Update app.py

app.py

@@ -7,71 +7,6 @@ client = InferenceClient("Satyam-Singh/LLaVa-Large-Language-Virtual-Assistant")
 
 TITLE = """<h1 align="center">LLaVa Large Language Virtual Assistant</h1>"""
 
-temperature_component = gr.Slider(
-    minimum=0,
-    maximum=1.0,
-    value=0.4,
-    step=0.05,
-    label="Temperature",
-    info=(
-        "Temperature controls the degree of randomness in token selection. Lower "
-        "temperatures are good for prompts that expect a true or correct response, "
-        "while higher temperatures can lead to more diverse or unexpected results. "
-    ))
-max_output_tokens_component = gr.Slider(
-    minimum=1,
-    maximum=2048,
-    value=1024,
-    step=1,
-    label="Token limit",
-    info=(
-        "Token limit determines the maximum amount of text output from one prompt. A "
-        "token is approximately four characters. The default value is 2048."
-    ))
-stop_sequences_component = gr.Textbox(
-    label="Add stop sequence",
-    value="",
-    type="text",
-    placeholder="STOP, END",
-    info=(
-        "A stop sequence is a series of characters (including spaces) that stops "
-        "response generation if the model encounters it. The sequence is not included "
-        "as part of the response. You can add up to five stop sequences."
-    ))
-top_k_component = gr.Slider(
-    minimum=1,
-    maximum=40,
-    value=32,
-    step=1,
-    label="Top-K",
-    info=(
-        "Top-k changes how the model selects tokens for output. A top-k of 1 means the "
-        "selected token is the most probable among all tokens in the model’s "
-        "vocabulary (also called greedy decoding), while a top-k of 3 means that the "
-        "next token is selected from among the 3 most probable tokens (using "
-        "temperature)."
-    ))
-top_p_component = gr.Slider(
-    minimum=0,
-    maximum=1,
-    value=1,
-    step=0.01,
-    label="Top-P",
-    info=(
-        "Top-p changes how the model selects tokens for output. Tokens are selected "
-        "from most probable to least until the sum of their probabilities equals the "
-        "top-p value. For example, if tokens A, B, and C have a probability of .3, .2, "
-        "and .1 and the top-p value is .5, then the model will select either A or B as "
-        "the next token (using temperature). "
-    ))
-
-additional_inputs = [
-    temperature_component,
-    max_output_tokens_component,
-    stop_sequences_component,
-    top_k_component,
-    top_p_component,
-]
 
 safety_settings = [
   {
@@ -193,7 +128,6 @@ gr.ChatInterface(
                        ),
     title="LLAVA: Large Language Virtual Assistant",
     description="Official Demo Of ```LLAVA``` based on ```Large Language Virtual Assistant ```.",
-    additional_inputs=additional_inputs,
     examples=examples,
     concurrency_limit=20,
 ).launch(show_api=True)