Update app.py

app.py

@@ -1,83 +1,59 @@
-import os
 import gradio as gr
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer
-import threading
-
-import app_math as app_math  # keeping your existing import
-
-# ---- Model setup ----
-HF_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN")
-MODEL_ID = "HuggingFaceH4/zephyr-7b-beta"
-
-# Automatically map model across available devices (GPU/CPU)
-tokenizer = AutoTokenizer.from_pretrained(
-    MODEL_ID,
-    token=HF_TOKEN,
-)
-
-model = AutoModelForCausalLM.from_pretrained(
-    MODEL_ID,
-    device_map="auto",          # << key change
-    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
-    low_cpu_mem_usage=True,
-    token=HF_TOKEN,
-)
-
-# Ensure pad token is set
-if tokenizer.pad_token_id is None and tokenizer.eos_token_id is not None:
-    tokenizer.pad_token_id = tokenizer.eos_token_id
-
-
-def respond(message, history: list[tuple[str, str]], system_message, max_tokens, temperature, top_p):
-    # Build chat messages
+from witness.witness_rzero import WitnessRZero
+import app_math as app_math
+
+# Instantiate WitnessRZero – change device to "cuda" if GPU is available
+wrz = WitnessRZero(device="cpu")
+
+def respond(
+    message,
+    history: list[tuple[str, str]],
+    system_message,
+    max_tokens,
+    temperature,
+    top_p,
+):
+    # Build conversation history in OpenAI-style message format
     messages = [{"role": "system", "content": system_message}]
-    for u, a in history:
-        if u:
-            messages.append({"role": "user", "content": u})
-        if a:
-            messages.append({"role": "assistant", "content": a})
+    for val in history:
+        if val[0]:
+            messages.append({"role": "user", "content": val[0]})
+        if val[1]:
+            messages.append({"role": "assistant", "content": val[1]})
     messages.append({"role": "user", "content": message})
 
-    # Tokenize with Zephyr's chat template
-    inputs = tokenizer.apply_chat_template(
-        messages,
-        add_generation_prompt=True,
-        tokenize=True,
-        return_tensors="pt",
-    ).to(model.device)
-
-    # Stream generation
-    streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
-
-    gen_kwargs = {
-        "inputs": inputs,
-        "max_new_tokens": int(max_tokens),
-        "do_sample": True,
-        "temperature": float(temperature),
-        "top_p": float(top_p),
-        "eos_token_id": tokenizer.eos_token_id,
-        "pad_token_id": tokenizer.pad_token_id,
-        "streamer": streamer,
-    }
-
-    thread = threading.Thread(target=model.generate, kwargs=gen_kwargs)
-    thread.start()
-
-    partial = ""
-    for new_text in streamer:
-        partial += new_text
-        yield partial
-
-
-# ---- Gradio UI ----
+    # Concatenate all into a single prompt for WitnessRZero
+    prompt = ""
+    for m in messages:
+        prompt += f"{m['role'].capitalize()}: {m['content']}\n"
+
+    response = ""
+    # Stream the output from WitnessRZero.generate()
+    for token in wrz.client.text_generation(
+        prompt,
+        max_new_tokens=max_tokens,
+        stream=True,
+        temperature=temperature,
+        top_p=top_p,
+    ):
+        part = token  # huggingface_hub’s stream yields token text chunks
+        response += part
+        yield response
+
+# Build the Gradio ChatInterface
 demo = gr.ChatInterface(
     respond,
     additional_inputs=[
         gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
         gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
         gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
+        gr.Slider(
+            minimum=0.1,
+            maximum=1.0,
+            value=0.95,
+            step=0.05,
+            label="Top-p (nucleus sampling)",
+        ),
     ],
 )
 
@@ -85,3 +61,4 @@ if __name__ == "__main__":
     demo.launch()
 
 
+