app.py

import streamlit as st
import openai

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

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

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

# 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!")

# 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 userinput and api_key and st.button("Extract Claims",key="claims_extraction",disabled=disable_button_bool):

    # Display status message
    learning_status_placeholder.text("Generating learning objectives...")
    # API call to generate objectives
    claims_extraction_response = openai.ChatCompletion.create(
        model=model_choice,
        messages=[
            {"role": "user", "content": f"Extract any patentable claims from the following: \n {userinput}. \n extract each claim. Briefly explain why you extracted this wordphrase. Exclude any additional commentary."}
        ]
    )
    # Extract the generated objectives from the API response
    claims_extraction=claims_extraction_response['choices'][0]['message']['content']
    
    # Save generated objectives to session state
    # st.session_state.claims_extraction = claims_extraction.strip()
    
    # Display generated objectives
    learning_status_placeholder.text(f"Patentable Claims Extracted!\n{claims_extraction.strip()}")

# Generate Lesson Plan Button
if st.button("Extract Claims") and api_key:
    
    # Construct the prompt as a dictionary
    prompt_dict = {
        "context": context,
        "userinput": userinput,
        "claims_extraction": claims_extraction.strip(),  # Use the claims_extraction variable
        "tasks": [
            {"task": "Extract Claims", "objective": "extract any wordphrases in the text provided that could be considered a patentable claim"},
            {"task": "Extract Every Claim", "objective": "Ensure each and every wordphrase with a claim is evaluated whether or not it is patentable"},
            {"task": "Explain Your Choice", "objective": "Briefly explain why you have retained each claim"},
            {"task": "Check Your Work", "objective": "Check your work to assure you have not forgotten any claims"}
        ],
        "output_format": """Present in a structured format.
        \nClaim: 
        \nExplanation:
        \nNotes:
        
        """
    }

    
    # Convert the dictionary to a string
    prompt_str = str(prompt_dict)

    # API call to generate the lesson plan
    lesson_plan_response = openai.ChatCompletion.create(
        model=model_choice,
        messages=[
            {"role": "user", "content": f"Create a lesson plan based on the following parameters: {prompt_str}"}
        ]
    )

    # Display status message
    lesson_plan=st.text("Extracting Patentable Claims...")

    # Extract and display
    assistant_reply = claims_extraction_response['choices'][0]['message']['content']
    claims_extraction=st.text(assistant_reply.strip())

# Citation 
st.markdown("<sub>This app was created by [Tonic](https://huggingface.co/tonic)</sub>", unsafe_allow_html=True)