Update app.py

app.py

@@ -16,6 +16,8 @@ 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 = "# Mistral-7B v0.2"
 
 if not torch.cuda.is_available():
@@ -104,6 +106,12 @@ def generate(
         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=[
@@ -158,12 +166,27 @@ 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()
+    
+    # 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...")
@@ -173,7 +196,8 @@ if __name__ == "__main__":
 
 
 
-#!/usr/bin/env python
+# WORKING
+# #!/usr/bin/env python
 
 # import os
 # from threading import Thread
@@ -184,21 +208,39 @@ if __name__ == "__main__":
 # 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"
-#     model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto", token=HF_TOKEN)
-#     tokenizer = AutoTokenizer.from_pretrained(model_id, token=HF_TOKEN)
+#     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
@@ -211,36 +253,54 @@ if __name__ == "__main__":
 #     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})
 
-#     input_ids = tokenizer.apply_chat_template(conversation, 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)
-#     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,
-#     )
-#     t = Thread(target=model.generate, kwargs=generate_kwargs)
-#     t.start()
-
-#     outputs = []
-#     for text in streamer:
-#         outputs.append(text)
-#         yield "".join(outputs)
+#     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(
@@ -292,6 +352,9 @@ if __name__ == "__main__":
 #     ],
 # )
 
+# # Debugging: Interface setup
+# print("Setting up interface...")
+
 # with gr.Blocks(css="style.css") as demo:
 #     gr.Markdown(DESCRIPTION)
 #     gr.DuplicateButton(
@@ -301,126 +364,8 @@ if __name__ == "__main__":
 #     )
 #     chat_interface.render()
 
-# if __name__ == "__main__":
-#     demo.queue(max_size=20).launch(share=True)
-
-#     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'"],
-#     ],
-# ).launch(share=True)
-
-
-
-
-
-# import gradio as gr
-# import spaces
-# from huggingface_hub import InferenceClient
-# import gradio as gr
-
-
-
-# """
-# For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-# """
-# client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-# @spaces.GPU()
-# def respond(
-#     message,
-#     history: list[tuple[str, str]],
-#     system_message,
-#     max_tokens,
-#     temperature,
-#     top_p,
-# ):
-#     messages = [{"role": "system", "content": system_message}]
-
-#     for val in history:
-#         if val[0]:
-#             messages.append({"role": "user", "content": val[0]})
-#         if val[1]:
-#             messages.append({"role": "assistant", "content": val[1]})
-
-#     messages.append({"role": "user", "content": message})
-
-#     response = ""
-
-#     for message in client.chat_completion(
-#         messages,
-#         max_tokens=max_tokens,
-#         stream=True,
-#         temperature=temperature,
-#         top_p=top_p,
-#     ):
-#         token = message.choices[0].delta.content
-
-#         response += token
-#         yield response
-
-# """
-# For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-# """
-# demo = gr.ChatInterface(
-#     respond,
-#     additional_inputs=[
-#         gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-#         gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-#         gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-#         gr.Slider(
-#             minimum=0.1,
-#             maximum=1.0,
-#             value=0.95,
-#             step=0.05,
-#             label="Top-p (nucleus sampling)",
-#         ),
-#     ],
-# )
-
+# # Debugging: Starting queue and launching the demo
+# print("Launching demo...")
 
 # if __name__ == "__main__":
-#     demo.launch()
+#     demo.queue(max_size=20).launch(share=True)