Super-squash branch 'main' using huggingface_hub

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README.md

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+---
+title: Granite Vision 3.1 2B
+emoji: 👀
+colorFrom: indigo
+colorTo: green
+sdk: gradio
+sdk_version: 5.15.0
+app_file: app.py
+pinned: false
+license: apache-2.0
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import random
+import torch
+import gradio as gr
+from transformers import LlavaNextProcessor, LlavaNextForConditionalGeneration
+
+model_path = "ibm-granite/granite-vision-3.1-2b-preview"
+processor = LlavaNextProcessor.from_pretrained(model_path, use_fast=True)
+model = LlavaNextForConditionalGeneration.from_pretrained(model_path, torch_dtype="auto", device_map="auto")
+
+def get_text_from_content(content):
+    texts = []
+    for item in content:
+        if item["type"] == "text":
+            texts.append(item["text"])
+        elif item["type"] == "image":
+            texts.append("[Image]")
+    return " ".join(texts)
+
+def chat_inference(image, text, temperature, top_p, top_k, max_tokens, conversation):
+    if conversation is None:
+        conversation = []
+        
+    user_content = []
+    if image is not None:
+        user_content.append({"type": "image", "image": image})
+    if text and text.strip():
+        user_content.append({"type": "text", "text": text.strip()})
+    if not user_content:
+        return conversation_display(conversation), conversation
+
+    conversation.append({
+        "role": "user",
+        "content": user_content
+    })
+
+    inputs = processor.apply_chat_template(
+        conversation,
+        add_generation_prompt=True,
+        tokenize=True,
+        return_dict=True,
+        return_tensors="pt"
+    ).to("cpu")
+
+    torch.manual_seed(random.randint(0, 10000))
+
+    generation_kwargs = {
+        "max_new_tokens": max_tokens,
+        "temperature": temperature,
+        "top_p": top_p,
+        "top_k": top_k,
+        "do_sample": True,
+    }
+
+    output = model.generate(**inputs, **generation_kwargs)
+    assistant_response = processor.decode(output[0], skip_special_tokens=True)
+
+    conversation.append({
+        "role": "assistant",
+        "content": [{"type": "text", "text": assistant_response.strip()}]
+    })
+    
+    return conversation_display(conversation), conversation
+
+def conversation_display(conversation):
+    chat_history = []
+    for msg in conversation:
+        if msg["role"] == "user":
+            user_text = get_text_from_content(msg["content"])
+        elif msg["role"] == "assistant":
+            assistant_text = msg["content"][0]["text"].split("<|assistant|>")[-1].strip()
+            chat_history.append({"role": "user", "content": user_text})
+            chat_history.append({"role": "assistant", "content": assistant_text})
+    return chat_history
+
+def clear_chat():
+    return [], [], "", None
+    
+with gr.Blocks(title="Granite Vision 3.1 2B", css="h1 { overflow: hidden; }") as demo:
+    gr.Markdown("# Granite Vision 3.1 2B")
+    
+    with gr.Row():
+        with gr.Column(scale=2):
+            image_input = gr.Image(type="pil", label="Upload Image (optional)")
+            with gr.Column():
+                temperature_input = gr.Slider(minimum=0.0, maximum=2.0, value=0.2, step=0.01, label="Temperature")
+                top_p_input = gr.Slider(minimum=0.0, maximum=1.0, value=0.95, step=0.01, label="Top p")
+                top_k_input = gr.Slider(minimum=0, maximum=100, value=50, step=1, label="Top k")
+                max_tokens_input = gr.Slider(minimum=10, maximum=300, value=128, step=1, label="Max Tokens")
+            
+        with gr.Column(scale=3):
+            chatbot = gr.Chatbot(label="Chat History", elem_id="chatbot", type='messages')
+            text_input = gr.Textbox(lines=2, placeholder="Enter your message here", label="Message")
+            with gr.Row():
+                send_button = gr.Button("Chat")
+                clear_button = gr.Button("Clear Chat")
+    
+
+    state = gr.State([])
+
+    send_button.click(
+        chat_inference,
+        inputs=[image_input, text_input, temperature_input, top_p_input, top_k_input, max_tokens_input, state],
+        outputs=[chatbot, state]
+    )
+
+    clear_button.click(
+        clear_chat,
+        inputs=None,
+        outputs=[chatbot, state, text_input, image_input]
+    )
+
+    gr.Examples(
+        examples=[
+            ["https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/bus.png", "What is this?"]
+        ],
+        inputs=[image_input, text_input]
+    )
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+torch
+torchvision
+git+https://github.com/huggingface/transformers.git
+gradio
+accelerate
+bitsandbytes