Update app.py

app.py

@@ -1,34 +1,48 @@
+#!/usr/bin/env python
+
 import os
 from collections.abc import Iterator
 from threading import Thread
 
 import gradio as gr
+import spaces
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
 
+#
+# 1) Custom Pastel Gradient CSS
+#
 CUSTOM_CSS = """
 .gradio-container {
     background: linear-gradient(to right, #FFDEE9, #B5FFFC);
-    color: black;
 }
 """
 
-DESCRIPTION = """# Bonjour Dans le chat du consentement  
-Mistral-7B Instruct Demo  
+#
+# 2) Description: Add French greeting, plus any info
+#
+DESCRIPTION = """# Bonjour Dans le chat du consentement
+
+Mistral-7B Instruct Demo
 """
 
-MAX_INPUT_TOKEN_LENGTH = 4096  # just a default
+if not torch.cuda.is_available():
+    DESCRIPTION += (
+        "\n<p style='color:red;'>Running on CPU - This is likely too large to run effectively.</p>"
+    )
 
-# Define model/tokenizer at the top so they're visible in all scopes
-tokenizer = None
-model = None
+MAX_MAX_NEW_TOKENS = 2048
+DEFAULT_MAX_NEW_TOKENS = 1024
+MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))
 
-# Try to load the model only if GPU is available
+#
+# 3) Load Mistral-7B Instruct (requires gating, GPU recommended)
+#
 if torch.cuda.is_available():
     model_id = "mistralai/Mistral-7B-Instruct-v0.3"
     tokenizer = AutoTokenizer.from_pretrained(
-        model_id,
-        trust_remote_code=True
+        model_id, 
+        trust_remote_code=True  # Might be needed for custom code
     )
     model = AutoModelForCausalLM.from_pretrained(
         model_id,
@@ -36,57 +50,110 @@ if torch.cuda.is_available():
         device_map="auto",
         trust_remote_code=True
     )
-else:
-    # Show a warning in the description
-    DESCRIPTION += "\n**Running on CPU** — This model is too large for CPU inference!"
-
-def generate(message: str, chat_history: list[dict]) -> Iterator[str]:
-    # If there's no GPU (thus no tokenizer/model), return an error
-    if tokenizer is None or model is None:
-        yield "Error: No GPU available. Unable to load Mistral-7B-Instruct."
-        return
 
+def generate(
+    message: str,
+    chat_history: list[dict],
+    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]:
+    """
+    This function handles streaming chat text as the model generates it.
+    Uses Mistral's 'apply_chat_template' to handle chat-style prompting.
+    """
     conversation = [*chat_history, {"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.")
-
+        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,
+        tokenizer, 
+        timeout=20.0, 
+        skip_prompt=True, 
         skip_special_tokens=True
     )
-
-    generate_kwargs = {
-        "input_ids": input_ids,
-        "streamer": streamer,
-        "max_new_tokens": 512,
-        "do_sample": True,
-        "temperature": 0.7,
-        "top_p": 0.9,
-        "repetition_penalty": 1.1,
-    }
-
+    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)
+        # Stream partial output as it's generated
         yield "".join(outputs)
 
+#
+# 4) Build the Chat Interface with extra sliders
+#
 demo = gr.ChatInterface(
     fn=generate,
     description=DESCRIPTION,
-    css=CUSTOM_CSS,
-    examples=None,
-    type="messages"
+    css=CUSTOM_CSS,  # Use our pastel gradient
+    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 what the Python programming language is?"],
+        ["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'"],
+    ],
+    type="messages",
 )
 
 if __name__ == "__main__":
-    demo.queue().launch()
+    demo.queue(max_size=20).launch()