Update app.py

app.py

@@ -1,371 +1,371 @@
-#!/usr/bin/env python
-
-import os
-from threading import Thread
-from typing import Iterator
-
-import gradio as gr
-import spaces
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
-
-# Debugging: Start script
-print("Starting script...")
-
-HF_TOKEN = os.environ.get("HF_TOKEN")
-if HF_TOKEN is None:
-    print("Warning: HF_TOKEN is not set!")
-
-PASSWORD = os.getenv("APP_PASSWORD", "mysecretpassword")  # Set your desired password here or via environment variable
-
-DESCRIPTION = "# FT of Lama"
-
-if not torch.cuda.is_available():
-    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"
-    print("Warning: No GPU available. This model cannot run on CPU.")
-else:
-    print("GPU is available!")
-
-MAX_MAX_NEW_TOKENS = 2048
-DEFAULT_MAX_NEW_TOKENS = 1024
-MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))
-
-# Debugging: GPU check passed, loading model
-if torch.cuda.is_available():
-    model_id = "BGLAW/llama-3-8b-Instruct-bglawinsv1UNS_merged"
-    try:
-        print("Loading model...")
-        model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto", token=HF_TOKEN)
-        print("Model loaded successfully!")
-        
-        print("Loading tokenizer...")
-        tokenizer = AutoTokenizer.from_pretrained(model_id, token=HF_TOKEN)
-        print("Tokenizer loaded successfully!")
-    except Exception as e:
-        print(f"Error loading model or tokenizer: {e}")
-        raise e  # Re-raise the error after logging it
-
-
-@spaces.GPU
-def generate(
-    message: str,
-    chat_history: list[tuple[str, str]],
-    max_new_tokens: int = 1024,
-    temperature: float = 0.6,
-    top_p: float = 0.9,
-    top_k: int = 50,
-    repetition_penalty: float = 1.2,
-) -> Iterator[str]:
-    print(f"Received message: {message}")
-    print(f"Chat history: {chat_history}")
-
-    conversation = []
-    for user, assistant in chat_history:
-        conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}])
-    conversation.append({"role": "user", "content": message})
-
-    try:
-        print("Tokenizing input...")
-        input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt")
-        print(f"Input tokenized: {input_ids.shape}")
-        
-        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.")
-            print("Trimmed input tokens due to length.")
-        
-        input_ids = input_ids.to(model.device)
-        print("Input moved to the model's device.")
-        
-        streamer = TextIteratorStreamer(tokenizer, timeout=20.0, skip_prompt=True, skip_special_tokens=True)
-        generate_kwargs = dict(
-            {"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,
-        )
-        
-        print("Starting generation...")
-        t = Thread(target=model.generate, kwargs=generate_kwargs)
-        t.start()
-        print("Thread started for model generation.")
-        
-        outputs = []
-        for text in streamer:
-            outputs.append(text)
-            print(f"Generated text so far: {''.join(outputs)}")
-            yield "".join(outputs)
-    
-    except Exception as e:
-        print(f"Error during generation: {e}")
-        raise e  # Re-raise the error after logging it
-
-
-def password_auth(password):
-    if password == PASSWORD:
-        return gr.update(visible=True), gr.update(visible=False)
-    else:
-        return gr.update(visible=False), gr.update(visible=True, value="Incorrect password. Try again.")
-
-chat_interface = 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,
-        ),
-    ],
-    stop_btn=None,
-    examples=[
-        ["Hello there! How are you doing?"],
-        ["Can you explain briefly to me what is the Python programming language?"],
-        ["Explain the plot of Cinderella in a sentence."],
-        ["How many hours does it take a man to eat a Helicopter?"],
-        ["Write a 100-word article on 'Benefits of Open-Source in AI research'"],
-    ],
-)
-
-# Debugging: Interface setup
-print("Setting up interface...")
-
-with gr.Blocks(css="style.css") as demo:
-    gr.Markdown(DESCRIPTION)
-    
-    # Create login components
-    with gr.Row(visible=True) as login_area:
-        password_input = gr.Textbox(
-            label="Enter Password", type="password", placeholder="Password", show_label=True
-        )
-        login_btn = gr.Button("Submit")
-        incorrect_password_msg = gr.Markdown("Incorrect password. Try again.", visible=False)
-    
-    # Main chat interface
-    with gr.Column(visible=False) as chat_area:
-        gr.Markdown(DESCRIPTION)
-        gr.DuplicateButton(
-            value="Duplicate Space for private use",
-            elem_id="duplicate-button",
-            visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1",
-        )
-        chat_interface.render()
-    
-    # Bind login button to check password
-    login_btn.click(password_auth, inputs=password_input, outputs=[chat_area, incorrect_password_msg])
-
-# Debugging: Starting queue and launching the demo
-print("Launching demo...")
-
-if __name__ == "__main__":
-    demo.queue(max_size=20).launch(share=True)
-
-
-
-# WORKING
-# #!/usr/bin/env python
-
-# import os
-# from threading import Thread
-# from typing import Iterator
-
-# import gradio as gr
-# import spaces
-# import torch
-# from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
-
-# # Debugging: Start script
-# print("Starting script...")
-
-# HF_TOKEN = os.environ.get("HF_TOKEN")
-# if HF_TOKEN is None:
-#     print("Warning: HF_TOKEN is not set!")
-
-# DESCRIPTION = "# Mistral-7B v0.2"
-
-# if not torch.cuda.is_available():
-#     DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"
-#     print("Warning: No GPU available. This model cannot run on CPU.")
-# else:
-#     print("GPU is available!")
-
-# MAX_MAX_NEW_TOKENS = 2048
-# DEFAULT_MAX_NEW_TOKENS = 1024
-# MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))
-
-# # Debugging: GPU check passed, loading model
-# if torch.cuda.is_available():
-#     model_id = "mistralai/Mistral-7B-Instruct-v0.2"
-#     try:
-#         print("Loading model...")
-#         model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto", token=HF_TOKEN)
-#         print("Model loaded successfully!")
-        
-#         print("Loading tokenizer...")
-#         tokenizer = AutoTokenizer.from_pretrained(model_id, token=HF_TOKEN)
-#         print("Tokenizer loaded successfully!")
-#     except Exception as e:
-#         print(f"Error loading model or tokenizer: {e}")
-#         raise e  # Re-raise the error after logging it
-
-
-# @spaces.GPU
-# def generate(
-#     message: str,
-#     chat_history: list[tuple[str, str]],
-#     max_new_tokens: int = 1024,
-#     temperature: float = 0.6,
-#     top_p: float = 0.9,
-#     top_k: int = 50,
-#     repetition_penalty: float = 1.2,
-# ) -> Iterator[str]:
-#     print(f"Received message: {message}")
-#     print(f"Chat history: {chat_history}")
-
-#     conversation = []
-#     for user, assistant in chat_history:
-#         conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}])
-#     conversation.append({"role": "user", "content": message})
-
-#     try:
-#         print("Tokenizing input...")
-#         input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt")
-#         print(f"Input tokenized: {input_ids.shape}")
-        
-#         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.")
-#             print("Trimmed input tokens due to length.")
-        
-#         input_ids = input_ids.to(model.device)
-#         print("Input moved to the model's device.")
-        
-#         streamer = TextIteratorStreamer(tokenizer, timeout=20.0, skip_prompt=True, skip_special_tokens=True)
-#         generate_kwargs = dict(
-#             {"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,
-#         )
-        
-#         print("Starting generation...")
-#         t = Thread(target=model.generate, kwargs=generate_kwargs)
-#         t.start()
-#         print("Thread started for model generation.")
-        
-#         outputs = []
-#         for text in streamer:
-#             outputs.append(text)
-#             print(f"Generated text so far: {''.join(outputs)}")
-#             yield "".join(outputs)
-    
-#     except Exception as e:
-#         print(f"Error during generation: {e}")
-#         raise e  # Re-raise the error after logging it
-
-
-# chat_interface = 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,
-#         ),
-#     ],
-#     stop_btn=None,
-#     examples=[
-#         ["Hello there! How are you doing?"],
-#         ["Can you explain briefly to me what is the Python programming language?"],
-#         ["Explain the plot of Cinderella in a sentence."],
-#         ["How many hours does it take a man to eat a Helicopter?"],
-#         ["Write a 100-word article on 'Benefits of Open-Source in AI research'"],
-#     ],
-# )
-
-# # Debugging: Interface setup
-# print("Setting up interface...")
-
-# with gr.Blocks(css="style.css") as demo:
-#     gr.Markdown(DESCRIPTION)
-#     gr.DuplicateButton(
-#         value="Duplicate Space for private use",
-#         elem_id="duplicate-button",
-#         visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1",
-#     )
-#     chat_interface.render()
-
-# # Debugging: Starting queue and launching the demo
-# print("Launching demo...")
-
-# if __name__ == "__main__":
-#     demo.queue(max_size=20).launch(share=True)
+#!/usr/bin/env python
+
+import os
+from threading import Thread
+from typing import Iterator
+
+import gradio as gr
+import spaces
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
+
+# Debugging: Start script
+print("Starting script...")
+
+HF_TOKEN = os.environ.get("HF_TOKEN")
+if HF_TOKEN is None:
+    print("Warning: HF_TOKEN is not set!")
+
+PASSWORD = os.getenv("APP_PASSWORD", "mysecretpassword")  # Set your desired password here or via environment variable
+
+DESCRIPTION = "# FT of Lama"
+
+if not torch.cuda.is_available():
+    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"
+    print("Warning: No GPU available. This model cannot run on CPU.")
+else:
+    print("GPU is available!")
+
+MAX_MAX_NEW_TOKENS = 2048
+DEFAULT_MAX_NEW_TOKENS = 1024
+MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))
+
+# Debugging: GPU check passed, loading model
+if torch.cuda.is_available():
+    model_id = "torVik/bggpt-Instruct-bglawinsv1UNS"
+    try:
+        print("Loading model...")
+        model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto", token=HF_TOKEN)
+        print("Model loaded successfully!")
+        
+        print("Loading tokenizer...")
+        tokenizer = AutoTokenizer.from_pretrained(model_id, token=HF_TOKEN)
+        print("Tokenizer loaded successfully!")
+    except Exception as e:
+        print(f"Error loading model or tokenizer: {e}")
+        raise e  # Re-raise the error after logging it
+
+
+@spaces.GPU
+def generate(
+    message: str,
+    chat_history: list[tuple[str, str]],
+    max_new_tokens: int = 1024,
+    temperature: float = 0.6,
+    top_p: float = 0.9,
+    top_k: int = 50,
+    repetition_penalty: float = 1.2,
+) -> Iterator[str]:
+    print(f"Received message: {message}")
+    print(f"Chat history: {chat_history}")
+
+    conversation = []
+    for user, assistant in chat_history:
+        conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}])
+    conversation.append({"role": "user", "content": message})
+
+    try:
+        print("Tokenizing input...")
+        input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt")
+        print(f"Input tokenized: {input_ids.shape}")
+        
+        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.")
+            print("Trimmed input tokens due to length.")
+        
+        input_ids = input_ids.to(model.device)
+        print("Input moved to the model's device.")
+        
+        streamer = TextIteratorStreamer(tokenizer, timeout=20.0, skip_prompt=True, skip_special_tokens=True)
+        generate_kwargs = dict(
+            {"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,
+        )
+        
+        print("Starting generation...")
+        t = Thread(target=model.generate, kwargs=generate_kwargs)
+        t.start()
+        print("Thread started for model generation.")
+        
+        outputs = []
+        for text in streamer:
+            outputs.append(text)
+            print(f"Generated text so far: {''.join(outputs)}")
+            yield "".join(outputs)
+    
+    except Exception as e:
+        print(f"Error during generation: {e}")
+        raise e  # Re-raise the error after logging it
+
+
+def password_auth(password):
+    if password == PASSWORD:
+        return gr.update(visible=True), gr.update(visible=False)
+    else:
+        return gr.update(visible=False), gr.update(visible=True, value="Incorrect password. Try again.")
+
+chat_interface = 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,
+        ),
+    ],
+    stop_btn=None,
+    examples=[
+        ["Hello there! How are you doing?"],
+        ["Can you explain briefly to me what is the Python programming language?"],
+        ["Explain the plot of Cinderella in a sentence."],
+        ["How many hours does it take a man to eat a Helicopter?"],
+        ["Write a 100-word article on 'Benefits of Open-Source in AI research'"],
+    ],
+)
+
+# Debugging: Interface setup
+print("Setting up interface...")
+
+with gr.Blocks(css="style.css") as demo:
+    gr.Markdown(DESCRIPTION)
+    
+    # Create login components
+    with gr.Row(visible=True) as login_area:
+        password_input = gr.Textbox(
+            label="Enter Password", type="password", placeholder="Password", show_label=True
+        )
+        login_btn = gr.Button("Submit")
+        incorrect_password_msg = gr.Markdown("Incorrect password. Try again.", visible=False)
+    
+    # Main chat interface
+    with gr.Column(visible=False) as chat_area:
+        gr.Markdown(DESCRIPTION)
+        gr.DuplicateButton(
+            value="Duplicate Space for private use",
+            elem_id="duplicate-button",
+            visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1",
+        )
+        chat_interface.render()
+    
+    # Bind login button to check password
+    login_btn.click(password_auth, inputs=password_input, outputs=[chat_area, incorrect_password_msg])
+
+# Debugging: Starting queue and launching the demo
+print("Launching demo...")
+
+if __name__ == "__main__":
+    demo.queue(max_size=20).launch(share=True)
+
+
+
+# WORKING
+# #!/usr/bin/env python
+
+# import os
+# from threading import Thread
+# from typing import Iterator
+
+# import gradio as gr
+# import spaces
+# import torch
+# from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
+
+# # Debugging: Start script
+# print("Starting script...")
+
+# HF_TOKEN = os.environ.get("HF_TOKEN")
+# if HF_TOKEN is None:
+#     print("Warning: HF_TOKEN is not set!")
+
+# DESCRIPTION = "# Mistral-7B v0.2"
+
+# if not torch.cuda.is_available():
+#     DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"
+#     print("Warning: No GPU available. This model cannot run on CPU.")
+# else:
+#     print("GPU is available!")
+
+# MAX_MAX_NEW_TOKENS = 2048
+# DEFAULT_MAX_NEW_TOKENS = 1024
+# MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))
+
+# # Debugging: GPU check passed, loading model
+# if torch.cuda.is_available():
+#     model_id = "mistralai/Mistral-7B-Instruct-v0.2"
+#     try:
+#         print("Loading model...")
+#         model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto", token=HF_TOKEN)
+#         print("Model loaded successfully!")
+        
+#         print("Loading tokenizer...")
+#         tokenizer = AutoTokenizer.from_pretrained(model_id, token=HF_TOKEN)
+#         print("Tokenizer loaded successfully!")
+#     except Exception as e:
+#         print(f"Error loading model or tokenizer: {e}")
+#         raise e  # Re-raise the error after logging it
+
+
+# @spaces.GPU
+# def generate(
+#     message: str,
+#     chat_history: list[tuple[str, str]],
+#     max_new_tokens: int = 1024,
+#     temperature: float = 0.6,
+#     top_p: float = 0.9,
+#     top_k: int = 50,
+#     repetition_penalty: float = 1.2,
+# ) -> Iterator[str]:
+#     print(f"Received message: {message}")
+#     print(f"Chat history: {chat_history}")
+
+#     conversation = []
+#     for user, assistant in chat_history:
+#         conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}])
+#     conversation.append({"role": "user", "content": message})
+
+#     try:
+#         print("Tokenizing input...")
+#         input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt")
+#         print(f"Input tokenized: {input_ids.shape}")
+        
+#         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.")
+#             print("Trimmed input tokens due to length.")
+        
+#         input_ids = input_ids.to(model.device)
+#         print("Input moved to the model's device.")
+        
+#         streamer = TextIteratorStreamer(tokenizer, timeout=20.0, skip_prompt=True, skip_special_tokens=True)
+#         generate_kwargs = dict(
+#             {"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,
+#         )
+        
+#         print("Starting generation...")
+#         t = Thread(target=model.generate, kwargs=generate_kwargs)
+#         t.start()
+#         print("Thread started for model generation.")
+        
+#         outputs = []
+#         for text in streamer:
+#             outputs.append(text)
+#             print(f"Generated text so far: {''.join(outputs)}")
+#             yield "".join(outputs)
+    
+#     except Exception as e:
+#         print(f"Error during generation: {e}")
+#         raise e  # Re-raise the error after logging it
+
+
+# chat_interface = 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,
+#         ),
+#     ],
+#     stop_btn=None,
+#     examples=[
+#         ["Hello there! How are you doing?"],
+#         ["Can you explain briefly to me what is the Python programming language?"],
+#         ["Explain the plot of Cinderella in a sentence."],
+#         ["How many hours does it take a man to eat a Helicopter?"],
+#         ["Write a 100-word article on 'Benefits of Open-Source in AI research'"],
+#     ],
+# )
+
+# # Debugging: Interface setup
+# print("Setting up interface...")
+
+# with gr.Blocks(css="style.css") as demo:
+#     gr.Markdown(DESCRIPTION)
+#     gr.DuplicateButton(
+#         value="Duplicate Space for private use",
+#         elem_id="duplicate-button",
+#         visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1",
+#     )
+#     chat_interface.render()
+
+# # Debugging: Starting queue and launching the demo
+# print("Launching demo...")
+
+# if __name__ == "__main__":
+#     demo.queue(max_size=20).launch(share=True)