Update app.py

app.py

@@ -14,24 +14,17 @@ model.to('cpu')
 
 # Define the function that generates text from a prompt
 def generate_text(prompt, temperature):
-    word_count = len(prompt.split())
-    if word_count < 5:
-        return "Please provide at least 5 words in the prompt."
+    print(prompt)
     
-    # Tokenize the prompt to check the number of tokens
-    input_tokens = tokenizer.encode(prompt, return_tensors='pt')
-    if input_tokens.size(1) > 512:
-        return "Please provide an input with fewer than 512 tokens."
-
-    prompt_with_eos = "#CONTEXT# " + prompt + " #TOPIC# "  # Add the string "EOS" to the end of the prompt
+    prompt_with_eos = "#CONTEXT# " + prompt + " #TOPIC# "
     input_tokens = tokenizer.encode(prompt_with_eos, return_tensors='pt')
 
     input_tokens = input_tokens.to('cpu')
 
-    generated_text = prompt_with_eos  # Start with the initial prompt plus "EOS"
+    generated_text = prompt_with_eos
     prompt_length = len(generated_text)
 
-    for _ in range(80):  # Adjust the range to control the number of tokens generated
+    for _ in range(80):
         with torch.no_grad():
             outputs = model(input_tokens)
             predictions = outputs.logits[:, -1, :] / temperature
@@ -40,21 +33,37 @@ def generate_text(prompt, temperature):
         input_tokens = torch.cat((input_tokens, next_token), dim=1)
         
         decoded_token = tokenizer.decode(next_token.item())
-        generated_text += decoded_token  # Append the new token to the generated text
-        if decoded_token == "#":  # Stop if the end of sequence token is generated
+        generated_text += decoded_token
+        if decoded_token == "#":
             break
-        yield generated_text[prompt_length:]  # Yield the generated text excluding the initial prompt plus "EOS"
+        yield generated_text[prompt_length:]
+
+# Custom CSS for a modern look
+custom_css = """
+body {font-family: 'Arial', sans-serif; background-color: #f0f2f5; margin: 0; padding: 0;}
+.gradio-container {width: 100%; max-width: 900px; margin: 5% auto; background: white; box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1); border-radius: 10px; overflow: hidden;}
+input, textarea {width: 100%; padding: 10px; border: 1px solid #ccc; border-radius: 5px; margin-top: 5px; font-size: 16px;}
+button {background-color: #4CAF50; color: white; padding: 10px 20px; border: none; border-radius: 5px; font-size: 16px; cursor: pointer;}
+button:hover {background-color: #45a049;}
+h1, h3 {color: #333;}
+.slider {width: 100%; margin-top: 20px;}
+footer {text-align: center; padding: 20px; background-color: #4CAF50; color: white; font-size: 16px;}
+"""
 
 # Create a Gradio interface with a text input and a slider for temperature
 interface = gr.Interface(
     fn=generate_text,
     inputs=[
-        gr.Textbox(lines=2, placeholder="Enter your prompt here..."),
-        gr.Slider(minimum=0.1, maximum=1.0, value=0.3, label="Temperature"),
+        gr.Textbox(lines=2, placeholder="Enter your prompt here...", label="Prompt"),
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.3, label="Temperature", step=0.1),
     ],
-    outputs=gr.Textbox(),
+    outputs=gr.Textbox(label="Generated Text"),
     live=False,
-    description="TopicGPT processes the input and returns a reasonably accurate guess of the topic/theme of a given conversation."
+    description="TopicGPT processes the input and returns a reasonably accurate guess of the topic/theme of a given conversation.",
+    title="TopicGPT: Theme & Topic Guessing",
+    layout="vertical",
+    theme="huggingface",
+    css=custom_css,
 )
 
 interface.launch()