Update app.py

app.py

@@ -1,45 +1,85 @@
-import gradio as gr
-from transformers import BlenderbotTokenizer, BlenderbotForConditionalGeneration
-
-tokenizer = BlenderbotTokenizer.from_pretrained("facebook/blenderbot-400M-distill")
-model = BlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-400M-distill")
-
-def chat_with_model(input_text):
-    input_ids = tokenizer.encode("You: " + input_text, return_tensors="pt", max_length=512, truncation=True)
-    response_ids = model.generate(input_ids, max_length=100, num_return_sequences=1, no_repeat_ngram_size=2)
-    reply = tokenizer.decode(response_ids[0], skip_special_tokens=True)
-    return reply
-
-custom_css = """
-<style>
-.gr-form-box {
-    border: 2px solid #0074D9;
-    border-radius: 10px;
-    background-color: #F5F5F5;
-    padding: 10px;
-}
-.gr-textbox {
-    background-color: #EAEAEA;
-    border: 1px solid #0074D9;
-    border-radius: 5px;
-    padding: 5px;
-}
-.gr-button {
-    background-color: #0074D9;
-    color: #FFFFFF;
-    border: 2px solid #0074D9;
-    border-radius: 5px;
-    padding: 5px 10px;
-}
-</style>
-"""
-
-iface = gr.Interface(
-    fn=chat_with_model,
-    inputs=gr.Textbox(prompt="You:"),
-    outputs=gr.Textbox(prompt="Bot:"),
-    live=True,
-    custom_css=custom_css  # Add your custom CSS styles here
+import torch
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
+
+MODEL_NAME = "IlyaGusev/saiga_mistral_7b"
+DEFAULT_MESSAGE_TEMPLATE = "<s>{role}\n{content}</s>"
+DEFAULT_RESPONSE_TEMPLATE = "<s>bot\n"
+DEFAULT_SYSTEM_PROMPT = "Ты — Сайга, русскоязычный автоматический ассистент. Ты разговариваешь с людьми и помогаешь им."
+
+class Conversation:
+    def __init__(
+        self,
+        message_template=DEFAULT_MESSAGE_TEMPLATE,
+        system_prompt=DEFAULT_SYSTEM_PROMPT,
+        response_template=DEFAULT_RESPONSE_TEMPLATE
+    ):
+        self.message_template = message_template
+        self.response_template = response_template
+        self.messages = [{
+            "role": "system",
+            "content": system_prompt
+        }]
+
+    def add_user_message(self, message):
+        self.messages.append({
+            "role": "user",
+            "content": message
+        })
+
+    def add_bot_message(self, message):
+        self.messages.append({
+            "role": "bot",
+            "content": message
+        })
+
+    def get_prompt(self, tokenizer):
+        final_text = ""
+        for message in self.messages:
+            message_text = self.message_template.format(**message)
+            final_text += message_text
+        final_text += DEFAULT_RESPONSE_TEMPLATE
+        return final_text.strip()
+
+
+def generate(model, tokenizer, prompt, generation_config):
+    data = tokenizer(prompt, return_tensors="pt", add_special_tokens=False)
+    data = {k: v.to(model.device) for k, v in data.items()}
+    output_ids = model.generate(
+        **data,
+        generation_config=generation_config
+    )[0]
+    output_ids = output_ids[len(data["input_ids"][0]):]
+    output = tokenizer.decode(output_ids, skip_special_tokens=True)
+    return output.strip()
+
+config = PeftConfig.from_pretrained(MODEL_NAME)
+model = AutoModelForCausalLM.from_pretrained(
+    config.base_model_name_or_path,
+    load_in_8bit=True,
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+model = PeftModel.from_pretrained(
+    model,
+    MODEL_NAME,
+    torch_dtype=torch.float16
 )
+model.eval()
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, use_fast=False)
+generation_config = GenerationConfig.from_pretrained(MODEL_NAME)
+print(generation_config)
+
+inputs = ["Почему трава зеленая?", "Сочини длинный рассказ, обязательно упоминая следующие объекты. Дано: Таня, мяч"]
+for inp in inputs:
+    conversation = Conversation()
+    conversation.add_user_message(inp)
+    prompt = conversation.get_prompt(tokenizer)
 
-iface.launch()
+    output = generate(model, tokenizer, prompt, generation_config)
+    print(inp)
+    print(output)
+    print()
+    print("==============================")
+    print()