Add application file

app.py

@@ -1,7 +1,6 @@
 from transformers import AutoModelForCausalLM, AutoTokenizer
 from PIL import Image
 import gradio as gr
-import numpy as np
 
 # Load the model and tokenizer
 model_id = "vikhyatk/moondream2"
@@ -12,23 +11,25 @@ model = AutoModelForCausalLM.from_pretrained(
 tokenizer = AutoTokenizer.from_pretrained(model_id, revision=revision)
 
 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
+    # 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."
 
-# Create Gradio interface
+# Define custom CSS to make the interface purple
+custom_css = """
+body { background-color: #800080; }
+button { background-color: #9932CC; color: white; }
+textarea { background-color: #DDA0DD; color: black; }
+"""
+
+# Create Gradio interface with custom CSS for a purple theme
 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.")
+                     description="Upload an image and ask a question about it directly using the model.",
+                     theme="dark",  # Use the dark theme as a base
+                     css=custom_css)  # Apply custom CSS
 
 # Launch the interface
 iface.launch()