claims_extraction = claims_extraction_response['choices'][0]['message']['content']

        # Append the extracted claims from the current chunk to the overall results
        all_extracted_claims += claims_extraction.strip()

    # Save the generated objectives to session state
    st.session_state.claims_extraction = all_extracted_claims

    # Display generated objectives for all chunks
    learning_status_placeholder.text(f"Patentable Claims Extracted!\n{all_extracted_claims.strip()}")


# Get the extracted claims from Streamlit's session state
claims_extracted = st.session_state.claims_extraction

from transformers import AutoConfig, AutoTokenizer, AutoModel
from summarizer import Summarizer

# Define the BERT-based model name
model_name = 'nlpaueb/legal-bert-base-uncased'

# Initialize BERT-based model and tokenizer
custom_config = AutoConfig.from_pretrained(model_name)
custom_config.output_hidden_states = True
custom_tokenizer = AutoTokenizer.from_pretrained(model_name)
custom_model = AutoModel.from_pretrained(model_name, config=custom_config)
bert_legal_model = Summarizer(custom_model=custom_model, custom_tokenizer=custom_tokenizer)
print('Using model {}\n'.format(model_name))

# Get the extracted claims from Streamlit's session state
claims_extracted = st.session_state.claims_extraction

# Define the chunk size
chunk_size = 350

# Split the extracted claims into chunks
if isinstance(claims_extracted, str):
    chunks = [claims_extracted[i:i+chunk_size] for i in range(0, len(claims_extracted), chunk_size)]
else:
    chunks = []

# Process each chunk with the BERT-based model
summaries = []
for chunk in chunks:
    summary = bert_legal_model(chunk, min_length=8, ratio=0.05)
    summaries.append(summary)

# Now you have a list of summaries for each chunk
# You can access them using `summaries[0]`, `summaries[1]`, etc.
# After generating summaries
for i, summary in enumerate(summaries):
    st.write(f"### Summary {i+1}")
    st.write(summary)