Update app.py

app.py

@@ -3,56 +3,10 @@ import requests
 import os
 import logging
 from datetime import datetime
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM
 from transformers import pipeline
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-messages = [
-    {"role": "user", "content": "Who are you?"},
-]
+# Initialize the pipeline
 pipe = pipeline("text-generation", model="mistralai/Mixtral-8x7B-Instruct-v0.1")
-pipe(messages)
-
-def stream_chat(
-    message: str,
-    history: list,
-    system_prompt: str,
-    temperature: float = 0.8,
-    max_new_tokens: int = 1024,
-    top_p: float = 1.0,
-    top_k: int = 20,
-    penalty: float = 1.2,
-):
-    conversation = [
-        {"role": "system", "content": system_prompt}
-    ]
-    for prompt, answer in history:
-        conversation.extend([
-            {"role": "user", "content": prompt},
-            {"role": "assistant", "content": answer},
-        ])
-
-    conversation.append({"role": "user", "content": message})
-
-    input_ids = tokenizer.apply_chat_template(conversation, add_generation_prompt=True, return_tensors="pt").to(device)
-
-    streamer = TextStreamer(tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True)
-
-    generate_kwargs = dict(
-        input_ids=input_ids,
-        max_new_tokens=max_new_tokens,
-        do_sample=temperature != 0,
-        top_p=top_p,
-        top_k=top_k,
-        temperature=temperature,
-        eos_token_id=[128001, 128008, 128009],
-        streamer=streamer,
-    )
-
-    output = model.generate(**generate_kwargs)
-    return tokenizer.decode(output[0], skip_special_tokens=True)
 
 app = Flask(__name__)
 
@@ -71,6 +25,16 @@ logging.basicConfig(
 )
 logger = logging.getLogger(__name__)
 
+def stream_chat(message: str, history: list, system_prompt: str):
+    conversation = [{"role": "system", "content": system_prompt}]
+    for prompt, answer in history:
+        conversation.extend([
+            {"role": "user", "content": prompt},
+            {"role": "assistant", "content": answer},
+        ])
+    conversation.append({"role": "user", "content": message})
+    return pipe(conversation)
+
 @app.route('/')
 def index():
     return render_template('index.html')