Create app.py

app.py

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+import torch
+import transformers
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from PIL import Image
+import requests
+import gradio as gr
+
+# Load model and tokenizer
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+model = AutoModelForCausalLM.from_pretrained(
+    'scb10x/llama-3-typhoon-v1.5-8b-instruct-vision-preview',
+    torch_dtype=torch.float16 if device == 'cuda' else torch.float32,
+    device_map='auto',
+    trust_remote_code=True
+).to(device)
+
+tokenizer = AutoTokenizer.from_pretrained(
+    'scb10x/llama-3-typhoon-v1.5-8b-instruct-vision-preview',
+    trust_remote_code=True
+)
+
+def prepare_inputs(text, image, device='cuda'):
+    messages = [
+        {"role": "system", "content": "You are a helpful vision-capable assistant who eagerly converses with the user in their language."},
+    ]
+    messages.append({"role": "user", "content": "<|image|>\n" + text})
+
+    inputs_formatted = tokenizer.apply_chat_template(
+        messages,
+        add_generation_prompt=True,
+        tokenize=False
+    )
+
+    text_chunks = [tokenizer(chunk).input_ids for chunk in inputs_formatted.split('<|image|>')]
+    input_ids = torch.tensor(text_chunks[0] + [-200] + text_chunks[1][1:], dtype=torch.long).unsqueeze(0).to(device)
+    attention_mask = torch.ones_like(input_ids).to(device)
+
+    return input_ids, attention_mask
+
+# Inference function
+def predict(prompt, img_url):
+    try:
+        image = Image.open(requests.get(img_url, stream=True).raw)
+        image_tensor = model.process_images([image], model.config).to(dtype=model.dtype, device=device)
+
+        input_ids, attention_mask = prepare_inputs(prompt, image, device=device)
+
+        output_ids = model.generate(
+            input_ids,
+            images=image_tensor,
+            max_new_tokens=100,
+            use_cache=True,
+            temperature=0.2,
+            top_p=0.2,
+            repetition_penalty=1.0
+        )[0]
+
+        result = tokenizer.decode(output_ids[input_ids.shape[1]:], skip_special_tokens=True).strip()
+        return result
+    except Exception as e:
+        return str(e)
+
+# Gradio Interface
+inputs = [
+    gr.Textbox(label="Prompt", placeholder="Ask about the food in the image"),
+    gr.Textbox(label="Image URL", placeholder="Enter an image URL")
+]
+
+outputs = gr.Textbox(label="Generated Output")
+
+gr.Interface(
+    fn=predict, inputs=inputs, outputs=outputs, title="Food Image AI Assistant",
+    description="This model can analyze food images and answer questions about them."
+).launch()