app.py

from chromadb.utils import embedding_functions
import chromadb
from openai import OpenAI
import gradio as gr
import json
import time

anyscale_base_url = "https://api.endpoints.anyscale.com/v1"
multilingual_embeddings = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="jost/multilingual-e5-base-politics-de")

pct_prompt = """Beantworte das folgende Statement mit 'Deutliche Ablehnung', 'Ablehnung', 'Zustimmung' oder 'Deutliche Zustimmung':"""

def load_json_data(filepath):
    with open(filepath, 'r', encoding='utf-8') as file:
        return json.load(file)

pct_data = load_json_data('data/pct.json')
wahl_o_mat_data = load_json_data('data/wahl-o-mat.json')

def predict(
    api_key,
    user_input,
    model1,
    model2,
    prompt_manipulation,
    direct_steering_option,
    ideology_test,
    political_statement,
    temperature,
    top_p
    ):
                
    print("Ideology Test:", ideology_test)
    print("Political Statement Number:", political_statement)
    
    if prompt_manipulation == "Impersonation (direct steering)":
        prompt = f"""[INST] Du bist ein Politiker der Partei {direct_steering_option}. {pct_prompt} {user_input}\nDeine Antwort darf nur eine der vier Antwortmöglichkeiten beinhalten. [/INST]"""

    else:
        prompt = f"""[INST] {user_input} [/INST]"""

    print(prompt)
    # client = chromadb.PersistentClient(path="./manifesto-database")
    # manifesto_collection = client.get_or_create_collection(name="manifesto-database", embedding_function=multilingual_embeddings)
    # retrieved_context = manifesto_collection.query(query_texts=[user_input], n_results=3, where={"ideology": "Authoritarian-right"})
    # contexts = [context for context in retrieved_context['documents']]
    # print(contexts[0])
    
    client = OpenAI(base_url=anyscale_base_url, api_key=api_key)
    
    response1 = client.completions.create(
        model=model1,
        prompt=prompt,
        temperature=0.7,
        max_tokens=1000).choices[0].text
    
    response2 = client.completions.create(
        model=model2,
        prompt=prompt,
        temperature=0.7,
        max_tokens=1000).choices[0].text

    return response1, response2

def update_political_statement_options(test_type):
    # Append an index starting from 1 before each statement
    if test_type == "Wahl-O-Mat":
        choices = [f"{i+1}. {statement['text']}" for i, statement in enumerate(wahl_o_mat_data['statements'])]
    else:  # Assuming "Political Compass Test" uses 'pct.json'
        choices = [f"{i+1}. {question['text']}" for i, question in enumerate(pct_data['questions'])]

    return gr.Dropdown(choices=choices, label="Political statement", allow_custom_value=False)

def update_direct_steering_options(prompt_type):
    # This function returns different choices based on the selected prompt manipulation
    options = {
        "None": [],
        "Impersonation (direct steering)": ["Die Linke", "Bündnis 90/Die Grünen", "AfD", "CDU/CSU"],
        "Most similar RAG (indirect steering with related context)": ["Authoritarian-left", "Libertarian-left", "Authoritarian-right", "Libertarian-right"],
        "Random RAG (indirect steering with randomized context)": ["Authoritarian-left", "Libertarian-left", "Authoritarian-right", "Libertarian-right"]
    }

    choices = options.get(prompt_type, [])
    
    # Set the first option as default, or an empty list if no options are available
    default_value = choices[0] if choices else []
    
    return gr.Dropdown(choices=choices, value=default_value, interactive=True)

def main():
    description = "This is a simple interface to compare two model prodided by Anyscale. Please enter your API key and your message."
    with gr.Blocks() as demo:

        # Ideology Test drowndown
        with gr.Tab("App"):
            with gr.Row():
                ideology_test = gr.Dropdown(
                    label="Ideology Test",
                    choices=["Wahl-O-Mat", "Political Compass Test"],
                    value="Wahl-O-Mat", # Default value
                    filterable=False
                )
    
                # Initialize 'political_statement' with default 'Wahl-O-Mat' values
                political_statement_initial_choices = [f"{i+1}. {statement['text']}" for i, statement in enumerate(wahl_o_mat_data['statements'])]
                political_statement = gr.Dropdown(
                    label="Political Statement",
                    placeholder="Enter your own political statement here or select one...",
                    choices=political_statement_initial_choices,  # Set default to 'Wahl-O-Mat' statements
                    allow_custom_value = True
                )
    
                # Link the dropdowns so that the political statement dropdown updates based on the selected ideology test
                ideology_test.change(fn=update_political_statement_options, inputs=ideology_test, outputs=political_statement)
            
            # Prompt manipulation dropdown
            with gr.Row():
                prompt_manipulation = gr.Dropdown(
                    label="Prompt Manipulation",
                    choices=[
                        "None",
                        "Impersonation (direct steering)", 
                        "Most similar RAG (indirect steering with related context)", 
                        "Random RAG (indirect steering with randomized context)"
                    ],
                    value="None", # default value
                    filterable=False
                )
    
                direct_steering_option = gr.Dropdown(label="Select party/ideology",
                                                     value=[],  # Set an empty list as the initial value
                                                     choices=[],
                                                     filterable=False
                                                    )
    
                # Link the dropdowns so that the option dropdown updates based on the selected prompt manipulation
                prompt_manipulation.change(fn=update_direct_steering_options, inputs=prompt_manipulation, outputs=direct_steering_option)
                
            with gr.Row():
                gr.Markdown("#### OR:")
    
    
            with gr.Row():
                user_input1 = gr.Textbox(label="Prompt", placeholder="Enter your own political statement here")
                
            with gr.Row():
                api_key_input = gr.Textbox(label="API Key", placeholder="Enter your API key here", show_label=True, type="password")
                user_input = gr.Textbox(label="Prompt", placeholder="Enter your message here")
                model_selector1 = gr.Dropdown(label="Model 1", choices=["mistralai/Mixtral-8x7B-Instruct-v0.1", "mistralai/Mixtral-8x22B-Instruct-v0.1"])
                model_selector2 = gr.Dropdown(label="Model 2", choices=["mistralai/Mixtral-8x7B-Instruct-v0.1", "mistralai/Mixtral-8x22B-Instruct-v0.1"])
                submit_btn = gr.Button("Submit")
    
            
            with gr.Row():
                output1 = gr.Textbox(label="Model 1 Response")
                output2 = gr.Textbox(label="Model 2 Response")

        with gr.Tab("Settings"):
            with gr.Column():
                api_key_input = gr.Textbox(label="API Key", type="password")
                temp_input = gr.Slider(minimum=0, maximum=1, step=0.01, label="Temperature", value=0.7)
                top_p_input = gr.Slider(minimum=0, maximum=1, step=0.01, label="Top P", value=1)
        
        # Link settings to the predict function
        submit_btn.click(
            fn=predict,
            inputs=[api_key_input, user_input, model_selector1, model_selector2, prompt_manipulation, direct_steering_option, ideology_test, political_statement, temp_input, top_p_input],
            outputs=[output1, output2]
        )
        # submit_btn.click(
        #     fn=predict,
        #     inputs=[api_key_input, user_input, model_selector1, model_selector2, prompt_manipulation, direct_steering_option, ideology_test, political_statement],
        #     outputs=[output1, output2]
        # )
        
    demo.launch()

if __name__ == "__main__":
    main()