Update app.py

app.py

@@ -1,64 +1,37 @@
 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("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    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 = ""
-
-    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
-
-        response += token
-        yield response
-
-
-"""
-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=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+from peft import PeftModel
+
+# Model names
+base_model_name = "unsloth/qwen2.5-coder-3b-instruct-bnb-4bit"
+lora_model_name = "MarioCap/OCodeR_500-Qwen-2.5-Code-3B"
+
+# Load tokenizer and base model
+tokenizer = AutoTokenizer.from_pretrained(base_model_name)
+base_model = AutoModelForCausalLM.from_pretrained(base_model_name, device_map="auto", trust_remote_code=True)
+model = PeftModel.from_pretrained(base_model, lora_model_name)
+
+# Create pipeline
+pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
+
+# Chat/inference function
+def generate(prompt, max_new_tokens=200, temperature=0.7):
+    response = pipe(prompt, max_new_tokens=max_new_tokens, temperature=temperature, do_sample=True)
+    return response[0]['generated_text']
+
+# Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("### 💡 Code Assistant - OCodeR + Qwen 2.5")
+    with gr.Row():
+        prompt = gr.Textbox(label="Enter your coding prompt", placeholder="Write a Python function to reverse a string...")
+    with gr.Row():
+        max_tokens = gr.Slider(50, 1024, value=200, step=50, label="Max New Tokens")
+        temp = gr.Slider(0.1, 1.5, value=0.7, step=0.1, label="Temperature")
+    with gr.Row():
+        output = gr.Textbox(label="Generated Output")
+    with gr.Row():
+        submit = gr.Button("Generate")
+    
+    submit.click(fn=generate, inputs=[prompt, max_tokens, temp], outputs=output)
+
+demo.launch()