Add application file

app.py

@@ -1,12 +1,7 @@
-from __future__ import annotations
-
 from transformers import AutoModelForCausalLM, AutoTokenizer
 from PIL import Image
 import gradio as gr
-from typing import Iterable
-from gradio.themes.base import Base
-from gradio.themes.utils import colors, fonts, sizes
-import time
+import numpy as np
 
 # Load the model and tokenizer
 model_id = "vikhyatk/moondream2"
@@ -16,76 +11,24 @@ model = AutoModelForCausalLM.from_pretrained(
 )
 tokenizer = AutoTokenizer.from_pretrained(model_id, revision=revision)
 
-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 f"This is a placeholder answer for {name} with count {count}."
-
-class Seafoam(Base):
-    def __init__(
-        self,
-        *,
-        primary_hue: colors.Color | str = colors.emerald,
-        secondary_hue: colors.Color | str = colors.blue,
-        neutral_hue: colors.Color | str = colors.blue,
-        spacing_size: sizes.Size | str = sizes.spacing_md,
-        radius_size: sizes.Size | str = sizes.radius_md,
-        text_size: sizes.Size | str = sizes.text_lg,
-        font: fonts.Font
-        | str
-        | Iterable[fonts.Font | str] = (
-            fonts.GoogleFont("Quicksand"),
-            "ui-sans-serif",
-            "sans-serif",
-        ),
-        font_mono: fonts.Font
-        | str
-        | Iterable[fonts.Font | str] = (
-            fonts.GoogleFont("IBM Plex Mono"),
-            "ui-monospace",
-            "monospace",
-        ),
-    ):
-        super().__init__(
-            primary_hue=primary_hue,
-            secondary_hue=secondary_hue,
-            neutral_hue=neutral_hue,
-            spacing_size=spacing_size,
-            radius_size=radius_size,
-            text_size=text_size,
-            font=font,
-            font_mono=font_mono,
-        )
-        super().set(
-            body_background_fill="repeating-linear-gradient(45deg, *primary_200, *primary_200 10px, *primary_50 10px, *primary_50 20px)",
-            body_background_fill_dark="repeating-linear-gradient(45deg, *primary_800, *primary_800 10px, *primary_900 10px, *primary_900 20px)",
-            button_primary_background_fill="linear-gradient(90deg, *primary_300, *secondary_400)",
-            button_primary_background_fill_hover="linear-gradient(90deg, *primary_200, *secondary_300)",
-            button_primary_text_color="white",
-            button_primary_background_fill_dark="linear-gradient(90deg, *primary_600, *secondary_800)",
-            slider_color="*secondary_300",
-            slider_color_dark="*secondary_600",
-            block_title_text_weight="600",
-            block_border_width="3px",
-            block_shadow="*shadow_drop_lg",
-            button_shadow="*shadow_drop_lg",
-            button_large_padding="32px",
-        )
-
-
-seafoam = Seafoam()
-
-with gr.Blocks(theme=seafoam) as demo:
-    with gr.Row():
-        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("Submit")
-        clear_button = gr.Button("Clear")
-    output = gr.Textbox(label="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()
+def analyze_image_direct(image, question):
+    # Convert PIL Image to the format expected by the model
+    # Note: This step depends on the model's expected input format
+    # For demonstration, assuming the model accepts PIL images directly
+    enc_image = model.encode_image(image)  # This method might not exist; adjust based on actual model capabilities
+    
+    # Generate an answer to the question based on the encoded image
+    # Note: This step is hypothetical and depends on the model's capabilities
+    answer = model.answer_question(enc_image, question, tokenizer)  # Adjust based on actual model capabilities
+    
+    return answer
+
+# Create Gradio interface
+iface = gr.Interface(fn=analyze_image_direct,
+                     inputs=[gr.Image(type="pil"), gr.Textbox(lines=2, placeholder="Enter your question here...")],
+                     outputs='text',
+                     title="Direct Image Question Answering",
+                     description="Upload an image and ask a question about it directly using the model.")
+
+# Launch the interface
+iface.launch()