Update app.py

app.py

@@ -1,70 +1,49 @@
+import transformers
 import gradio as gr
+import git
+import os
+os.system("pip install --upgrade pip")
 
-def chatbot(message):
-    if "conversation" not in gr.Interface.cache:
-        # Initialize conversation as an empty list
-        gr.Interface.cache["conversation"] = []
-        
-    # Add user input to the conversation history
-    gr.Interface.cache["conversation"].append("User: " + message)
-    
-    # Define the bot's responses based on user input
-    if message.lower() == "hello":
-        response = "Hi, how can I help you?"
-    elif message.lower() == "how are you?":
-        response = "I'm doing well, thank you!"
-    elif message.lower() == "goodbye":
-        response = "Goodbye! Have a great day."
-    else:
-        response = "Sorry, I didn't understand that. Can you please rephrase?"
-    
-    # Add bot response to the conversation history
-    gradio.Interface.cache["conversation"].append("Bot: " + response)
-    
-    # Return the bot's response
-    return response
+#Load arabert preprocessor
+import git
+git.Git("arabert").clone("https://github.com/aub-mind/arabert")
+from arabert.preprocess import ArabertPreprocessor
+arabert_prep = ArabertPreprocessor(model_name="bert-base-arabert", keep_emojis=False)
 
-iface = gr.Interface(fn=chatbot, inputs="text", outputs="text")
 
-# Customize the layout to display the conversation
-iface.interface_html = """
-<div>
-    <div>
-        <textarea id="input_text" rows="4" cols="50" placeholder="User input"></textarea>
-        <button id="submit_button">Send</button>
-    </div>
-    <div id="conversation" style="margin-top: 10px;"></div>
-</div>
+#Load Model
+from transformers import EncoderDecoderModel, AutoTokenizer
+tokenizer = AutoTokenizer.from_pretrained("tareknaous/bert2bert-empathetic-response-msa")
+model = EncoderDecoderModel.from_pretrained("tareknaous/bert2bert-empathetic-response-msa")
+model.eval()
 
-<script>
-    // Function to update the conversation display
-    function updateConversation() {
-        var conversation = gradio.interfaceInstances[0].cache["conversation"];
-        var conversationDiv = document.getElementById("conversation");
-        conversationDiv.innerHTML = "";
-        for (var i = 0; i < conversation.length; i++) {
-            var message = conversation[i];
-            var messageDiv = document.createElement("div");
-            messageDiv.innerHTML = message;
-            conversationDiv.appendChild(messageDiv);
-        }
-    }
-    
-    // Add event listener for the submit button
-    var submitButton = document.getElementById("submit_button");
-    submitButton.addEventListener("click", function() {
-        var inputText = document.getElementById("input_text").value;
-        if (inputText) {
-            gradio.interfaceInstances[0].input_interfaces[0].setValue(inputText);
-            gradio.interfaceInstances[0].submit();
-        }
-    });
-    
-    // Update the conversation display on page load
-    window.onload = function() {
-        updateConversation();
-    }
-</script>
-"""
+def generate_response(text, minimum_length, p, temperature):
+  text_clean = arabert_prep.preprocess(text)
+  inputs = tokenizer.encode_plus(text_clean,return_tensors='pt')
+  outputs = model.generate(input_ids = inputs.input_ids,
+                   attention_mask = inputs.attention_mask,
+                   do_sample = True,
+                   min_length=minimum_length,
+                   top_p = p,
+                   temperature = temperature)
+  preds = tokenizer.batch_decode(outputs) 
+  response = str(preds)
+  response = response.replace("\'", '')
+  response = response.replace("[[CLS]", '')
+  response = response.replace("[SEP]]", '')
+  response = str(arabert_prep.desegment(response))
+  return response
 
-iface.launch()
+# title = 'Empathetic Response Generation in Arabic'
+# description = 'This demo is for a BERT2BERT model trained for single-turn open-domain empathetic dialogue response generation in Modern Standard Arabic'
+
+with gr.Blocks() as demo:
+    gr.Markdown("Empathetic Response Generation in Arabic")
+    chatbot = gr.Chatbot()
+    output-slider=gr.Slider(5, 20, step=1, label='Minimum Output Length')
+    top-p-slider=gr.Slider(0.7, 1, step=0.1,  label='Top-P')
+    temperature-slider=gr.Slider(1, 3, step=0.1, label='Temperature')
+    msg = gr.Textbox()
+    clear = gr.Button("Clear")
+    msg.submit(generate_response, [msg, chatbot,output-slider,top-p-slider,temperature-slider], [msg, chatbot])
+    clear.click(lambda: None, None, chatbot, queue=False)