Update app.py

app.py

@@ -1,64 +1,62 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
-
+import transformers_gradio
+
+# Load the models
+demo = gr.load(name="deepseek-ai/DeepSeek-R1-Distill-Qwen-32B", src=transformers_gradio.registry)
+demo = gr.load(name="deepseek-ai/DeepSeek-R1", src=transformers_gradio.registry)
+demo = gr.load(name="deepseek-ai/DeepSeek-R1-Zero", src=transformers_gradio.registry)
+
+# Disable API names for all functions
+for fn in demo.fns.values():
+    fn.api_name = False
+
+# Define the optional parameters section
+def create_optional_parameters():
+    with gr.Accordion("Optional Parameters (Click to Expand)", open=False):
+        system_message = gr.Textbox(
+            label="System Message", 
+            value="You are a friendly Chatbot created by balione.com",
+            lines=2
+        )
+        max_new_tokens = gr.Slider(minimum=1, maximum=4000, value=200, label="Max New Tokens")
+        temperature = gr.Slider(minimum=0.10, maximum=4.00, value=0.70, label="Temperature")
+        top_p = gr.Slider(minimum=0.10, maximum=1.00, value=0.90, label="Top-p (nucleus sampling)")
+    return system_message, max_new_tokens, temperature, top_p
+
+# Define the main interface
+def chat_interface(user_input, system_message, max_new_tokens, temperature, top_p):
+    # Placeholder response - integrate with actual model here
+    response = f"""**System Message**: {system_message}
+**Your Input**: {user_input}
+**Parameters Used**:
+- Max New Tokens: {max_new_tokens}
+- Temperature: {temperature}
+- Top-p: {top_p}
+
+*Note: Actual model integration required for real responses*"""
+    return response
+
+# Create the Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# DeepSeek Chatbot\nCreated by [ruslanmv.com](https://ruslanmv.com/)")
+    
+    with gr.Row():
+        with gr.Column():
+            user_input = gr.Textbox(label="Your Message", placeholder="Type your message here...", lines=3)
+            submit_button = gr.Button("Submit", variant="primary")
+        with gr.Column():
+            output = gr.Markdown(label="Chatbot Response")
+    
+    # Add the optional parameters section
+    system_message, max_new_tokens, temperature, top_p = create_optional_parameters()
+    
+    # Link the submit button to the chat interface
+    submit_button.click(
+        chat_interface,
+        inputs=[user_input, system_message, max_new_tokens, temperature, top_p],
+        outputs=output
+    )
+
+# Launch the demo
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()