Update app.py

app.py

@@ -1,63 +1,131 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
+from transformers import AutoModelForCausalLM, AutoProcessor, GenerationConfig
+from PIL import Image
+import torch
+import spaces
+
+# Flag to use GPU (set to False by default)
+USE_GPU = False
+
+# Load the processor and model
+device = torch.device("cuda" if USE_GPU and torch.cuda.is_available() else "cpu")
+
+processor = AutoProcessor.from_pretrained(
+    'allenai/Molmo-7B-D-0924',
+    trust_remote_code=True,
+    torch_dtype='auto',
+)
+
+model = AutoModelForCausalLM.from_pretrained(
+    'allenai/Molmo-7B-D-0924',
+    trust_remote_code=True,
+    torch_dtype='auto',
 )
 
+model.to(device)
+
+# Predefined prompts
+prompts = [
+    "Describe this image in detail",
+    "What objects can you see in this image?",
+    "What's the main subject of this image?",
+    "Describe the colors in this image",
+    "What emotions does this image evoke?"
+]
+
+def process_image_and_text(image, text, max_new_tokens, temperature, top_p):
+    # Process the image and text
+    inputs = processor.process(
+        images=[Image.fromarray(image)],
+        text=text
+    )
+
+    # Move inputs to the correct device and make a batch of size 1
+    inputs = {k: v.to(device).unsqueeze(0) for k, v in inputs.items()}
+
+    # Generate output
+    output = model.generate_from_batch(
+        inputs,
+        GenerationConfig(
+            max_new_tokens=max_new_tokens,
+            temperature=temperature,
+            top_p=top_p,
+            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
+
+def chatbot(image, text, history, max_new_tokens, temperature, top_p):
+    if image is None:
+        return history + [("Please upload an image first.", None)]
+
+    response = process_image_and_text(image, text, max_new_tokens, temperature, top_p)
+    history.append((text, response))
+    return history
+
+def update_textbox(prompt):
+    return gr.update(value=prompt)
+
+# Define the Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Image Chatbot with Molmo-7B-D-0924")
+    
+    with gr.Row():
+        image_input = gr.Image(type="numpy")
+        chatbot_output = gr.Chatbot()
+    
+    with gr.Row():
+        text_input = gr.Textbox(placeholder="Ask a question about the image...")
+        prompt_dropdown = gr.Dropdown(choices=[""] + prompts, label="Select a premade prompt", value="")
+    
+    submit_button = gr.Button("Submit")
+    clear_button = gr.ClearButton([text_input, chatbot_output])
+
+    with gr.Accordion("Advanced options", open=False):
+        max_new_tokens = gr.Slider(minimum=1, maximum=500, value=200, step=1, label="Max new tokens")
+        temperature = gr.Slider(minimum=0.1, maximum=2.0, value=1.0, step=0.1, label="Temperature")
+        top_p = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")
+
+    state = gr.State([])
+
+    # Add copy button for raw output
+    with gr.Row():
+        raw_output = gr.Textbox(label="Raw Output", interactive=False)
+        copy_button = gr.Button("Copy Raw Output")
+
+    def update_raw_output(history):
+        if history:
+            return history[-1][1]
+        return ""
+
+    submit_button.click(
+        chatbot,
+        inputs=[image_input, text_input, state, max_new_tokens, temperature, top_p],
+        outputs=[chatbot_output]
+    ).then(
+        update_raw_output,
+        inputs=[chatbot_output],
+        outputs=[raw_output]
+    )
+
+    text_input.submit(
+        chatbot,
+        inputs=[image_input, text_input, state, max_new_tokens, temperature, top_p],
+        outputs=[chatbot_output]
+    ).then(
+        update_raw_output,
+        inputs=[chatbot_output],
+        outputs=[raw_output]
+    )
+
+    prompt_dropdown.change(update_textbox, inputs=[prompt_dropdown], outputs=[text_input])
+    
+    copy_button.click(lambda x: gr.update(value=x), inputs=[raw_output], outputs=[gr.Textbox(visible=False)])
 
-if __name__ == "__main__":
-    demo.launch()
+demo.launch()