Create app.py

app.py

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+import gradio as gr
+import torch
+import transformers
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from PIL import Image
+import warnings
+
+# disable some warnings
+transformers.logging.set_verbosity_error()
+transformers.logging.disable_progress_bar()
+warnings.filterwarnings('ignore')
+
+# set device
+torch.set_default_device('cuda')  # or 'cpu'
+
+model_name = 'cognitivecomputations/dolphin-vision-7b'
+
+# create model
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.float16,
+    device_map='auto',
+    trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained(
+    model_name,
+    trust_remote_code=True)
+
+def inference(prompt, image):
+    messages = [
+        {"role": "user", "content": f'<image>\n{prompt}'}
+    ]
+    text = tokenizer.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=True
+    )
+
+    text_chunks = [tokenizer(chunk).input_ids for chunk in text.split('<image>')]
+    input_ids = torch.tensor(text_chunks[0] + [-200] + text_chunks[1], dtype=torch.long).unsqueeze(0)
+
+    image_tensor = model.process_images([image], model.config).to(dtype=model.dtype)
+
+    # generate
+    output_ids = model.generate(
+        input_ids,
+        images=image_tensor,
+        max_new_tokens=2048,
+        use_cache=True)[0]
+
+    return tokenizer.decode(output_ids[input_ids.shape[1]:], skip_special_tokens=True).strip()
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column():
+            prompt_input = gr.Textbox(label="Prompt", placeholder="Describe this image in detail")
+            image_input = gr.Image(label="Image", type="pil")
+            submit_button = gr.Button("Submit")
+        with gr.Column():
+            output_text = gr.Textbox(label="Output")
+
+    submit_button.click(fn=inference, inputs=[prompt_input, image_input], outputs=output_text)
+
+demo.launch()