Update app.py

app.py

@@ -1,27 +1,63 @@
 import gradio as gr
 from llama_cpp import Llama
-#from transformers import AutoModelForCausalLM
+
 # Load the model
 llm = Llama.from_pretrained(
-    repo_id = "uonlp/Vistral-7B-Chat-gguf",
+    repo_id="uonlp/Vistral-7B-Chat-gguf",
     filename="ggml-vistral-7B-chat-f16.gguf"
 )
+
 # Define the function to interact with the model
 def chat_with_model(user_input):
     response = llm.create_chat_completion(
-        messages=[
-            {"role": "user", "content": user_input}
-        ]
+        messages=[{"role": "user", "content": user_input}]
     )
     return response['choices'][0]['message']['content']
 
+# Define CSS for clinic-like appearance
+custom_css = """
+    body {
+        background-color: #e6f7ff; /* Light clinic-blue background */
+        font-family: 'Arial', sans-serif;
+    }
+    .gradio-container {
+        border: 2px solid #cce7ff;
+        border-radius: 15px;
+        padding: 20px;
+        background-color: #ffffff;
+    }
+    h1 {
+        color: #2c6693;
+    }
+    input, textarea {
+        border: 1px solid #b3d9ff;
+        border-radius: 10px;
+        padding: 10px;
+        width: 100%;
+        font-size: 16px;
+    }
+    .gr-button {
+        background-color: #4da6ff;
+        border: none;
+        border-radius: 10px;
+        color: white;
+        padding: 10px 20px;
+        cursor: pointer;
+    }
+    .gr-button:hover {
+        background-color: #3399ff;
+    }
+"""
+
 # Create the Gradio interface
 iface = gr.Interface(
     fn=chat_with_model,
     inputs="text",
     outputs="text",
-    title="QA-medical Chatbot",
-    description="Ask the model any medical question !"
+    title="QA-Medical Chatbot",
+    description="Ask the model any medical question!",
+    theme="default",
+    css=custom_css
 )
 
 # Launch the interface