Update app.py

app.py

@@ -4,122 +4,99 @@ from huggingface_hub import login
 from transformers import AutoModelForSeq2SeqLM, T5Tokenizer
 from peft import PeftModel, PeftConfig
 
+# Hugging Face login
 token = os.environ.get("token")
 login(token)
-print("login is succesful")
-max_length=150
+print("Login is successful")
 
+# Model and tokenizer setup
 MODEL_NAME = "google/flan-t5-base"
 tokenizer = T5Tokenizer.from_pretrained(MODEL_NAME, token=token)
 config = PeftConfig.from_pretrained("Komal-patra/results")
 base_model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-base")
 model = PeftModel.from_pretrained(base_model, "Komal-patra/results")
 
-#gr.Interface.from_pipeline(pipe).launch()
-
+# Text generation function
 def generate_text(prompt, max_length=150):
-    """Generates text using the PEFT model.
-    Args:
-        prompt (str): The user-provided prompt to start the generation.
-           Returns:
-        str: The generated text.
-    """
-
-
-    # Preprocess the prompt
-    # inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
     inputs = tokenizer(prompt, return_tensors="pt")
-
-    # Generate text using beam search
     outputs = model.generate(
-       input_ids = inputs["input_ids"], 
-       max_length=max_length, 
-       num_beams=1,
-       repetition_penalty=2.2
-       )
-    
-    print(outputs)
-    # Decode the generated tokens
+        input_ids=inputs["input_ids"],
+        max_length=max_length,
+        num_beams=1,
+        repetition_penalty=2.2
+    )
     generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    print("show the generated text", generated_text)
     return generated_text
 
-#############
-custom_css="""
+# Custom CSS for the UI
+custom_css = """
 .message.pending {
     background: #A8C4D6;
 }
 /* Response message */
 .message.bot.svelte-1s78gfg.message-bubble-border {
-    /* background:  white; */
-    border-color: #266B99
+    border-color: #266B99;
 }
 /* User message */
-.message.user.svelte-1s78gfg.message-bubble-border{
+.message.user.svelte-1s78gfg.message-bubble-border {
     background: #9DDDF9;
-    border-color: #9DDDF9
-    
+    border-color: #9DDDF9;
 }   
 /* For both user and response message as per the document */
 span.md.svelte-8tpqd2.chatbot.prose p {
-    color: #266B99; 
+    color: #266B99;
 }
-/* Chatbot comtainer */
-.gradio-container{
-    /* background:  #84D5F7 */
+/* Chatbot container */
+.gradio-container {
+    background: #1c1c1c; /* Dark background */
+    color: white; /* Light text color */
 }
 /* RED (Hex: #DB1616) for action buttons and links only */
 .clear-btn {
-  background: #DB1616;
-  color: white;
+    background: #DB1616;
+    color: white;
 }
-/* #84D5F7 - Primary colours are set to be used for all sorts */
+/* Primary colors are set to be used for all sorts */
 .submit-btn {
-  background: #266B99; 
-  color: white;
+    background: #266B99;
+    color: white;
 }
 """
 
-### working correctly but  the welcoming message isnt rendering
+# Gradio interface setup
 with gr.Blocks(css=custom_css) as demo:
-    chatbot = gr.Chatbot()
-    msg = gr.Textbox(placeholder="Ask your question...")  # Add placeholder text
-    submit_button = gr.Button("Submit", elem_classes="submit-btn")
-    clear = gr.Button("Clear", elem_classes="clear-btn")
-
-
-    def user(user_message, history):
-        return "", history + [[user_message, None]]
-
-
-    def bot(history):
-      history[-1][1] = ""  # Update the last bot message (welcome message or response)
-      if len(history) < 0:  # Check if it's the first interaction
-          bot_message = "Hi there! How can I help you today?"
-          history.append([None, bot_message])  # Add welcome message to history
-          for character in bot_message:
-            history[-1][1] += character
-            yield history  # Yield the updated history character by character
-
-      else:
-          previous_message = history[-1][0]  # Access the previous user message
-          bot_message = generate_text(previous_message)  # Generate response based on previous message
-          for character in bot_message:
-            history[-1][1] += character
-            yield history  # Yield the updated history character by character
-
-
-
-    # Connect submit button to user and then bot functions
-    submit_button.click(user, [msg,  chatbot], [msg, chatbot], queue=False).then(
-        bot, chatbot, chatbot
-    )
-
-    # Trigger user function on Enter key press (same chain as submit button)
-    msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
-        bot, chatbot, chatbot
-    )
-
-    clear.click(lambda: None, None, chatbot, queue=False)
-
-demo.launch()
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.Markdown("<h2>My chats</h2>")
+            chat_topics = gr.Markdown("<!-- Dynamic content -->")
+
+        with gr.Column(scale=3):
+            gr.Markdown("<h1>Ask a question about the EU AI Act</h1>")
+            chatbot = gr.Chatbot()
+            msg = gr.Textbox(placeholder="Ask your question...", show_label=False)  # Add placeholder text
+            submit_button = gr.Button("Submit", elem_classes="submit-btn")
+            clear = gr.Button("Clear", elem_classes="clear-btn")
+
+            def user(user_message, history):
+                return "", history + [[user_message, None]]
+
+            def bot(history):
+                if len(history) == 1:  # Check if it's the first interaction
+                    bot_message = "Hi there! How can I help you today?"
+                    history[-1][1] = bot_message  # Add welcome message to history
+                else:
+                    history[-1][1] = ""  # Clear the last bot message
+                    previous_message = history[-1][0]  # Access the previous user message
+                    bot_message = generate_text(previous_message)  # Generate response based on previous message
+                    history[-1][1] = bot_message  # Update the last bot message
+                return history
+
+            submit_button.click(user, [msg, chatbot], [msg, chatbot], queue=False).then(
+                bot, chatbot, chatbot
+            )
+            msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
+                bot, chatbot, chatbot
+            )
+            clear.click(lambda: None, None, chatbot, queue=False)
+
+demo.launch()