fix model handling + more part 2

app.py

@@ -1,106 +1,55 @@
 import os
-from threading import Thread
-
 import gradio as gr
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
-
+from transformers import pipeline
 
 MODEL_ID = os.getenv("MODEL_ID", "GenueAI/Inelly-4.5-Blaze")
-
-tokenizer = None
-model = None
-
+MODEL_NAME = os.getenv("MODEL_NAME", "Inelly 4.5 Blaze")
+pipe = None
 
 def load_model():
-    global tokenizer, model
-
-    if tokenizer is not None and model is not None:
-        return tokenizer, model
-
-    tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
-    if tokenizer.pad_token_id is None:
-        tokenizer.pad_token = tokenizer.eos_token
-
-    kwargs = {
-        "trust_remote_code": True,
-        "torch_dtype": torch.float16 if torch.cuda.is_available() else torch.float32,
-        "low_cpu_mem_usage": True,
-    }
-
-    if torch.cuda.is_available():
-        kwargs["device_map"] = "auto"
-
-    model = AutoModelForCausalLM.from_pretrained(MODEL_ID, **kwargs)
-    if not torch.cuda.is_available():
-        model = model.to("cpu")
-
-    model.eval()
-    return tokenizer, model
-
-
-def build_prompt(message, history, system_prompt):
+    global pipe
+    pipe = pipeline(
+        "text-generation", 
+        model=MODEL_ID, 
+        torch_dtype="auto",
+        model_kwargs={
+            "low_cpu_mem_usage": True, 
+            "device_map": "sequential"
+        }
+    )
+
+def build_prompt(message_text, history, system_prompt):
     messages = []
     if system_prompt.strip():
         messages.append({"role": "system", "content": system_prompt.strip()})
 
-    for user_message, assistant_message in history:
-        if user_message:
-            messages.append({"role": "user", "content": user_message})
-        if assistant_message:
-            messages.append({"role": "assistant", "content": assistant_message})
-
-    messages.append({"role": "user", "content": message})
-
-    tok, _ = load_model()
-    if hasattr(tok, "apply_chat_template") and tok.chat_template:
-        return tok.apply_chat_template(
-            messages,
-            tokenize=False,
-            add_generation_prompt=True,
-        )
-
-    prompt = ""
-    for item in messages:
-        role = item["role"].capitalize()
-        prompt += f"{role}: {item['content']}\n"
-    return prompt + "Assistant:"
-
-
-def chat(message, history, system_prompt, max_new_tokens, temperature, top_p, repetition_penalty):
-    if not message.strip():
-        yield ""
-        return
-
-    tok, mdl = load_model()
-    prompt = build_prompt(message, history, system_prompt)
-    inputs = tok(prompt, return_tensors="pt").to(mdl.device)
-
-    streamer = TextIteratorStreamer(tok, skip_prompt=True, skip_special_tokens=True)
-    generation_kwargs = {
-        **inputs,
-        "streamer": streamer,
-        "max_new_tokens": int(max_new_tokens),
-        "temperature": float(temperature),
-        "top_p": float(top_p),
-        "repetition_penalty": float(repetition_penalty),
-        "do_sample": temperature > 0,
-        "pad_token_id": tok.pad_token_id,
-        "eos_token_id": tok.eos_token_id,
-    }
-
-    thread = Thread(target=mdl.generate, kwargs=generation_kwargs)
-    thread.start()
-
-    response = ""
-    for token in streamer:
-        response += token
-        yield response
-
-
-with gr.Blocks(title="Matrix Prime 8B Chat") as demo:
-    gr.Markdown("# Matrix Prime 8B Chat")
-    gr.Markdown(f"Chat with `{MODEL_ID}` from Hugging Face.")
+    for msg in history:
+        messages.append({"role": msg["role"], "content": msg["content"]})
+        
+    messages.append({"role": "user", "content": message_text})
+    return messages
+
+def chat(message, history, system_prompt):
+    message_text = message.get("text", "").strip() if isinstance(message, dict) else str(message).strip()
+    
+    if not message_text:
+        return ""
+
+    prompt = build_prompt(message_text, history, system_prompt)
+    
+    outputs = pipe(prompt) 
+    
+    try:
+        return outputs[0]["generated_text"][-1]["content"]
+    except (KeyError, IndexError, TypeError):
+        try:
+            return outputs["generated_text"][-1]["content"]
+        except (KeyError, IndexError, TypeError):
+            return str(outputs)
+
+with gr.Blocks(title="Genue Chat") as demo:
+    gr.Markdown("# Genue Chat")
+    gr.Markdown(f"Chat with {MODEL_NAME} from Hugging Face.")
 
     with gr.Row():
         with gr.Column(scale=4):
@@ -112,21 +61,16 @@ with gr.Blocks(title="Matrix Prime 8B Chat") as demo:
                         value="You are a helpful assistant.",
                         lines=3,
                     ),
-                    gr.Slider(64, 4096, value=512, step=32, label="Max new tokens"),
-                    gr.Slider(0.0, 2.0, value=0.7, step=0.05, label="Temperature"),
-                    gr.Slider(0.05, 1.0, value=0.9, step=0.05, label="Top-p"),
-                    gr.Slider(1.0, 2.0, value=1.1, step=0.05, label="Repetition penalty"),
                 ],
                 textbox=gr.Textbox(
-                    placeholder="Ask Matrix Prime 8B anything...",
+                    label="Prompt",
+                    placeholder="Ask anything...",
                     container=False,
                     scale=7,
                 ),
-                submit_btn="Send",
-                stop_btn="Stop"
             )
 
-
 if __name__ == "__main__":
+    load_model()
     demo.queue()
     demo.launch()