Update app.py

app.py

@@ -55,7 +55,7 @@ def predict(
         
         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=num_contexts, where={"ideology": direct_steering_option})
+        retrieved_context = manifesto_collection.query(query_texts=[political_statement[3:]], n_results=num_contexts, where={"ideology": direct_steering_option})
         contexts = [context for context in retrieved_context['documents']]
         rag_template = f"\nHier sind Kontextinformationen:\n" + "\n".join([f"{context}" for context in contexts])
         
@@ -100,7 +100,7 @@ def predict(
         temperature=temperature,
         max_tokens=1000).choices[0].message.content
  
-    return response1, response2
+    return response1, response2, prompt
 
 def update_political_statement_options(test_type):
     # Append an index starting from 1 before each statement
@@ -192,6 +192,10 @@ def main():
                 output1 = gr.Textbox(label="Model 1 Response")
                 output2 = gr.Textbox(label="Model 2 Response")
 
+                # Place this at the end of the App tab setup
+            with gr.Collapsible(label="Additional Information", open=False):
+                prompt_display = gr.Textbox(label="Used Prompt", interactive=False, placeholder="Prompt used in the last submission will appear here.")
+
         with gr.Tab("Settings"):
             with gr.Row():
                 openai_api_key = gr.Textbox(label="OpenAI API Key", placeholder="Enter your OpenAI API key here", show_label=True, type="password")
@@ -199,14 +203,18 @@ def main():
 
             with gr.Row():
                 temp_input = gr.Slider(minimum=0, maximum=1, step=0.01, label="Temperature", value=0.7)
+                
+            with gr.Row():
                 top_p_input = gr.Slider(minimum=0, maximum=1, step=0.01, label="Top P", value=1)
+                
+            with gr.Row():
                 num_contexts = gr.Slider(minimum=0, maximum=1, step=0.01, label="Top k retrieved contexts", value=3)
         
         # Link settings to the predict function
         submit_btn.click(
             fn=predict,
             inputs=[openai_api_key, togetherai_api_key, model_selector1, model_selector2, prompt_manipulation, direct_steering_option, ideology_test, political_statement, temp_input, top_p_input, num_contexts],
-            outputs=[output1, output2]
+            outputs=[output1, output2, prompt_display]
         )
     
     demo.launch()