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| 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() | |