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import streamlit as st | |
# Import the LangChain library | |
import langchain | |
# Load the AI model | |
model = langchain.load_model("model.pkl") | |
# Create a function to get the feedback from the AI model | |
def get_feedback(statement): | |
# Get the predictions from the AI model | |
predictions = model.predict(statement) | |
# Create a list of feedback | |
feedback = [] | |
for prediction in predictions: | |
feedback.append(prediction["feedback"]) | |
return feedback | |
# Create a function to display the feedback | |
def display_feedback(statement): | |
# Get the feedback from the AI model | |
feedback = get_feedback(statement) | |
# Display the feedback to the user | |
st.write("Here is the feedback from the AI model:") | |
st.write(feedback) | |
# Create a main function | |
def main(): | |
# Get the personal statement from the user | |
statement = st.text_input("Enter your personal statement:") | |
# Display the feedback to the user | |
display_feedback(statement) | |
# Run the main function | |
if __name__ == "__main__": | |
main() | |
# print("Start!") | |
# load_dotenv(find_dotenv()) | |
# # pinecone.init(api_key=os.getenv("PINECONE_API_KEY"), environment=os.getenv("PINECONE_ENVIRONMENT")) | |
# dataset_path = "./dataset.txt" | |
# loader = TextLoader(dataset_path) | |
# comments = loader.load_and_split() | |
# embeddings = OpenAIEmbeddings(model_name="ada") | |
# vectordb = Chroma.from_documents(comments, embedding=embeddings, persist_directory=".") | |
# vectordb.persist() | |
# memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True) | |
# # Assuming that GPT-4 is used for grammar, structure, and fact-checking | |
# # and Claude is used for providing tips and encouraging students to do their own research | |
# grammar_llm = OpenAI(temperature=0.8) | |
# tips_llm = Claude(temperature=0.8) | |
# grammar_qa = ConversationalRetrievalChain.from_llm(grammar_llm, vectordb.as_retriever(), memory=memory) | |
# tips_qa = ConversationalRetrievalChain.from_llm(tips_llm, vectordb.as_retriever(), memory=memory) | |
# st.title('AI Statement Reviewer') | |
# user_input = st.text_area("Enter your personal statement here:") | |
# if st.button('Get feedback'): | |
# grammar_result = grammar_qa({"question": user_input}) | |
# tips_result = tips_qa({"question": user_input}) | |
# st.write("Grammar and Structure Feedback:") | |
# st.write(grammar_result["answer"]) | |
# st.write("Tips and Recommendations:") | |
# st.write(tips_result["answer"]) | |