Update app.py

app.py

@@ -1,10 +1,85 @@
 import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import torch
 
-with gr.Blocks(fill_height=True) as demo:
-    with gr.Sidebar():
-        gr.Markdown("# Inference Provider")
-        gr.Markdown("This Space showcases the Salesforce/codet5-base model, served by the hf-inference API. Sign in with your Hugging Face account to use this API.")
-        button = gr.LoginButton("Sign in")
-    gr.load("models/Salesforce/codet5-base", accept_token=button, provider="hf-inference")
+# Load the Salesforce/codet5-base model and tokenizer
+# We are using the 'Salesforce/codet5-base' model, which is a pre-trained model for code-related tasks.
+# The AutoTokenizer and AutoModelForSeq2SeqLM classes from the Transformers library are used to load the model and tokenizer.
+model_name = "Salesforce/codet5-base"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+
+# Function to generate code
+# This function takes a prompt (code-related query) as input and generates code based on that prompt.
+# It uses the loaded model and tokenizer to encode the input, generate the output, and then decode the generated text.
+def generate_code(prompt, max_length=100):
+    # Encode the input prompt using the tokenizer
+    input_ids = tokenizer.encode(prompt, return_tensors="pt")
+    
+    # Generate the output using the model
+    # The `model.generate()` function is used to generate the output sequence based on the input.
+    # The `max_length` parameter sets the maximum length of the generated sequence.
+    # The `num_return_sequences` parameter specifies the number of output sequences to be generated (in this case, 1).
+    output = model.generate(input_ids, max_length=max_length, num_return_sequences=1)
+    
+    # Decode the generated output to get the actual code
+    # The `tokenizer.decode()` function is used to convert the output token IDs back to readable text.
+    # The `skip_special_tokens=True` argument ensures that any special tokens (e.g., start/end of sequence tokens) are removed from the output.
+    generated_code = tokenizer.decode(output[0], skip_special_tokens=True)
+    
+    # Return the generated code
+    return generated_code
+
+# Function to handle chat interaction
+# This function is responsible for managing the chat interaction between the user and the system.
+# It takes the user's message and the chat history as input, and returns the system's response and the updated chat history.
+def chat_interaction(message, history):
+    # Initialize the chat history if it's not provided
+    history = history or []
+    
+    # Generate the response using the `generate_code` function
+    response = generate_code(message)
     
-demo.launch()
+    # Update the chat history by appending the user's message and the system's response
+    history.append((message, response))
+    
+    # Return the empty message (to clear the input field) and the updated chat history
+    return "", history
+
+# Create the Gradio interface
+# The Gradio library is used to create an interactive web interface for the chat application.
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    # Add a Markdown title for the interface
+    gr.Markdown("# S-Dreamer Salesforce/codet5-base Chat Interface")
+    
+    # Create a row with two columns
+    with gr.Row():
+        # Left column for the chat area
+        with gr.Column(scale=3):
+            # Add a chatbot component to display the chat history
+            chatbot = gr.Chatbot(height=400)
+            # Add a text input field for the user to enter messages
+            message = gr.Textbox(label="Enter your code-related query", placeholder="Type your message here...")
+            # Add a submit button
+            submit_button = gr.Button("Submit")
+        
+        # Right column for the feature list
+        with gr.Column(scale=1):
+            # Add Markdown sections for the features
+            gr.Markdown("## Features")
+            gr.Markdown("- Code generation")
+            gr.Markdown("- Code completion")
+            gr.Markdown("- Code explanation")
+            gr.Markdown("- Error correction")
+            
+            # Add a clear button to reset the chat
+            clear_button = gr.Button("Clear Chat")
+    
+    # Connect the submit button to the `chat_interaction` function
+    submit_button.click(chat_interaction, inputs=[message, chatbot], outputs=[message, chatbot])
+    
+    # Connect the clear button to a lambda function that clears the chat
+    clear_button.click(lambda: None, outputs=[chatbot], inputs=[])
+
+# Launch the Gradio interface
+demo.launch()