Update app.py

app.py

@@ -1,54 +1,76 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("NorwAI/NorwAI-Llama2-7B")
+# --- Configuration ---
+MODEL_NAME = "NorwAI/NorwAI-Llama2-7B" #"google/gemma-2-9b"
 
+# --- Model Loading (Explicit) ---
+# Use a try-except block to handle potential loading errors
+try:
+    # Load the tokenizer
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+    # Load the model with appropriate configurations.
+    model = AutoModelForCausalLM.from_pretrained(
+        MODEL_NAME,
+        device_map="auto",  # Use "auto" to let Transformers handle device placement.
+        torch_dtype=torch.bfloat16,  # Use bfloat16 for reduced memory usage (if supported by your hardware).
+    )
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+except Exception as e:
+    print(f"Error loading model: {e}")
+    # You might want to raise the exception or exit gracefully here.
+    raise
 
-    messages.append({"role": "user", "content": message})
+# --- Inference Function ---
+def respond(message, history, system_message, max_tokens, temperature, top_p):
+    try:
+        # Build the conversation history.  Use the correct roles ("user", "model").
+        formatted_history = ""
+        for user_msg, model_msg in history:
+            formatted_history += f"<start_of_turn>user\n{user_msg}<end_of_turn>\n"
+            if model_msg:  # Check if model_msg is not None
+                formatted_history += f"<start_of_turn>model\n{model_msg}<end_of_turn>\n"
 
-    response = ""
+        # Combine system message, history, and current message.
+        prompt = f"<start_of_turn>system\n{system_message}<end_of_turn>\n{formatted_history}<start_of_turn>user\n{message}<end_of_turn>\n<start_of_turn>model\n"
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
+        # Tokenize the input
+        inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
 
-        response += token
-        yield response
+        # Generate text with streaming (important for a chatbot).
+        streamer = model.generate(
+            **inputs,
+            max_new_tokens=max_tokens,
+            temperature=temperature,
+            top_p=top_p,
+            do_sample=True,  # Enable sampling for more diverse responses.
+            streamer=True, #for stream
+            pad_token_id=tokenizer.eos_token_id
+        )
 
+        # Accumulate the response.  Decode in chunks.
+        response = ""
+        for chunk in streamer:
+            if chunk is not None:
+                response += tokenizer.decode(chunk[0], skip_special_tokens=True)
+                yield response
+
+
+    except Exception as e:
+        print(f"Error during inference: {e}")
+        yield "An error occurred during generation."
+        return
+
+# --- Gradio Interface ---
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/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=0.7, step=0.1, label="Temperature"),
         gr.Slider(
             minimum=0.1,
             maximum=1.0,
@@ -59,6 +81,5 @@ demo = gr.ChatInterface(
     ],
 )
 
-
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()