Delete app.py

app.py

@@ -1,54 +0,0 @@
-import gradio as gr
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import torch
-
-model_path = "BytArch/source-mini"
-
-tokenizer = AutoTokenizer.from_pretrained(model_path)
-model = AutoModelForCausalLM.from_pretrained(model_path)
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model.to(device)
-
-def chat_interface(user_message):
-    system_prompt_text = (
-        "You are source-mini-v2, a chatbot developed by BytArch. "
-        "Your creator is Joshua Kelly. Your purpose is to assist users with "
-        "nursing and medical inquiries and provide helpful guidance based on "
-        "pre-trained data. BytArch focuses on creating AI models for practical "
-        "and educational purposes, especially in nursing and medical assistance. "
-        "BytArch models like source-mini-v2 are trained on curated medical and "
-        "nursing datasets to provide accurate and safe information. They are "
-        "designed to assist users responsibly, but they are not substitutes for "
-        "professional advice."
-    )
-
-    prompt = f"System: {system_prompt_text}\nUser: {user_message}\nAssistant:"
-
-    inputs = tokenizer(prompt, return_tensors="pt").to(device)
-
-    outputs = model.generate(
-        **inputs,
-        max_new_tokens=200,
-        pad_token_id=tokenizer.eos_token_id,
-        do_sample=True,
-        top_k=30,
-        top_p=0.85,
-        temperature=0.7,
-    )
-
-    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-
-    if "Assistant:" in response:
-        return response.split("Assistant:", 1)[1].strip()
-    return response.strip()
-
-iface = gr.Interface(
-    fn=chat_interface,
-    inputs="text",
-    outputs="text",
-    title="source-mini Medical Assistant"
-)
-
-if __name__ == "__main__":
-    iface.launch(server_name="0.0.0.0", server_port=7860)