Update app.py

app.py

@@ -7,17 +7,29 @@ import time
 anyscale_base_url = "https://api.endpoints.anyscale.com/v1"
 multilingual_embeddings = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="jost/multilingual-e5-base-politics-de")
 
+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"]
+    }
+
+pct_prompt = """Beantworte das folgende Statement mit 'Deutliche Ablehnung', 'Ablehnung', 'Zustimmung' oder 'Deutliche Zustimmung':"""
+
 def predict(api_key, user_input, model1, model2, prompt_manipulation=None, direct_steering_option=None):
-    print(prompt_manipulation)
-    print(direct_steering_option)
+    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])
     
-    prompt = f"""[INST] {user_input} [/INST]"""
-    
     client = OpenAI(base_url=anyscale_base_url, api_key=api_key)
     
     response1 = client.completions.create(