import pandas as pd import streamlit as st from langchain import PromptTemplate, HuggingFaceHub, LLMChain from langchain.llms import OpenAI from transformers import AutoTokenizer, AutoModelForSequenceClassification import os import re def extract_positive_negative(text): pattern = r'\b(?:positive|negative)\b' result = re.findall(pattern, text) return result def classify_text(text, llm_chain, api): if api == "HuggingFace": classification = llm_chain.run(str(text)) elif api == "OpenAI": classification = llm_chain.run(str(text)) classification = re.sub(r'\s', '', classification) return classification.lower() def classify_csv(df, llm_chain, api): df["label_gold"] = df["label"] del df["label"] df["label_pred"] = df["text"].apply(classify_text, llm_chain=llm_chain, api=api) return df def classify_csv_zero(zero_file, llm_chain, api): df = pd.read_csv(zero_file, sep=';') df["label"] = df["text"].apply(classify_text, llm_chain=llm_chain, api=api) return df def evaluate_performance(df): merged_df = df correct_preds = sum(merged_df["label_gold"] == merged_df["label_pred"]) total_preds = len(merged_df) percentage_overlap = correct_preds / total_preds * 100 return percentage_overlap def display_home(): st.write("Please select an API and a model to classify the text. We currently support HuggingFace and OpenAI.") api = st.selectbox("Select an API", ["HuggingFace", "OpenAI"]) if api == "HuggingFace": model = st.selectbox("Select a model", ["google/flan-t5-xl", "databricks/dolly-v1-6b"]) api_key_hug = st.text_input("HuggingFace API Key") elif api == "OpenAI": model = None api_key_openai = st.text_input("OpenAI API Key") st.write("Please select a temperature for the model. The higher the temperature, the more creative the model will be.") temperature = st.slider("Set the temperature", min_value=0.0, max_value=1.0, value=0.0, step=0.01) st.write("We provide two different setups for the annotation task. In the first setup (**Test**), you can upload a CSV file with gold labels and evaluate the performance of the model. In the second setup (**Zero-Shot**), you can upload a CSV file without gold labels and use the model to classify the text.") setup = st.selectbox("Setup", ["Test", "Zero-Shot"]) if setup == "Test": gold_file = st.file_uploader("Upload Gold Labels CSV file with a text and a label column", type=["csv"]) elif setup == "Zero-Shot": gold_file = None zero_file = st.file_uploader("Upload CSV file with a text column", type=["csv"]) st.write("Please enter the prompt template below. You can use the following variables: {text} (text to classify).") prompt_template = st.text_area("Enter your task description", """Instruction: Identify the sentiment of a text. Please read the text and provide one of these responses: "positive" or "negative".\nText to classify in "positive" or "negative": {text}\nAnswer:""", height=200) classify_button = st.button("Run Classification/ Annotation") if classify_button: if prompt_template: prompt = PromptTemplate( template=prompt_template, input_variables=["text"] ) if api == "HuggingFace": if api_key_hug: os.environ["HUGGINGFACEHUB_API_TOKEN"] = api_key_hug llm_chain = LLMChain(prompt=prompt, llm=HuggingFaceHub(repo_id=model, model_kwargs={"temperature": temperature, "max_length": 128})) elif not api_key_hug: st.warning("Please enter your HuggingFace API key to classify the text.") elif api == "OpenAI": if api_key_openai: os.environ["OPENAI_API_KEY"] = api_key_openai llm_chain = LLMChain(prompt=prompt, llm=OpenAI(temperature=temperature)) elif not api_key_openai: st.warning("Please enter your OpenAI API key to classify the text.") if setup == "Zero-Shot": if zero_file is not None: df_predicted = classify_csv_zero(zero_file, llm_chain, api) st.write(df_predicted) st.download_button( label="Download CSV", data=df_predicted.to_csv(index=False), file_name="classified_zero-shot_data.csv", mime="text/csv" ) elif setup == "Test": if gold_file is not None: df = pd.read_csv(gold_file, sep=';') if "label" not in df.columns: st.warning("Please make sure that the gold labels CSV file contains a column named 'label'.") else: df = classify_csv(df, llm_chain, api) st.write(df) st.download_button( label="Download CSV", data=df.to_csv(index=False), file_name="classified_test_data.csv", mime="text/csv" ) percentage_overlap = evaluate_performance(df) st.write("**Performance Evaluation**") st.write(f"Percentage overlap between gold labels and predicted labels: {percentage_overlap:.2f}%") elif gold_file is None: st.warning("Please upload a gold labels CSV file to evaluate the performance of the model.") elif not prompt: st.warning("Please enter a prompt question to classify the text.") def main(): st.set_page_config(page_title="PromptCards Playground", page_icon=":pencil2:") st.title("AInnotator") # add a menu to the sidebar if "current_page" not in st.session_state: st.session_state.current_page = "homepage" # Initialize selected_prompt in session_state if not set if "selected_prompt" not in st.session_state: st.session_state.selected_prompt = "" # Add a menu menu = ["Homepage", "Playground", "Prompt Archive", "Annotator", "About"] st.sidebar.title("About") st.sidebar.write("AInnotator 🤖🏷️ is a tool for creating artificial labels/ annotations. It is based on the concept of PromptCards, which are small, self-contained descriptions of a task that can be used to generate labels for a wide range of NLP tasks. Check out the GitHub repository and the PromptCards Archive for more information.") st.sidebar.write("---") st.sidebar.write("Check out the [PromptCards archive]() to find a wide range of prompts for different NLP tasks.") st.sidebar.write("---") st.sidebar.write("Made with ❤️ and 🤖.") display_home() if __name__ == "__main__": main()