diff --git a/interactivechat.py b/interactivechat.py
new file mode 100644
index 0000000..ad16f0c
--- /dev/null
+++ b/interactivechat.py
@@ -0,0 +1,150 @@
+import os
+import PIL.Image
+import torch
+import numpy as np
+from transformers import AutoModelForCausalLM
+from janus.models import MultiModalityCausalLM, VLChatProcessor
+import time
+import re
+
+# Specify the path to the model
+model_path = "deepseek-ai/Janus-1.3B"
+vl_chat_processor: VLChatProcessor = VLChatProcessor.from_pretrained(model_path)
+tokenizer = vl_chat_processor.tokenizer
+
+vl_gpt: MultiModalityCausalLM = AutoModelForCausalLM.from_pretrained(
+    model_path, trust_remote_code=True
+)
+vl_gpt = vl_gpt.to(torch.bfloat16).cuda().eval()
+
+
+def create_prompt(user_input: str) -> str:
+    conversation = [
+        {
+            "role": "User",
+            "content": user_input,
+        },
+        {"role": "Assistant", "content": ""},
+    ]
+
+    sft_format = vl_chat_processor.apply_sft_template_for_multi_turn_prompts(
+        conversations=conversation,
+        sft_format=vl_chat_processor.sft_format,
+        system_prompt="",
+    )
+    prompt = sft_format + vl_chat_processor.image_start_tag
+    return prompt
+
+
+@torch.inference_mode()
+def generate(
+    mmgpt: MultiModalityCausalLM,
+    vl_chat_processor: VLChatProcessor,
+    prompt: str,
+    short_prompt: str,
+    parallel_size: int = 16,
+    temperature: float = 1,
+    cfg_weight: float = 5,
+    image_token_num_per_image: int = 576,
+    img_size: int = 384,
+    patch_size: int = 16,
+):
+    input_ids = vl_chat_processor.tokenizer.encode(prompt)
+    input_ids = torch.LongTensor(input_ids)
+
+    tokens = torch.zeros((parallel_size * 2, len(input_ids)), dtype=torch.int).cuda()
+    for i in range(parallel_size * 2):
+        tokens[i, :] = input_ids
+        if i % 2 != 0:
+            tokens[i, 1:-1] = vl_chat_processor.pad_id
+
+    inputs_embeds = mmgpt.language_model.get_input_embeddings()(tokens)
+
+    generated_tokens = torch.zeros((parallel_size, image_token_num_per_image), dtype=torch.int).cuda()
+    outputs = None  # Initialize outputs for use in the loop
+
+    for i in range(image_token_num_per_image):
+        outputs = mmgpt.language_model.model(
+            inputs_embeds=inputs_embeds,
+            use_cache=True,
+            past_key_values=outputs.past_key_values if i != 0 else None
+        )
+        hidden_states = outputs.last_hidden_state
+
+        logits = mmgpt.gen_head(hidden_states[:, -1, :])
+        logit_cond = logits[0::2, :]
+        logit_uncond = logits[1::2, :]
+
+        logits = logit_uncond + cfg_weight * (logit_cond - logit_uncond)
+        probs = torch.softmax(logits / temperature, dim=-1)
+
+        next_token = torch.multinomial(probs, num_samples=1)
+        generated_tokens[:, i] = next_token.squeeze(dim=-1)
+
+        next_token = torch.cat([next_token.unsqueeze(dim=1), next_token.unsqueeze(dim=1)], dim=1).view(-1)
+        img_embeds = mmgpt.prepare_gen_img_embeds(next_token)
+        inputs_embeds = img_embeds.unsqueeze(dim=1)
+
+    dec = mmgpt.gen_vision_model.decode_code(
+        generated_tokens.to(dtype=torch.int),
+        shape=[parallel_size, 8, img_size // patch_size, img_size // patch_size]
+    )
+    dec = dec.to(torch.float32).cpu().numpy().transpose(0, 2, 3, 1)
+
+    dec = np.clip((dec + 1) / 2 * 255, 0, 255)
+
+    visual_img = np.zeros((parallel_size, img_size, img_size, 3), dtype=np.uint8)
+    visual_img[:, :, :] = dec
+
+    os.makedirs('generated_samples', exist_ok=True)
+
+    # Create a timestamp
+    timestamp = time.strftime("%Y%m%d-%H%M%S")
+
+    # Sanitize the short_prompt to ensure it's safe for filenames
+    short_prompt = re.sub(r'\W+', '_', short_prompt)[:50]
+
+    # Save images with timestamp and part of the user prompt in the filename
+    for i in range(parallel_size):
+        save_path = os.path.join('generated_samples', f"img_{timestamp}_{short_prompt}_{i}.jpg")
+        PIL.Image.fromarray(visual_img[i]).save(save_path)
+
+
+def interactive_image_generator():
+    print("Welcome to the interactive image generator!")
+
+    # Ask for the number of images at the start of the session
+    while True:
+        num_images_input = input("How many images would you like to generate per prompt? (Enter a positive integer): ")
+        if num_images_input.isdigit() and int(num_images_input) > 0:
+            parallel_size = int(num_images_input)
+            break
+        else:
+            print("Invalid input. Please enter a positive integer.")
+
+    while True:
+        user_input = input("Please describe the image you'd like to generate (or type 'exit' to quit): ")
+
+        if user_input.lower() == 'exit':
+            print("Exiting the image generator. Goodbye!")
+            break
+
+        prompt = create_prompt(user_input)
+
+        # Create a sanitized version of user_input for the filename
+        short_prompt = re.sub(r'\W+', '_', user_input)[:50]
+
+        print(f"Generating {parallel_size} image(s) for: '{user_input}'")
+        generate(
+            mmgpt=vl_gpt,
+            vl_chat_processor=vl_chat_processor,
+            prompt=prompt,
+            short_prompt=short_prompt,
+            parallel_size=parallel_size  # Pass the user-specified number of images
+        )
+
+        print("Image generation complete! Check the 'generated_samples' folder for the output.\n")
+
+
+if __name__ == "__main__":
+    interactive_image_generator()