Create app.py

app.py

@@ -0,0 +1,58 @@
+import os
+import gradio as gr
+from huggingface_hub import InferenceClient
+
+# Retrieve the API key from environment variables
+api_key = os.getenv("API_KEY")
+
+# Initialize the InferenceClient with your provider and the API key from the environment variable
+client = InferenceClient(
+    provider="together",
+    api_key=api_key
+)
+
+def chatbot_response(user_input, chat_history):
+    """
+    Sends the user's input to the inference client and appends the response to the conversation history.
+    """
+    messages = [{"role": "user", "content": user_input}]
+    
+    # Get the response from the Hugging Face model
+    completion = client.chat.completions.create(
+        model="deepseek-ai/DeepSeek-R1", 
+        messages=messages, 
+        max_tokens=500,
+    )
+    
+    # Extract the model's response
+    bot_message = completion.choices[0].message
+    chat_history.append((user_input, bot_message))
+    
+    # Return an empty string to clear the input textbox and the updated chat history
+    return "", chat_history
+
+# Create the Gradio Blocks interface
+with gr.Blocks() as demo:
+    gr.Markdown("# DeepSeek-R1")
+    
+    chatbot = gr.Chatbot()
+    
+    state = gr.State([])
+    
+    with gr.Row():
+        txt = gr.Textbox(placeholder="Type your message here...", show_label=False)
+        send_btn = gr.Button("Send")
+    
+    txt.submit(
+        chatbot_response, 
+        inputs=[txt, state], 
+        outputs=[txt, chatbot]
+    )
+    send_btn.click(
+        chatbot_response,
+        inputs=[txt, state], 
+        outputs=[txt, chatbot]
+    )
+
+# Launch the interface
+demo.launch()