Create app.py

app.py

@@ -0,0 +1,55 @@
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoProcessor, GenerationConfig
+from PIL import Image
+import requests
+
+# Load the processor
+processor = AutoProcessor.from_pretrained(
+    'allenai/Molmo-7B-D-0924',
+    trust_remote_code=True,
+    torch_dtype='auto',
+    device_map='auto'
+)
+
+# Load the model
+model = AutoModelForCausalLM.from_pretrained(
+    'allenai/Molmo-7B-D-0924',
+    trust_remote_code=True,
+    torch_dtype='auto',
+    device_map='auto'
+)
+
+def describe_image(image):
+    # Process the image and text
+    inputs = processor.process(
+        images=[image],
+        text='''an image of a human sitting properly , with a laptop/pc clearly visible and the student’s face at least 40%-50% visible. The student should be looking at the laptop screen with both hands on the keyboard. There should be no other accessories other than laptop/pc, and no other second person should be present ." // analyse image on this conditions // if all condition satisfied answer YES else NO// Answer only in YES or NO'''
+    )
+    
+    # Move inputs to the correct device and make a batch of size 1
+    inputs = {k: v.to(model.device).unsqueeze(0) for k, v in inputs.items()}
+
+    # Generate output; maximum 200 new tokens; stop generation when <|endoftext|> is generated
+    output = model.generate_from_batch(
+        inputs,
+        GenerationConfig(max_new_tokens=200, stop_strings="<|endoftext|>"),
+        tokenizer=processor.tokenizer
+    )
+
+    # Only get generated tokens; decode them to text
+    generated_tokens = output[0, inputs['input_ids'].size(1):]
+    generated_text = processor.tokenizer.decode(generated_tokens, skip_special_tokens=True)
+
+    return generated_text
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=describe_image,
+    inputs=gr.Image(type="pil", label="Upload an Image"),
+    outputs=gr.Textbox(label="Description"),
+    title="OPPE",
+    description="OPPE VERRFICATION."
+)
+
+# Launch the interface
+iface.launch()