Update app.py

app.py

@@ -1,330 +1,331 @@
-import os
-import random
-import uuid
-import json
-import time
-import asyncio
-from threading import Thread
-
-import gradio as gr
-import spaces
-import torch
-import numpy as np
-from PIL import Image
-import edge_tts
-
-from transformers import (
-    AutoModelForCausalLM,
-    AutoTokenizer,
-    TextIteratorStreamer,
-    Qwen2VLForConditionalGeneration,
-    AutoProcessor,
-)
-from transformers.image_utils import load_image
-from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
-
-
-DESCRIPTION = """
-# QwQ Edge 💬
-"""
-
-css = '''
-h1 {
-  text-align: center;
-  display: block;
-}
-
-#duplicate-button {
-  margin: auto;
-  color: #fff;
-  background: #1565c0;
-  border-radius: 100vh;
-}
-'''
-
-MAX_MAX_NEW_TOKENS = 2048
-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 text-only model and tokenizer
-model_id = "prithivMLmods/FastThink-0.5B-Tiny"
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-model = AutoModelForCausalLM.from_pretrained(
-    model_id,
-    device_map="auto",
-    torch_dtype=torch.bfloat16,
-)
-model.eval()
-
-TTS_VOICES = [
-    "en-US-JennyNeural",  # @tts1
-    "en-US-GuyNeural",    # @tts2
-]
-
-MODEL_ID = "prithivMLmods/Qwen2-VL-OCR-2B-Instruct" 
-processor = AutoProcessor.from_pretrained(MODEL_ID, trust_remote_code=True)
-model_m = Qwen2VLForConditionalGeneration.from_pretrained(
-    MODEL_ID,
-    trust_remote_code=True,
-    torch_dtype=torch.float16
-).to("cuda").eval()
-
-async def text_to_speech(text: str, voice: str, output_file="output.mp3"):
-    """Convert text to speech using Edge TTS and save as MP3"""
-    communicate = edge_tts.Communicate(text, voice)
-    await communicate.save(output_file)
-    return output_file
-
-def clean_chat_history(chat_history):
-    """
-    Filter out any chat entries whose "content" is not a string.
-    This helps prevent errors when concatenating previous messages.
-    """
-    cleaned = []
-    for msg in chat_history:
-        if isinstance(msg, dict) and isinstance(msg.get("content"), str):
-            cleaned.append(msg)
-    return cleaned
-
-# Environment variables and parameters for Stable Diffusion XL
-MODEL_ID_SD = os.getenv("MODEL_VAL_PATH")  # SDXL Model repository path via env variable
-MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "4096"))
-USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE", "0") == "1"
-ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1"
-BATCH_SIZE = int(os.getenv("BATCH_SIZE", "1"))  # For batched image generation
-
-# Load the SDXL pipeline
-sd_pipe = StableDiffusionXLPipeline.from_pretrained(
-    MODEL_ID_SD,
-    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
-    use_safetensors=True,
-    add_watermarker=False,
-).to(device)
-sd_pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(sd_pipe.scheduler.config)
-
-# Ensure that the text encoder is in half-precision if using CUDA.
-if torch.cuda.is_available():
-    sd_pipe.text_encoder = sd_pipe.text_encoder.half()
-
-# Optional: compile the model for speedup if enabled
-if USE_TORCH_COMPILE:
-    sd_pipe.compile()
-
-# Optional: offload parts of the model to CPU if needed
-if ENABLE_CPU_OFFLOAD:
-    sd_pipe.enable_model_cpu_offload()
-
-MAX_SEED = np.iinfo(np.int32).max
-
-def save_image(img: Image.Image) -> str:
-    """Save a PIL image with a unique filename and return the path."""
-    unique_name = str(uuid.uuid4()) + ".png"
-    img.save(unique_name)
-    return unique_name
-
-def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-    return seed
-
-@spaces.GPU(duration=60, enable_queue=True)
-def generate_image_fn(
-    prompt: str,
-    negative_prompt: str = "",
-    use_negative_prompt: bool = False,
-    seed: int = 1,
-    width: int = 1024,
-    height: int = 1024,
-    guidance_scale: float = 3,
-    num_inference_steps: int = 25,
-    randomize_seed: bool = False,
-    use_resolution_binning: bool = True,
-    num_images: int = 1,
-    progress=gr.Progress(track_tqdm=True),
-):
-    """Generate images using the SDXL pipeline."""
-    seed = int(randomize_seed_fn(seed, randomize_seed))
-    generator = torch.Generator(device=device).manual_seed(seed)
-
-    options = {
-        "prompt": [prompt] * num_images,
-        "negative_prompt": [negative_prompt] * num_images if use_negative_prompt else None,
-        "width": width,
-        "height": height,
-        "guidance_scale": guidance_scale,
-        "num_inference_steps": num_inference_steps,
-        "generator": generator,
-        "output_type": "pil",
-    }
-    if use_resolution_binning:
-        options["use_resolution_binning"] = True
-
-    images = []
-    # Process in batches
-    for i in range(0, num_images, BATCH_SIZE):
-        batch_options = options.copy()
-        batch_options["prompt"] = options["prompt"][i:i+BATCH_SIZE]
-        if "negative_prompt" in batch_options and batch_options["negative_prompt"] is not None:
-            batch_options["negative_prompt"] = options["negative_prompt"][i:i+BATCH_SIZE]
-        # Wrap the pipeline call in autocast if using CUDA
-        if device.type == "cuda":
-            with torch.autocast("cuda", dtype=torch.float16):
-                outputs = sd_pipe(**batch_options)
-        else:
-            outputs = sd_pipe(**batch_options)
-        images.extend(outputs.images)
-    image_paths = [save_image(img) for img in images]
-    return image_paths, seed
-
-@spaces.GPU
-def generate(
-    input_dict: dict,
-    chat_history: list[dict],
-    max_new_tokens: int = 1024,
-    temperature: float = 0.6,
-    top_p: float = 0.9,
-    top_k: int = 50,
-    repetition_penalty: float = 1.2,
-):
-    """
-    Generates chatbot responses with support for multimodal input, TTS, and image generation.
-    Special commands:
-      - "@tts1" or "@tts2": triggers text-to-speech.
-      - "@image": triggers image generation using the SDXL pipeline.
-    """
-    text = input_dict["text"]
-    files = input_dict.get("files", [])
-
-    if text.strip().lower().startswith("@image"):
-        # Remove the "@image" tag and use the rest as prompt
-        prompt = text[len("@image"):].strip()
-        yield "Generating image..."
-        image_paths, used_seed = generate_image_fn(
-            prompt=prompt,
-            negative_prompt="",
-            use_negative_prompt=False,
-            seed=1,
-            width=1024,
-            height=1024,
-            guidance_scale=3,
-            num_inference_steps=25,
-            randomize_seed=True,
-            use_resolution_binning=True,
-            num_images=1,
-        )
-        # Yield the generated image so that the chat interface displays it.
-        yield gr.Image(image_paths[0])
-        return  # Exit early
-
-    tts_prefix = "@tts"
-    is_tts = any(text.strip().lower().startswith(f"{tts_prefix}{i}") for i in range(1, 3))
-    voice_index = next((i for i in range(1, 3) if text.strip().lower().startswith(f"{tts_prefix}{i}")), None)
-    
-    if is_tts and voice_index:
-        voice = TTS_VOICES[voice_index - 1]
-        text = text.replace(f"{tts_prefix}{voice_index}", "").strip()
-        # Clear previous chat history for a fresh TTS request.
-        conversation = [{"role": "user", "content": text}]
-    else:
-        voice = None
-        # Remove any stray @tts tags and build the conversation history.
-        text = text.replace(tts_prefix, "").strip()
-        conversation = clean_chat_history(chat_history)
-        conversation.append({"role": "user", "content": text})
-
-    if files:
-        if len(files) > 1:
-            images = [load_image(image) for image in files]
-        elif len(files) == 1:
-            images = [load_image(files[0])]
-        else:
-            images = []
-        messages = [{
-            "role": "user",
-            "content": [
-                *[{"type": "image", "image": image} for image in images],
-                {"type": "text", "text": text},
-            ]
-        }]
-        prompt = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-        inputs = processor(text=[prompt], images=images, return_tensors="pt", padding=True).to("cuda")
-        streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
-        generation_kwargs = {**inputs, "streamer": streamer, "max_new_tokens": max_new_tokens}
-        thread = Thread(target=model_m.generate, kwargs=generation_kwargs)
-        thread.start()
-
-        buffer = ""
-        yield "Thinking..."
-        for new_text in streamer:
-            buffer += new_text
-            buffer = buffer.replace("<|im_end|>", "")
-            time.sleep(0.01)
-            yield buffer
-    else:
-        
-        input_ids = tokenizer.apply_chat_template(conversation, add_generation_prompt=True, return_tensors="pt")
-        if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH:
-            input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:]
-            gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.")
-        input_ids = input_ids.to(model.device)
-        streamer = TextIteratorStreamer(tokenizer, timeout=20.0, skip_prompt=True, skip_special_tokens=True)
-        generation_kwargs = {
-            "input_ids": input_ids,
-            "streamer": streamer,
-            "max_new_tokens": max_new_tokens,
-            "do_sample": True,
-            "top_p": top_p,
-            "top_k": top_k,
-            "temperature": temperature,
-            "num_beams": 1,
-            "repetition_penalty": repetition_penalty,
-        }
-        t = Thread(target=model.generate, kwargs=generation_kwargs)
-        t.start()
-
-        outputs = []
-        for new_text in streamer:
-            outputs.append(new_text)
-            yield "".join(outputs)
-
-        final_response = "".join(outputs)
-        yield final_response
-
-        # If TTS was requested, convert the final response to speech.
-        if is_tts and voice:
-            output_file = asyncio.run(text_to_speech(final_response, voice))
-            yield gr.Audio(output_file, autoplay=True)
-
-demo = gr.ChatInterface(
-    fn=generate,
-    additional_inputs=[
-        gr.Slider(label="Max new tokens", minimum=1, maximum=MAX_MAX_NEW_TOKENS, step=1, value=DEFAULT_MAX_NEW_TOKENS),
-        gr.Slider(label="Temperature", minimum=0.1, maximum=4.0, step=0.1, value=0.6),
-        gr.Slider(label="Top-p (nucleus sampling)", minimum=0.05, maximum=1.0, step=0.05, value=0.9),
-        gr.Slider(label="Top-k", minimum=1, maximum=1000, step=1, value=50),
-        gr.Slider(label="Repetition penalty", minimum=1.0, maximum=2.0, step=0.05, value=1.2),
-    ],
-    examples=[
-        ["@tts1 Who is Nikola Tesla, and why did he die?"],
-        [{"text": "Extract JSON from the image", "files": ["examples/document.jpg"]}],
-        [{"text": "summarize the letter", "files": ["examples/1.png"]}],
-        ["@image Chocolate dripping from a donut against a yellow background, in the style of brocore, hyper-realistic"],
-        ["Write a Python function to check if a number is prime."],
-        ["@tts2 What causes rainbows to form?"],
-
-    ],
-    cache_examples=False,
-    type="messages",
-    description=DESCRIPTION,
-    css=css,
-    fill_height=True,
-    textbox=gr.MultimodalTextbox(label="Query Input", file_types=["image"], file_count="multiple"),
-    stop_btn="Stop Generation",
-    multimodal=True,
-)
-
-if __name__ == "__main__":
-    # To create a public link, set share=True in launch().
+import os
+import random
+import uuid
+import json
+import time
+import asyncio
+from threading import Thread
+
+import gradio as gr
+import spaces
+import torch
+import numpy as np
+from PIL import Image
+import edge_tts
+
+from transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    TextIteratorStreamer,
+    Qwen2VLForConditionalGeneration,
+    AutoProcessor,
+)
+from transformers.image_utils import load_image
+from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
+
+
+DESCRIPTION = """
+# QwQ Edge 💬
+"""
+
+css = '''
+h1 {
+  text-align: center;
+  display: block;
+}
+
+#duplicate-button {
+  margin: auto;
+  color: #fff;
+  background: #1565c0;
+  border-radius: 100vh;
+}
+'''
+
+MAX_MAX_NEW_TOKENS = 2048
+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 text-only model and tokenizer
+#model_id = "prithivMLmods/FastThink-0.5B-Tiny"
+model_id = "prithivMLmods/SmolLM2_135M_Grpo_Gsm8k"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    device_map="auto",
+    torch_dtype=torch.bfloat16,
+)
+model.eval()
+
+TTS_VOICES = [
+    "en-US-JennyNeural",  # @tts1
+    "en-US-GuyNeural",    # @tts2
+]
+
+MODEL_ID = "prithivMLmods/Qwen2-VL-OCR-2B-Instruct" 
+processor = AutoProcessor.from_pretrained(MODEL_ID, trust_remote_code=True)
+model_m = Qwen2VLForConditionalGeneration.from_pretrained(
+    MODEL_ID,
+    trust_remote_code=True,
+    torch_dtype=torch.float16
+).to("cuda").eval()
+
+async def text_to_speech(text: str, voice: str, output_file="output.mp3"):
+    """Convert text to speech using Edge TTS and save as MP3"""
+    communicate = edge_tts.Communicate(text, voice)
+    await communicate.save(output_file)
+    return output_file
+
+def clean_chat_history(chat_history):
+    """
+    Filter out any chat entries whose "content" is not a string.
+    This helps prevent errors when concatenating previous messages.
+    """
+    cleaned = []
+    for msg in chat_history:
+        if isinstance(msg, dict) and isinstance(msg.get("content"), str):
+            cleaned.append(msg)
+    return cleaned
+
+# Environment variables and parameters for Stable Diffusion XL
+MODEL_ID_SD = os.getenv("MODEL_VAL_PATH")  # SDXL Model repository path via env variable
+MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "4096"))
+USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE", "0") == "1"
+ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1"
+BATCH_SIZE = int(os.getenv("BATCH_SIZE", "1"))  # For batched image generation
+
+# Load the SDXL pipeline
+sd_pipe = StableDiffusionXLPipeline.from_pretrained(
+    MODEL_ID_SD,
+    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+    use_safetensors=True,
+    add_watermarker=False,
+).to(device)
+sd_pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(sd_pipe.scheduler.config)
+
+# Ensure that the text encoder is in half-precision if using CUDA.
+if torch.cuda.is_available():
+    sd_pipe.text_encoder = sd_pipe.text_encoder.half()
+
+# Optional: compile the model for speedup if enabled
+if USE_TORCH_COMPILE:
+    sd_pipe.compile()
+
+# Optional: offload parts of the model to CPU if needed
+if ENABLE_CPU_OFFLOAD:
+    sd_pipe.enable_model_cpu_offload()
+
+MAX_SEED = np.iinfo(np.int32).max
+
+def save_image(img: Image.Image) -> str:
+    """Save a PIL image with a unique filename and return the path."""
+    unique_name = str(uuid.uuid4()) + ".png"
+    img.save(unique_name)
+    return unique_name
+
+def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    return seed
+
+@spaces.GPU(duration=60, enable_queue=True)
+def generate_image_fn(
+    prompt: str,
+    negative_prompt: str = "",
+    use_negative_prompt: bool = False,
+    seed: int = 1,
+    width: int = 1024,
+    height: int = 1024,
+    guidance_scale: float = 3,
+    num_inference_steps: int = 25,
+    randomize_seed: bool = False,
+    use_resolution_binning: bool = True,
+    num_images: int = 1,
+    progress=gr.Progress(track_tqdm=True),
+):
+    """Generate images using the SDXL pipeline."""
+    seed = int(randomize_seed_fn(seed, randomize_seed))
+    generator = torch.Generator(device=device).manual_seed(seed)
+
+    options = {
+        "prompt": [prompt] * num_images,
+        "negative_prompt": [negative_prompt] * num_images if use_negative_prompt else None,
+        "width": width,
+        "height": height,
+        "guidance_scale": guidance_scale,
+        "num_inference_steps": num_inference_steps,
+        "generator": generator,
+        "output_type": "pil",
+    }
+    if use_resolution_binning:
+        options["use_resolution_binning"] = True
+
+    images = []
+    # Process in batches
+    for i in range(0, num_images, BATCH_SIZE):
+        batch_options = options.copy()
+        batch_options["prompt"] = options["prompt"][i:i+BATCH_SIZE]
+        if "negative_prompt" in batch_options and batch_options["negative_prompt"] is not None:
+            batch_options["negative_prompt"] = options["negative_prompt"][i:i+BATCH_SIZE]
+        # Wrap the pipeline call in autocast if using CUDA
+        if device.type == "cuda":
+            with torch.autocast("cuda", dtype=torch.float16):
+                outputs = sd_pipe(**batch_options)
+        else:
+            outputs = sd_pipe(**batch_options)
+        images.extend(outputs.images)
+    image_paths = [save_image(img) for img in images]
+    return image_paths, seed
+
+@spaces.GPU
+def generate(
+    input_dict: dict,
+    chat_history: list[dict],
+    max_new_tokens: int = 1024,
+    temperature: float = 0.6,
+    top_p: float = 0.9,
+    top_k: int = 50,
+    repetition_penalty: float = 1.2,
+):
+    """
+    Generates chatbot responses with support for multimodal input, TTS, and image generation.
+    Special commands:
+      - "@tts1" or "@tts2": triggers text-to-speech.
+      - "@image": triggers image generation using the SDXL pipeline.
+    """
+    text = input_dict["text"]
+    files = input_dict.get("files", [])
+
+    if text.strip().lower().startswith("@image"):
+        # Remove the "@image" tag and use the rest as prompt
+        prompt = text[len("@image"):].strip()
+        yield "Generating image..."
+        image_paths, used_seed = generate_image_fn(
+            prompt=prompt,
+            negative_prompt="",
+            use_negative_prompt=False,
+            seed=1,
+            width=1024,
+            height=1024,
+            guidance_scale=3,
+            num_inference_steps=25,
+            randomize_seed=True,
+            use_resolution_binning=True,
+            num_images=1,
+        )
+        # Yield the generated image so that the chat interface displays it.
+        yield gr.Image(image_paths[0])
+        return  # Exit early
+
+    tts_prefix = "@tts"
+    is_tts = any(text.strip().lower().startswith(f"{tts_prefix}{i}") for i in range(1, 3))
+    voice_index = next((i for i in range(1, 3) if text.strip().lower().startswith(f"{tts_prefix}{i}")), None)
+    
+    if is_tts and voice_index:
+        voice = TTS_VOICES[voice_index - 1]
+        text = text.replace(f"{tts_prefix}{voice_index}", "").strip()
+        # Clear previous chat history for a fresh TTS request.
+        conversation = [{"role": "user", "content": text}]
+    else:
+        voice = None
+        # Remove any stray @tts tags and build the conversation history.
+        text = text.replace(tts_prefix, "").strip()
+        conversation = clean_chat_history(chat_history)
+        conversation.append({"role": "user", "content": text})
+
+    if files:
+        if len(files) > 1:
+            images = [load_image(image) for image in files]
+        elif len(files) == 1:
+            images = [load_image(files[0])]
+        else:
+            images = []
+        messages = [{
+            "role": "user",
+            "content": [
+                *[{"type": "image", "image": image} for image in images],
+                {"type": "text", "text": text},
+            ]
+        }]
+        prompt = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+        inputs = processor(text=[prompt], images=images, return_tensors="pt", padding=True).to("cuda")
+        streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
+        generation_kwargs = {**inputs, "streamer": streamer, "max_new_tokens": max_new_tokens}
+        thread = Thread(target=model_m.generate, kwargs=generation_kwargs)
+        thread.start()
+
+        buffer = ""
+        yield "Thinking..."
+        for new_text in streamer:
+            buffer += new_text
+            buffer = buffer.replace("<|im_end|>", "")
+            time.sleep(0.01)
+            yield buffer
+    else:
+        
+        input_ids = tokenizer.apply_chat_template(conversation, add_generation_prompt=True, return_tensors="pt")
+        if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH:
+            input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:]
+            gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.")
+        input_ids = input_ids.to(model.device)
+        streamer = TextIteratorStreamer(tokenizer, timeout=20.0, skip_prompt=True, skip_special_tokens=True)
+        generation_kwargs = {
+            "input_ids": input_ids,
+            "streamer": streamer,
+            "max_new_tokens": max_new_tokens,
+            "do_sample": True,
+            "top_p": top_p,
+            "top_k": top_k,
+            "temperature": temperature,
+            "num_beams": 1,
+            "repetition_penalty": repetition_penalty,
+        }
+        t = Thread(target=model.generate, kwargs=generation_kwargs)
+        t.start()
+
+        outputs = []
+        for new_text in streamer:
+            outputs.append(new_text)
+            yield "".join(outputs)
+
+        final_response = "".join(outputs)
+        yield final_response
+
+        # If TTS was requested, convert the final response to speech.
+        if is_tts and voice:
+            output_file = asyncio.run(text_to_speech(final_response, voice))
+            yield gr.Audio(output_file, autoplay=True)
+
+demo = gr.ChatInterface(
+    fn=generate,
+    additional_inputs=[
+        gr.Slider(label="Max new tokens", minimum=1, maximum=MAX_MAX_NEW_TOKENS, step=1, value=DEFAULT_MAX_NEW_TOKENS),
+        gr.Slider(label="Temperature", minimum=0.1, maximum=4.0, step=0.1, value=0.6),
+        gr.Slider(label="Top-p (nucleus sampling)", minimum=0.05, maximum=1.0, step=0.05, value=0.9),
+        gr.Slider(label="Top-k", minimum=1, maximum=1000, step=1, value=50),
+        gr.Slider(label="Repetition penalty", minimum=1.0, maximum=2.0, step=0.05, value=1.2),
+    ],
+    examples=[
+        ["@tts1 Who is Nikola Tesla, and why did he die?"],
+        [{"text": "Extract JSON from the image", "files": ["examples/document.jpg"]}],
+        [{"text": "summarize the letter", "files": ["examples/1.png"]}],
+        ["@image Chocolate dripping from a donut against a yellow background, in the style of brocore, hyper-realistic"],
+        ["Write a Python function to check if a number is prime."],
+        ["@tts2 What causes rainbows to form?"],
+
+    ],
+    cache_examples=False,
+    type="messages",
+    description=DESCRIPTION,
+    css=css,
+    fill_height=True,
+    textbox=gr.MultimodalTextbox(label="Query Input", file_types=["image"], file_count="multiple"),
+    stop_btn="Stop Generation",
+    multimodal=True,
+)
+
+if __name__ == "__main__":
+    # To create a public link, set share=True in launch().
     demo.queue(max_size=20).launch(share=True)