fix: default values

app.py

@@ -85,12 +85,12 @@ def display_streamlit_sidebar():
 
     model_name_or_path = form.selectbox("Select model", gptq_model_options())
     
-    temperature = form.slider(label="Temperature", min_value=0.0, max_value=1.0, step=0.01, value=0.01)
-    do_sample = form.checkbox('do_sample')
-    top_p = form.slider(label="top_p", min_value=0.0, max_value=1.0, step=0.01, value=0.95)
-    top_k = form.slider(label="top_k", min_value=1, max_value=1000, step=1, value=40)
-    max_new_tokens = form.slider(label="max_new_tokens", min_value=32, max_value=512, step=1, value=32)
-    repetition_penalty = form.slider(label="repetition_penalty", min_value=0.0, max_value=1.0, step=0.01, value=0.95)
+    temperature = form.slider(label="Temperature", min_value=0.0, max_value=1.0, step=0.01, value=st.session_state["temperature"])
+    do_sample = form.checkbox('do_sample', value=st.session_state["do_sample"])
+    top_p = form.slider(label="top_p", min_value=0.0, max_value=1.0, step=0.01, value=st.session_state["top_p"])
+    top_k = form.slider(label="top_k", min_value=1, max_value=1000, step=1, value=st.session_state["top_k"])
+    max_new_tokens = form.slider(label="max_new_tokens", min_value=32, max_value=512, step=1, value=st.session_state["max_new_tokens"])
+    repetition_penalty = form.slider(label="repetition_penalty", min_value=0.0, max_value=5.0, step=0.01, value=st.session_state["repetition_penalty"])
     
     temperature = form.slider('Temperature (0 = deterministic, 1 = more freedom)', min_value=0.0,
                               max_value=1.0, value=st.session_state['temperature'], step=0.1)
@@ -115,74 +115,6 @@ def display_streamlit_sidebar():
         st.session_state["repetition_penalty"] = repetition_penalty
 
         st.rerun()
-
-
-def to_str(text):
-    res = str(text)
-    if res == "nan":
-        return " "
-    return " " + res
-
-
-def set_df_prompts(path, sheet_name):
-    df_prompts = pd.read_excel(path, sheet_name, header=None)
-    for i in range(3, df_prompts.shape[0]):
-        df_prompts.iloc[2] += df_prompts.iloc[i].apply(to_str)
-
-    df_prompts = df_prompts.T
-    df_prompts = df_prompts[[0, 1, 2]]
-    df_prompts[0] = df_prompts[0].astype(str)
-    df_prompts[1] = df_prompts[1].astype(str)
-    df_prompts[2] = df_prompts[2].astype(str)
-
-    df_prompts.columns = ["Questionnaire", "Used Guideline", "Prompt"]
-    df_prompts = df_prompts[1:]
-    st.session_state["df_prompts"] = df_prompts
-
-
-def handle_nbq_click(c):
-    question_without_source = re.sub(r'\[.*\]', '', c)
-    question_without_source = question_without_source.strip()
-    st.session_state['doctor_question'] = question_without_source
-
-
-def get_doctor_question_value():
-    if 'doctor_question' in st.session_state:
-        return st.session_state['doctor_question']
-
-    return ''
-
-
-def update_chat_history(dr_question, patient_reply):
-    print("update_chat_history" + str(dr_question) + " - " + str(patient_reply) + '...\n')
-    if dr_question is not None:
-        dr_msg = {
-            "role": "Doctor",
-            "content": dr_question
-        }
-        st.session_state["chat_history_array"].append(dr_msg)
-
-    if patient_reply is not None:
-        patient_msg = {
-            "role": "Patient",
-            "content": patient_reply
-        }
-        st.session_state["chat_history_array"].append(patient_msg)
-
-    return st.session_state["chat_history_array"]
-
-
-def get_chat_history_string(chat_history):
-    res = ''
-    for i in chat_history:
-        if i["role"] == "Doctor":
-            res += '**Doctor**: ' + str(i["content"].strip()) + "  \n  "
-        elif i["role"] == "Patient":
-            res += '**Patient**: ' + str(i["content"].strip()) + "  \n\n  "
-        else:
-            raise Exception('Unknown role: ' + str(i["role"]))
-
-    return res
       
 
 def init_session_state():
@@ -202,9 +134,6 @@ def get_genders():
     return ['Male', 'Female']
 
 
-
-
-
 def display_session_overview():
     st.subheader('History of LLM queries')
     st.write(st.session_state["llm_messages"])