Update app.py

app.py

@@ -9,21 +9,39 @@ 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"
-    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
@@ -36,36 +54,54 @@ def generate(
     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(
@@ -117,6 +153,9 @@ chat_interface = gr.ChatInterface(
     ],
 )
 
+# Debugging: Interface setup
+print("Setting up interface...")
+
 with gr.Blocks(css="style.css") as demo:
     gr.Markdown(DESCRIPTION)
     gr.DuplicateButton(
@@ -126,57 +165,193 @@ with gr.Blocks(css="style.css") as demo:
     )
     chat_interface.render()
 
+# Debugging: Starting queue and launching the demo
+print("Launching demo...")
+
 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)
+
+
+#!/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
+
+# HF_TOKEN = os.environ.get("HF_TOKEN")
+
+# 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>"
+
+# MAX_MAX_NEW_TOKENS = 2048
+# DEFAULT_MAX_NEW_TOKENS = 1024
+# MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))
+
+# 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)
+
+
+# @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]:
+#     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)
+
+
+# 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'"],
+#     ],
+# )
+
+# 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()
+
+# 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)