chatgpt version

app.py

@@ -1,26 +1,19 @@
 import gradio as gr
-from transformers import AutoModelForCausalLM, AutoTokenizer
 
-# Load the pre-trained LLM model and tokenizer
-model_name = "microsoft/DialoGPT-medium"
-model = AutoModelForCausalLM.from_pretrained(model_name)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
+# Mock LLM function for demonstration. Replace this with your actual LLM call.
+def ask_llm(question):
+    # Here you would normally interact with an LLM. For demonstration, we'll just echo the question.
+    return f"LLM Response: {question}"
 
-# Define the function to generate the chatbot response
-def chatbot(input_text):
-    input_ids = tokenizer.encode(input_text, return_tensors="pt")
-    output = model.generate(input_ids, max_length=1000, do_sample=True, top_p=0.92, top_k=0, num_return_sequences=1)
-    response = tokenizer.decode(output[0], skip_special_tokens=True)
-    return response
+def chat_with_llm(user_input):
+    return ask_llm(user_input)
 
 # Create the Gradio interface
-chat_interface = gr.Blocks()
+iface = gr.Interface(fn=chat_with_llm,
+                     inputs=gr.inputs.Textbox(lines=2, placeholder="Ask me anything!"),
+                     outputs="text",
+                     title="Chat with LLM",
+                     description="Type your question below and get responses from an LLM.")
 
-with chat_interface:
-    gr.Markdown("# Chatbot")
-    chatbot_input = gr.Textbox(placeholder="Type your message here...")
-    chatbot_output = gr.Textbox(label="Chatbot Response")
-    chat_btn = gr.Button("Send")
-    chat_btn.click(chatbot, inputs=chatbot_input, outputs=chatbot_output)
-
-chat_interface.launch()
+# Launch the app
+iface.launch()