Add application file

app.py

@@ -1,6 +1,6 @@
-import gradio as gr
-from gradio import themes
 from transformers import AutoModelForCausalLM, AutoTokenizer
+from PIL import Image
+import gradio as gr
 import numpy as np
 
 # Load the model and tokenizer
@@ -13,34 +13,20 @@ tokenizer = AutoTokenizer.from_pretrained(model_id, revision=revision)
 
 def analyze_image_direct(image, question):
     # Convert PIL Image to the format expected by the model
-    # This is a placeholder transformation; adjust as needed
-    enc_image = np.array(image)
+    # 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
     
-    # Example of processing text input with the model
-    inputs = tokenizer(question, return_tensors='pt')
-    outputs = model.generate(**inputs, max_length=50)
-    answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    # 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 a Gradio interface
+with gr.Block() as block:
+    image = gr.inputs.Image(label="Image")
+    question = gr.inputs.Textbox(label="Question")
+    output = gr.outputs.Textbox(label="Answer")
+    gr.Interface(fn=analyze_image_direct, inputs=[image, question], outputs=output).launch()
 
-# Define a custom theme with purple color scheme
-class PurpleTheme(themes.Theme):
-    base = "light"
-    font = "Arial"
-    colors = {
-        "primary": "#9b59b6",
-        "text": "#FFFFFF",
-        "background": "#5B2C6F",
-        "secondary_background": "#7D3C98",
-    }
-
-# Create Gradio interface with the custom theme
-iface = gr.Interface(fn=analyze_image_direct,
-                     theme=PurpleTheme(),
-                     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()
+block.launch()