Update app.py

app.py

@@ -1,7 +1,193 @@
+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 = "INSAIT-Institute/BgGPT-Gemma-2-27B-IT-v1.0"
+    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])
 
-def greet(name):
-    return "Hello " + name + "!!"
+# Debugging: Starting queue and launching the demo
+print("Launching demo...")
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+if __name__ == "__main__":
+    demo.queue(max_size=20).launch(share=True)