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app.py

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+#!/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)

requirements.txt

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+huggingface_hub
+accelerate==0.31.0
+bitsandbytes==0.43.1
+gradio==4.36.1
+scipy==1.13.0
+sentencepiece==0.2.0
+spaces==0.28.3
+torch==2.0.1
+transformers==4.41.2

style.css

@@ -0,0 +1,17 @@
+h1 {
+  text-align: center;
+  display: block;
+}
+
+#duplicate-button {
+  margin: auto;
+  color: white;
+  background: #1565c0;
+  border-radius: 100vh;
+}
+
+.contain {
+  max-width: 900px;
+  margin: auto;
+  padding-top: 1.5rem;
+}