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app.py

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+import gradio as gr
+from transformers import AutoProcessor, Idefics3ForConditionalGeneration
+import re
+import time
+from PIL import Image
+import torch
+import spaces
+import subprocess
+subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
+
+processor = AutoProcessor.from_pretrained("HuggingFaceM4/Idefics3-8B-Llama3")
+model = Idefics3ForConditionalGeneration.from_pretrained("HuggingFaceM4/Idefics3-8B-Llama3",
+        torch_dtype=torch.bfloat16,
+        trust_remote_code=True).to("cuda")
+
+BAD_WORDS_IDS = processor.tokenizer(["<image>", "<fake_token_around_image>"], add_special_tokens=False).input_ids
+EOS_WORDS_IDS = [processor.tokenizer.eos_token_id]
+
+@spaces.GPU
+def model_inference(
+    images, text, assistant_prefix, decoding_strategy, temperature, max_new_tokens,
+    repetition_penalty, top_p
+):
+    if text == "" and not images:
+        gr.Error("Please input a query and optionally image(s).")
+
+    if text == "" and images:
+        gr.Error("Please input a text query along the image(s).")
+
+    # Check if the input is related to marketing
+    marketing_keywords = ["product", "brand", "advertisement", "marketing", "strategy", "comparison", "analysis", "trend", "audience"]
+    if not any(keyword in text.lower() for keyword in marketing_keywords):
+        return "Your question is not in the marketing area. Please upload another image or try again."
+
+    if isinstance(images, Image.Image):
+        images = [images]
+
+    resulting_messages = [
+                {
+                    "role": "user",
+                    "content": [{"type": "image"}] + [
+                        {"type": "text", "text": text}
+                    ]
+                }
+            ]
+
+    if assistant_prefix:
+      text = f"{assistant_prefix} {text}"
+
+    prompt = processor.apply_chat_template(resulting_messages, add_generation_prompt=True)
+    inputs = processor(text=prompt, images=[images], return_tensors="pt")
+    inputs = {k: v.to("cuda") for k, v in inputs.items()}
+
+    generation_args = {
+        "max_new_tokens": max_new_tokens,
+        "repetition_penalty": repetition_penalty,
+    }
+
+    assert decoding_strategy in [
+        "Greedy",
+        "Top P Sampling",
+    ]
+    if decoding_strategy == "Greedy":
+        generation_args["do_sample"] = False
+    elif decoding_strategy == "Top P Sampling":
+        generation_args["temperature"] = temperature
+        generation_args["do_sample"] = True
+        generation_args["top_p"] = top_p
+
+    generation_args.update(inputs)
+
+    # Generate
+    generated_ids = model.generate(**generation_args)
+
+    generated_texts = processor.batch_decode(generated_ids[:, generation_args["input_ids"].size(1):], skip_special_tokens=True)
+    return generated_texts[0]
+
+with gr.Blocks(fill_height=True) as demo:
+    gr.Markdown("## Marketing Vision App 📈")
+    gr.Markdown("This app uses the [HuggingFaceM4/Idefics3-8B-Llama3](https://huggingface.co/HuggingFaceM4/Idefics3-8B-Llama3) model to answer questions related to marketing. Upload an image and a text query, or try one of the examples.")
+    gr.Markdown("**Disclaimer:** This app may not consistently follow prompts or handle complex tasks. However, adding a prefix to the assistant's response can significantly improve the output. You could also play with the parameters such as the temperature in non-greedy mode.")
+    with gr.Column():
+        image_input = gr.Image(label="Upload your Image", type="pil", scale=1)
+        query_input = gr.Textbox(label="Prompt")
+        assistant_prefix = gr.Textbox(label="Assistant Prefix", placeholder="Let's think step by step.")
+
+        submit_btn = gr.Button("Submit")
+        output = gr.Textbox(label="Output")
+
+    with gr.Accordion(label="Example Inputs and Advanced Generation Parameters"):
+        examples=[
+            ["example_images/iphone_vs_samsung.jpg", "I want to buy a smartphone with features similar to the one in the photo. Suggest models and provide a comparison.", None, "Product Recommendation", 0.5, 768, 1.1, 0.85],
+            ["example_images/ad_analysis.png", "Analyze this advertisement and explain its marketing strategy.", None, "Advertisement Analysis", 0.5, 768, 1.1, 0.85],
+            ["example_images/market_trends.png", "Analyze this marketing chart and explain the market trends it represents.", None, "Market Trend Analysis", 0.5, 768, 1.1, 0.85],
+            ["example_images/social_media_post.png", "Critique this social media post about product quality and suggest improvements.", None, "Social Media Post Analysis", 0.5, 768, 1.1, 0.85],
+            ["example_images/brand_comparison.jpg", "Compare these three brands based on their price strategies.", None, "Brand Comparison", 0.5, 768, 1.1, 0.85],
+            ["example_images/target_audience.jpg", "Analyze this image and suggest a target audience for the product.", None, "Target Audience Analysis", 0.5, 768, 1.1, 0.85]
+        ]
+
+        # Hyper-parameters for generation
+        max_new_tokens = gr.Slider(
+              minimum=8,
+              maximum=1024,
+              value=512,
+              step=1,
+              interactive=True,
+              label="Maximum number of new tokens to generate",
+          )
+        repetition_penalty = gr.Slider(
+              minimum=0.01,
+              maximum=5.0,
+              value=1.2,
+              step=0.01,
+              interactive=True,
+              label="Repetition penalty",
+              info="1.0 is equivalent to no penalty",
+          )
+        temperature = gr.Slider(
+              minimum=0.0,
+              maximum=5.0,
+              value=0.4,
+              step=0.1,
+              interactive=True,
+              label="Sampling temperature",
+              info="Higher values will produce more diverse outputs.",
+          )
+        top_p = gr.Slider(
+              minimum=0.01,
+              maximum=0.99,
+              value=0.8,
+              step=0.01,
+              interactive=True,
+              label="Top P",
+              info="Higher values is equivalent to sampling more low-probability tokens.",
+          )
+        decoding_strategy = gr.Radio(
+              [
+                  "Greedy",
+                  "Top P Sampling",
+              ],
+              value="Greedy",
+              label="Decoding strategy",
+              interactive=True,
+              info="Higher values is equivalent to sampling more low-probability tokens.",
+          )
+        decoding_strategy.change(
+              fn=lambda selection: gr.Slider(
+                  visible=(
+                      selection in ["contrastive_sampling", "beam_sampling", "Top P Sampling", "sampling_top_k"]
+                  )
+              ),
+              inputs=decoding_strategy,
+              outputs=temperature,
+          )
+
+        decoding_strategy.change(
+              fn=lambda selection: gr.Slider(
+                  visible=(
+                      selection in ["contrastive_sampling", "beam_sampling", "Top P Sampling", "sampling_top_k"]
+                  )
+              ),
+              inputs=decoding_strategy,
+              outputs=repetition_penalty,
+          )
+        decoding_strategy.change(
+              fn=lambda selection: gr.Slider(visible=(selection in ["Top P Sampling"])),
+              inputs=decoding_strategy,
+              outputs=top_p,
+          )
+        gr.Examples(
+                        examples = examples,
+                        inputs=[image_input, query_input, assistant_prefix, decoding_strategy, temperature,
+                                                              max_new_tokens, repetition_penalty, top_p],
+                        outputs=output,
+                        fn=model_inference
+                    )
+
+        submit_btn.click(model_inference, inputs = [image_input, query_input, assistant_prefix, decoding_strategy, temperature,
+                                                      max_new_tokens, repetition_penalty, top_p], outputs=output)
+
+demo.launch(debug=True)

requirements.txt

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+torch
+accelerate
+huggingface_hub
+gradio
+git+https://github.com/andimarafioti/transformers.git@idefics3
+spaces