Update app.py

app.py

@@ -1,12 +1,34 @@
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
 import gradio as gr
-from huggingface_hub import InferenceClient
 
-"""
-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("Futuresony/future_ai_12_10_2024.gguf")
+# --------------------
+# Load Base Model and LoRA Adapter
+# --------------------
+def load_model_and_adapter():
+    base_model_name = "unsloth/Llama-3.2-3B-Instruct"  # Replace with your base model name
+    adapter_repo = "Futuresony/future_ai_12_10_2024"   # Your Hugging Face LoRA repo
+    
+    # Load tokenizer and base model
+    tokenizer = AutoTokenizer.from_pretrained(base_model_name)
+    base_model = AutoModelForCausalLM.from_pretrained(
+        base_model_name,
+        torch_dtype=torch.float16,  # Use float16 for efficiency if GPU is available
+        device_map="auto"           # Automatically map to GPU or CPU
+    )
+    
+    # Load LoRA adapter
+    model = PeftModel.from_pretrained(base_model, adapter_repo)
+    model.eval()  # Set to evaluation mode
+    return tokenizer, model
 
+# Load the model and tokenizer once
+tokenizer, model = load_model_and_adapter()
 
+# --------------------
+# Generate Response Function
+# --------------------
 def respond(
     message,
     history: list[tuple[str, str]],
@@ -16,50 +38,48 @@ def respond(
     top_p,
 ):
     messages = [{"role": "system", "content": system_message}]
-
+    
     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})
 
-    response = ""
+    # Prepare input prompt for generation
+    prompt = "\n".join([f"{m['role']}: {m['content']}" for m in messages])
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
+    # Generate response
+    outputs = model.generate(
+        **inputs,
+        max_length=max_tokens,
         temperature=temperature,
         top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
+        pad_token_id=tokenizer.eos_token_id,
+        eos_token_id=tokenizer.eos_token_id
+    )
+    
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    response = response.split("assistant:")[-1].strip()  # Clean response
+    return response
 
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
+# --------------------
+# Gradio Interface
+# --------------------
 demo = gr.ChatInterface(
     respond,
     additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
+        gr.Textbox(value="You are a helpful assistant.", 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)"),
     ],
 )
 
-
+# --------------------
+# Launch the Interface
+# --------------------
 if __name__ == "__main__":
     demo.launch()
-