Add application file

app.py

@@ -4,7 +4,6 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
 from PIL import Image
 import gradio as gr
 from typing import Iterable
-import gradio as gr
 from gradio.themes.base import Base
 from gradio.themes.utils import colors, fonts, sizes
 import time
@@ -17,10 +16,10 @@ model = AutoModelForCausalLM.from_pretrained(
 )
 tokenizer = AutoTokenizer.from_pretrained(model_id, revision=revision)
 
-def analyze_image_direct(image, question):
+def analyze_image_direct(name, count):
     # This is a placeholder function. You need to implement the logic based on your model's capabilities.
     # For demonstration, it returns a static response.
-    return "This is a placeholder answer."
+    return f"This is a placeholder answer for {name} with count {count}."
 
 class Seafoam(Base):
     def __init__(
@@ -78,12 +77,15 @@ seafoam = Seafoam()
 
 with gr.Blocks(theme=seafoam) as demo:
     with gr.Row():
-        image_input = gr.Image(type="pil")
-        question_input = gr.Textbox(lines=2, placeholder="Enter your question here...")
+        name_input = gr.Textbox(label="Name", placeholder="Enter your name here...")
+    with gr.Row():
+        count_slider = gr.Slider(label="Count", minimum=0, maximum=100, step=1, value=0)
     with gr.Row():
-        submit_button = gr.Button("Analyze")
-    output = gr.Textbox()
+        submit_button = gr.Button("Submit")
+        clear_button = gr.Button("Clear")
+    output = gr.Textbox(label="Output")
 
-    submit_button.click(fn=analyze_image_direct, inputs=[image_input, question_input], outputs=output)
+    submit_button.click(fn=analyze_image_direct, inputs=[name_input, count_slider], outputs=output)
+    clear_button.click(fn=lambda: ("", 0, ""), inputs=None, outputs=[name_input, count_slider, output])
 
-demo.launch()
+demo.launch()