Update app.py

app.py

@@ -1,161 +1,161 @@
-import os
-import time
-import threading
-import gradio as gr
-import spaces
-import torch
-from PIL import Image
-from transformers import (
-    AutoModelForImageTextToText,
-    AutoProcessor,
-    TextIteratorStreamer,
-)
-from transformers.image_utils import load_image
-
-# Constants for text generation
-MAX_MAX_NEW_TOKENS = 4096
-DEFAULT_MAX_NEW_TOKENS = 1024
-MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))
-
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-
-# Load LFM2-VL-1.6B
-MODEL_ID_M = "LiquidAI/LFM2-VL-1.6B"
-processor_m = AutoProcessor.from_pretrained(MODEL_ID_M, trust_remote_code=True)
-model_m = AutoModelForImageTextToText.from_pretrained(
-    MODEL_ID_M,
-    trust_remote_code=True,
-    torch_dtype="bfloat16",
-).to(device).eval()
-
-# Load LFM2-VL-450M
-MODEL_ID_T = "LiquidAI/LFM2-VL-450M"
-processor_t = AutoProcessor.from_pretrained(MODEL_ID_T, trust_remote_code=True)
-model_t = AutoModelForImageTextToText.from_pretrained(
-    MODEL_ID_T,
-    trust_remote_code=True,
-    torch_dtype="bfloat16",
-).to(device).eval()
-
-@spaces.GPU
-def generate_image(model_name: str, text: str, image: Image.Image,
-                   max_new_tokens: int = 1024,
-                   temperature: float = 0.6,
-                   top_p: float = 0.9,
-                   top_k: int = 50,
-                   repetition_penalty: float = 1.2):
-    """
-    Generate responses using the selected model for image input.
-    """
-    if model_name == "LFM2-VL-1.6B":
-        processor = processor_m
-        model = model_m
-    elif model_name == "LFM2-VL-450M":
-        processor = processor_t
-        model = model_t
-    else:
-        yield "Invalid model selected.", "Invalid model selected."
-        return
-
-    if image is None:
-        yield "Please upload an image.", "Please upload an image."
-        return
-
-    messages = [{
-        "role": "user",
-        "content": [
-            {"type": "image", "image": image},
-            {"type": "text", "text": text},
-        ]
-    }]
-    prompt_full = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-    inputs = processor(
-        text=[prompt_full],
-        images=[image],
-        return_tensors="pt",
-        padding=True,
-        truncation=False,
-        max_length=MAX_INPUT_TOKEN_LENGTH
-    ).to(device)
-    streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
-    generation_kwargs = {**inputs, "streamer": streamer, "max_new_tokens": max_new_tokens}
-    thread = threading.Thread(target=model.generate, kwargs=generation_kwargs)
-    thread.start()
-    buffer = ""
-    for new_text in streamer:
-        buffer += new_text
-        time.sleep(0.01)
-        yield buffer, buffer
-
-# Define examples for image inference
-image_examples = [
-    ["According to this diagram, where do severe droughts occur?", "images/1.png"],
-    ["Could you describe this image?", "images/2.jpg"],
-    ["Provide a description of this image.", "images/3.jpg"],
-    ["Explain the movie shot in detail.", "images/4.png"],
-]
-
-# Updated CSS with model choice highlighting
-css = """
-.submit-btn {
-    background-color: #2980b9 !important;
-    color: white !important;
-}
-.submit-btn:hover {
-    background-color: #3498db !important;
-}
-.canvas-output {
-    border: 2px solid #4682B4;
-    border-radius: 10px;
-    padding: 20px;
-}
-"""
-
-# Create the Gradio Interface
-with gr.Blocks(css=css, theme=gr.themes.Soft()) as demo:
-    gr.Markdown("# **LFM2-VL by [LiquidAI](https://huggingface.co/collections/LiquidAI/lfm2-vl-68963bbc84a610f7638d5ffa)**")
-    with gr.Row():
-        with gr.Column():
-            image_query = gr.Textbox(label="Query Input", placeholder="✦︎ Enter your query")
-            image_upload = gr.Image(type="pil", label="Image")
-            image_submit = gr.Button("Submit", elem_classes="submit-btn")
-            gr.Examples(
-                examples=image_examples,
-                inputs=[image_query, image_upload]
-            )
-
-            with gr.Accordion("Advanced options", open=False):
-                max_new_tokens = gr.Slider(label="Max new tokens", minimum=1, maximum=MAX_MAX_NEW_TOKENS, step=1, value=DEFAULT_MAX_NEW_TOKENS)
-                temperature = gr.Slider(label="Temperature", minimum=0.1, maximum=4.0, step=0.1, value=0.6)
-                top_p = gr.Slider(label="Top-p (nucleus sampling)", minimum=0.05, maximum=1.0, step=0.05, value=0.9)
-                top_k = gr.Slider(label="Top-k", minimum=1, maximum=1000, step=1, value=50)
-                repetition_penalty = gr.Slider(label="Repetition penalty", minimum=1.0, maximum=2.0, step=0.05, value=1.2)
-
-        with gr.Column():
-            with gr.Column(elem_classes="canvas-output"):
-                gr.Markdown("## Output")
-                output = gr.Textbox(label="Raw Output Stream", interactive=False, lines=2)
-                with gr.Accordion("(Result.md)", open=False):                
-                    markdown_output = gr.Markdown(label="(Result.md)")
-
-            model_choice = gr.Dropdown(
-                choices=["LFM2-VL-1.6B", "LFM2-VL-450M"],
-                label="Select Model",
-                value="LFM2-VL-1.6B"
-            )
-
-            gr.Markdown("**Model Info 💻** | [Report Bug](https://huggingface.co/spaces/prithivMLmods/LFM2-VL-Demo/discussions)")
-            gr.Markdown("> [LFM2‑VL](https://huggingface.co/collections/LiquidAI/lfm2-vl-68963bbc84a610f7638d5ffa) is [Liquid AI’s](https://huggingface.co/LiquidAI) first multimodal model series, featuring models with 450M and 1.6B parameters designed for efficient processing of both text and images at native resolutions up to 512×512, ideal for low-latency edge AI applications; leveraging a hybrid conv+attention LFM2 backbone and SigLIP2 NaFlex vision encoders, it delivers flexible, user-tunable inference with rapid speeds (2× faster than existing VLMs on GPU)")
-            gr.Markdown("> Competitive accuracy, and dynamic image tokenization for scalable throughput, while supporting 32,768 text tokens and English language generation, and is best fine-tuned for targeted use cases using provided supervised fine-tuning tools, all released under the LFM Open License v1.0 for research and deployment scenarios not requiring safety-critical guarantees.")
-
-    # Define the submit button action
-    image_submit.click(fn=generate_image,
-                       inputs=[
-                           model_choice, image_query, image_upload,
-                           max_new_tokens, temperature, top_p, top_k,
-                           repetition_penalty
-                       ],
-                       outputs=[output, markdown_output])
-
-if __name__ == "__main__":
+import os
+import time
+import threading
+import gradio as gr
+import spaces
+import torch
+from PIL import Image
+from transformers import (
+    AutoModelForImageTextToText,
+    AutoProcessor,
+    TextIteratorStreamer,
+)
+from transformers.image_utils import load_image
+
+# Constants for text generation
+MAX_MAX_NEW_TOKENS = 4096
+DEFAULT_MAX_NEW_TOKENS = 1024
+MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+# Load LFM2-VL-1.6B
+MODEL_ID_M = "LiquidAI/LFM2-VL-1.6B"
+processor_m = AutoProcessor.from_pretrained(MODEL_ID_M, trust_remote_code=True)
+model_m = AutoModelForImageTextToText.from_pretrained(
+    MODEL_ID_M,
+    trust_remote_code=True,
+    torch_dtype="bfloat16",
+).to(device).eval()
+
+# Load LFM2-VL-450M
+MODEL_ID_T = "LiquidAI/LFM2-VL-450M"
+processor_t = AutoProcessor.from_pretrained(MODEL_ID_T, trust_remote_code=True)
+model_t = AutoModelForImageTextToText.from_pretrained(
+    MODEL_ID_T,
+    trust_remote_code=True,
+    torch_dtype="bfloat16",
+).to(device).eval()
+
+@spaces.GPU
+def generate_image(model_name: str, text: str, image: Image.Image,
+                   max_new_tokens: int = 1024,
+                   temperature: float = 0.6,
+                   top_p: float = 0.9,
+                   top_k: int = 50,
+                   repetition_penalty: float = 1.2):
+    """
+    Generate responses using the selected model for image input.
+    """
+    if model_name == "LFM2-VL-1.6B":
+        processor = processor_m
+        model = model_m
+    elif model_name == "LFM2-VL-450M":
+        processor = processor_t
+        model = model_t
+    else:
+        yield "Invalid model selected.", "Invalid model selected."
+        return
+
+    if image is None:
+        yield "Please upload an image.", "Please upload an image."
+        return
+
+    messages = [{
+        "role": "user",
+        "content": [
+            {"type": "image", "image": image},
+            {"type": "text", "text": text},
+        ]
+    }]
+    prompt_full = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    inputs = processor(
+        text=[prompt_full],
+        images=[image],
+        return_tensors="pt",
+        padding=True,
+        truncation=False,
+        max_length=MAX_INPUT_TOKEN_LENGTH
+    ).to(device)
+    streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
+    generation_kwargs = {**inputs, "streamer": streamer, "max_new_tokens": max_new_tokens}
+    thread = threading.Thread(target=model.generate, kwargs=generation_kwargs)
+    thread.start()
+    buffer = ""
+    for new_text in streamer:
+        buffer += new_text
+        time.sleep(0.01)
+        yield buffer, buffer
+
+# Define examples for image inference
+image_examples = [
+    ["Explain the movie shot in detail.", "images/4.png"],
+    ["According to this diagram, where do severe droughts occur?", "images/1.png"],
+    ["Could you describe this image?", "images/2.jpg"],
+    ["Provide a description of this image.", "images/3.jpg"],
+]
+
+# Updated CSS with model choice highlighting
+css = """
+.submit-btn {
+    background-color: #2980b9 !important;
+    color: white !important;
+}
+.submit-btn:hover {
+    background-color: #3498db !important;
+}
+.canvas-output {
+    border: 2px solid #4682B4;
+    border-radius: 10px;
+    padding: 20px;
+}
+"""
+
+# Create the Gradio Interface
+with gr.Blocks(css=css, theme="gstaff/sketch") as demo:
+    gr.Markdown("# **LFM2-VL by [LiquidAI](https://huggingface.co/LiquidAI)**")
+    with gr.Row():
+        with gr.Column():
+            image_query = gr.Textbox(label="Query Input", placeholder="✦︎ Enter your query")
+            image_upload = gr.Image(type="pil", label="Image")
+            image_submit = gr.Button("Submit", elem_classes="submit-btn")
+            gr.Examples(
+                examples=image_examples,
+                inputs=[image_query, image_upload]
+            )
+
+            with gr.Accordion("Advanced options", open=False):
+                max_new_tokens = gr.Slider(label="Max new tokens", minimum=1, maximum=MAX_MAX_NEW_TOKENS, step=1, value=DEFAULT_MAX_NEW_TOKENS)
+                temperature = gr.Slider(label="Temperature", minimum=0.1, maximum=4.0, step=0.1, value=0.6)
+                top_p = gr.Slider(label="Top-p (nucleus sampling)", minimum=0.05, maximum=1.0, step=0.05, value=0.9)
+                top_k = gr.Slider(label="Top-k", minimum=1, maximum=1000, step=1, value=50)
+                repetition_penalty = gr.Slider(label="Repetition penalty", minimum=1.0, maximum=2.0, step=0.05, value=1.2)
+
+        with gr.Column():
+            with gr.Column(elem_classes="canvas-output"):
+                gr.Markdown("## Output")
+                output = gr.Textbox(label="Raw Output Stream", interactive=False, lines=2)
+                with gr.Accordion("(Result.md)", open=False):                
+                    markdown_output = gr.Markdown(label="(Result.md)")
+
+            model_choice = gr.Dropdown(
+                choices=["LFM2-VL-1.6B", "LFM2-VL-450M"],
+                label="Select Model",
+                value="LFM2-VL-1.6B"
+            )
+
+            gr.Markdown("**Model Info 💻** | [Report Bug](https://huggingface.co/spaces/prithivMLmods/LFM2-VL-Demo/discussions)")
+            gr.Markdown("> [LFM2‑VL](https://huggingface.co/collections/LiquidAI/lfm2-vl-68963bbc84a610f7638d5ffa) is [Liquid AI’s](https://huggingface.co/LiquidAI) first multimodal model series, featuring models with 450M and 1.6B parameters designed for efficient processing of both text and images at native resolutions up to 512×512, ideal for low-latency edge AI applications; leveraging a hybrid conv+attention LFM2 backbone and SigLIP2 NaFlex vision encoders, it delivers flexible, user-tunable inference with rapid speeds (2× faster than existing VLMs on GPU)")
+            gr.Markdown("> Competitive accuracy, and dynamic image tokenization for scalable throughput, while supporting 32,768 text tokens and English language generation, and is best fine-tuned for targeted use cases using provided supervised fine-tuning tools, all released under the LFM Open License v1.0 for research and deployment scenarios not requiring safety-critical guarantees.")
+
+    # Define the submit button action
+    image_submit.click(fn=generate_image,
+                       inputs=[
+                           model_choice, image_query, image_upload,
+                           max_new_tokens, temperature, top_p, top_k,
+                           repetition_penalty
+                       ],
+                       outputs=[output, markdown_output])
+
+if __name__ == "__main__":
     demo.queue(max_size=50).launch(share=True, mcp_server=True, ssr_mode=False, show_error=True)