Update app.py

app.py

@@ -1,3 +1,44 @@
 import gradio as gr
 
-gr.load("models/microsoft/Phi-3.5-mini-instruct").launch(share=True)
+# gr.load("models/microsoft/Phi-3.5-mini-instruct").launch(share=True)
+
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Load the model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3.5-mini-instruct", trust_remote_code=True, use_auth_token=True)
+model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-3.5-mini-instruct", trust_remote_code=True, use_auth_token=True)
+
+# Define the role prompt for advertisement assistance
+role_prompt = "You are an advertisement assistant. Respond professionally and helpfully to advertising-related questions.\n\n"
+
+# Function to generate responses
+def generate_response(user_input):
+    # Prepend role information to user input
+    input_text = role_prompt + user_input
+    
+    # Tokenize and generate response
+    inputs = tokenizer(input_text, return_tensors="pt")
+    outputs = model.generate(
+        **inputs, 
+        max_new_tokens=50,   # Increase this if you want longer responses
+        temperature=0.7,     # Adjust for creativity
+        top_p=0.9            # Nucleus sampling to control randomness
+    )
+    
+    # Decode and return the response
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response
+
+# Set up Gradio interface
+interface = gr.Interface(
+    fn=generate_response,
+    inputs="text",
+    outputs="text",
+    title="Advertisement Assistant Chatbot",
+    description="Ask me anything related to advertising. I'm here to help!"
+)
+
+# Launch the Gradio app with sharing enabled
+interface.launch(share=True)