updated working

app.py

@@ -1,4 +1,3 @@
-import torch
 import gradio as gr
 from transformers import AutoTokenizer, AutoModelForCausalLM
 
@@ -9,53 +8,81 @@ model, tokenizer = (
 )
 
 # Function to generate text (suggestions)
-def generate_text(prompt, output_length):
+def generate_text(prompt, output_length, temperature, top_k, top_p):
     inputs = tokenizer(prompt, return_tensors='pt').to(model.device)
     generated_output = model.generate(
         inputs['input_ids'],
         max_length=inputs['input_ids'].shape[-1] + output_length,  # Generate 10 more tokens
         no_repeat_ngram_size=2,
-        temperature=0.8,
-        top_k=50,
-        top_p=0.9,
+        temperature=temperature,
+        top_k=top_k,
+        top_p=top_p,
         do_sample=True,
     )
     output_text = tokenizer.decode(generated_output[0], skip_special_tokens=True)
     return output_text
 
+# Set up the Gradio interface with custom CSS
+with gr.Blocks(css="""
+    #response-text {
+        background-color: #e1bee7; /* Light purple background */
+        border-radius: 8px; /* Rounded corners */
+        padding: 10px; /* Padding inside the textbox */
+        font-size: 16px; /* Font size */
+        color: #4a148c; /* Dark purple text color */
+    }
+""") as demo:
 
-# Set up the Gradio interface
-with gr.Blocks() as demo:
+    # Add a title to the interface
+    gr.Markdown("# Hinglish Chat Prediction")
 
-    # Add a chat interface above the text boxes
-    with gr.Column(scale=4):
-        chatbox = gr.Chatbot(label="Chat", type="messages")
+    # Add a chat interface above the text boxes with reduced size
+    with gr.Row():
+        chatbox = gr.Chatbot(label="Chat", type="messages", height=350, value=[{"role": "assistant", "content": "Kya kar rahe ho"}])
     
     with gr.Row():
         # Create a column for the two text boxes
         with gr.Column(scale=3):
             # Input text box for user input (first column)
-            input_text = gr.Textbox(label="Enter your message", interactive=True)
+            input_text = gr.Textbox(label="Start chatting", interactive=True)
             
+        # Create a separate column for the buttons
+        with gr.Column(scale=1):
+            # Submit button placed above the replace button
+            submit_button = gr.Button("Submit", variant="primary", elem_id="submit-btn")
+
+    with gr.Row():
+        # Create a column for the two text boxes
+        with gr.Column(scale=3):
             # Response text box (second column)
-            response_text = gr.Textbox(label="Response", interactive=False)
+            response_text = gr.Textbox(label="Suggestion", interactive=False, elem_id="response-text")
+
+        # Create a separate column for the buttons
+        with gr.Column(scale=1):
+            replace_button = gr.Button("Replace Text", variant="secondary", elem_id="replace-btn")
+            regenerate_button = gr.Button("Regenerate", variant="secondary", elem_id="regenerate-btn")
 
-        # Create a separate column for the button
+    with gr.Row():
+        # Create a dropdown menu for text generation parameters
         with gr.Column(scale=1):
-            # Button placed beside the text boxes
-            replace_button = gr.Button("Replace Text", elem_id="replace-btn")
+            with gr.Accordion("Change Parameters", open=False):
+                output_length_slider = gr.Slider(1, 20, value=8, label="Output Length", step=1)
+                temperature_slider = gr.Slider(0.1, 1.0, value=0.8, label="Temperature (Controls randomness)")
+                top_k_slider = gr.Slider(1, 100, value=50, label="Top-k (Limits vocabulary size)", step=1)
+                top_p_slider = gr.Slider(0.1, 1.0, value=0.9, label="Top-p (Nucleus sampling)")
     
     # Set up the interaction between input and output
-    def validate_and_generate(prompt, output_length=4):
+    def validate_and_generate(prompt, output_length, temperature, top_k, top_p):
         if prompt.strip():
-            return generate_text(prompt, output_length)
+            print(f"Prompt: {prompt}")
+            return generate_text(prompt, output_length, temperature, top_k, top_p)
 
-    input_text.input(validate_and_generate, inputs=input_text, outputs=response_text)
+    input_text.input(validate_and_generate, inputs=[input_text, output_length_slider, temperature_slider, top_k_slider, top_p_slider], outputs=response_text)
     replace_button.click(lambda x: x, inputs=response_text, outputs=input_text)
 
-    def chat_interaction(prompt, history):
+    def chat_interaction(prompt, history, output_length, temperature, top_k, top_p):
         if prompt.strip():
-            response = generate_text(prompt, output_length=10)
+            response = generate_text(prompt, output_length, temperature, top_k, top_p)
 
             # Exclude the input prompt text from the response
             response = response[len(prompt):].strip()
@@ -63,10 +90,21 @@ with gr.Blocks() as demo:
             history.append({"role": "assistant", "content": response})
             
             # Call validate_and_generate with the response
-            response_text_value = validate_and_generate(response, output_length=10)
+            response_text_value = validate_and_generate(response, output_length, temperature, top_k, top_p)
             return history, response_text_value[len(response):].strip(), ""
+        
+        return history, "", ""
+        
+    def regenerate_text(input_text, history, output_length, temperature, top_k, top_p):
+        if input_text.strip():
+            return generate_text(input_text, output_length, temperature, top_k, top_p)
+        else:
+            last_message = history[-1]["content"]
+            return generate_text(last_message, output_length, temperature, top_k, top_p)[len(last_message):].strip()
 
-    input_text.submit(chat_interaction, inputs=[input_text, chatbox], outputs=[chatbox, response_text, input_text])
+    input_text.submit(chat_interaction, inputs=[input_text, chatbox, output_length_slider, temperature_slider, top_k_slider, top_p_slider], outputs=[chatbox, response_text, input_text])
+    submit_button.click(chat_interaction, inputs=[input_text, chatbox, output_length_slider, temperature_slider, top_k_slider, top_p_slider], outputs=[chatbox, response_text, input_text])
+    regenerate_button.click(regenerate_text, inputs=[input_text, chatbox, output_length_slider, temperature_slider, top_k_slider, top_p_slider], outputs=response_text)
 
 # Launch the interface
 demo.launch()