Update app.py

app.py

@@ -1,12 +1,13 @@
 import re
 import openai
+import transformers
 import gradio as gr
 
 # Define a regular expression to match Python code blocks
 code_pattern = re.compile(r"```python\n(.*?)\n```", re.DOTALL)
 
-# Define the chat function
-def chat(api_key, model, message):
+# Define the chat function for OpenAI API
+def openai_chat(api_key, model, message):
     # Check if an API key has been provided
     if api_key is None:
         return "Please enter your OpenAI API key and try again."
@@ -18,7 +19,7 @@ def chat(api_key, model, message):
     highlighted_message = message
     for code in code_blocks:
         highlighted_code = f'<span style="background-color: #FFFF00;">{code}</span>'
-        highlighted_message = highlighted_message.replace(f'```python\n{code}\n```', highlighted_code)
+        highlighted_message = re.sub(f'```python\n{code}\n```', highlighted_code, highlighted_message, flags=re.IGNORECASE)
     
     # Set up the OpenAI API request
     response = openai.Completion.create(
@@ -38,10 +39,33 @@ def chat(api_key, model, message):
     highlighted_bot_response = bot_response
     for code in code_blocks:
         highlighted_code = f'<span style="background-color: #FFFF00;">{code}</span>'
-        highlighted_bot_response = highlighted_bot_response.replace(code, highlighted_code)
+        highlighted_bot_response = re.sub(f'{code}', highlighted_code, highlighted_bot_response, flags=re.IGNORECASE)
     
     return highlighted_bot_response
 
+# Define the chat function for Hugging Face API
+def hf_chat(model_name, message):
+    # Load the model
+    tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
+    model = transformers.AutoModelForCausalLM.from_pretrained(model_name)
+    
+    # Encode the message using the model's tokenizer
+    input_ids = tokenizer.encode(message, return_tensors="pt")
+    
+    # Generate a response from the model
+    output = model.generate(
+        input_ids=input_ids,
+        max_length=1024,
+        do_sample=True,
+        top_p=0.9,
+        top_k=0,
+    )
+    
+    # Decode the response
+    bot_response = tokenizer.decode(output[0], skip_special_tokens=True)
+    
+    return bot_response
+
 # Define a function to extract code blocks from a string
 def extract_code_blocks(text):
     code_blocks = []
@@ -52,19 +76,38 @@ def extract_code_blocks(text):
 # Define the Gradio interface
 api_key_input = gr.inputs.Textbox(label="OpenAI API Key", default=None)
 model_input = gr.inputs.Dropdown(
-    label="Select model",
-    choices=["text-davinci-003", "text-davinci-002"],
-    default="text-davinci-003",
+    label="Select OpenAI model",
+    choices=["davinci", "davinci-002", "davinci-003"],
+    default="davinci-003",
 )
 message_input = gr.inputs.Textbox(label="Enter your message here")
 output = gr.outputs.HTML(label="Bot response")
 
-chat_button = gr.Interface(
-    fn=chat,
+openai_chat_button = gr.Interface(
+    fn=openai_chat,
     inputs=[api_key_input, model_input, message_input],
     outputs=output,
     title="OpenAI Chatbot",
-    description="Enter your message below to chat with an AI",
+    description="Enter your message below to chat with an OpenAI AI",
+    theme="compact",
+    layout="vertical",
+    allow_flagging=False,
+    allow_screenshot=False,
+    allow_share=False,
+)
+
+hf_chat_models = ["microsoft/DialoGPT-large", "microsoft/DialoGPT-medium", "microsoft/DialoGPT-small"]
+hf_model_input = gr.inputs.Dropdown(
+    label="Select Hugging Face model",
+    choices=hf_chat_models,
+    default=hf_chat_models[0],
+)
+hf_chat_button = gr.Interface(
+    fn=hf_chat,
+    inputs=[hf_model_input, message_input],
+    outputs=output,
+    title="Hugging Face Chatbot",
+    description="Enter your message below to chat with a Hugging Face AI",
     theme="compact",
     layout="vertical",
     allow_flagging=False,
@@ -72,5 +115,45 @@ chat_button = gr.Interface(
     allow_share=False,
 )
 
-# Launch the app
-chat_button.launch()
+chat_button = gr.Interface(
+    inputs="text",
+    outputs=["text", "text"],
+    layout="vertical",
+    title="Chatbot",
+    description="Enter your message below to chat with an AI",
+    theme="compact",
+    allow_flagging=False,
+    allow_screenshot=False,
+    allow_share=False,
+    examples=[
+        ["Hello, how are you?", ""],
+        ["What's the weather like today?", ""],
+        ["Can you help me with some Python code?", "```python\nfor i in range(10):\n    print(i)\n```"],
+    ],
+)
+
+chat_button.set_config(gr.InterfaceConfig(
+    dependencies=["transformers"],
+    inputs=[gr.InterfaceComponent(model_input, label="OpenAI Model", visible=False)],
+    outputs=[gr.InterfaceComponent(output, label="Bot Response"), gr.InterfaceComponent(api_key_input, label="OpenAI API Key", visible=False)],
+    layout="vertical",
+    title="OpenAI Chatbot (API Key Required)",
+))
+
+chat_button.set_config(gr.InterfaceConfig(
+    dependencies=["transformers"],
+    inputs=[gr.InterfaceComponent(hf_model_input, label="Hugging Face Model", visible=False)],
+    outputs=[gr.InterfaceComponent(output, label="Bot Response")],
+    layout="vertical",
+    title="Hugging Face Chatbot",
+))
+
+chat_button.set_config(gr.InterfaceConfig(
+    dependencies=["transformers"],
+    inputs=[gr.InterfaceComponent(model_input, label="OpenAI Model"), gr.InterfaceComponent(hf_model_input, label="Hugging Face Model", visible=False)],
+    outputs=[gr.InterfaceComponent(output, label="Bot Response"), gr.InterfaceComponent(api_key_input, label="OpenAI API Key")],
+    layout="vertical",
+    title="Chatbot",
+))
+
+chat_button.test_launch()  # Launch the Gradio interface