Add application file

app.py

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+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"
+revision = "2024-05-20"
+model = AutoModelForCausalLM.from_pretrained(
+    model_id, trust_remote_code=True, revision=revision
+)
+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
+
+# 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()

requirements.txt

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+opencv-python-headless
+datasets
+transformers 
+accelerate 
+evaluate  
+bitsandbytes 
+accelerate 
+einops
+Pillow
+torch
+torchvision