update

app.py

@@ -0,0 +1,61 @@
+import os
+import gradio as gr
+from huggingface_hub import InferenceClient
+from dotenv import load_dotenv
+
+# Load environment variables
+load_dotenv()
+
+# Initialize Hugging Face client
+client = InferenceClient(
+    provider="novita",
+    api_key=os.getenv("HF_TOKEN")
+)
+
+def chat(message, history):
+    """
+    Process chat messages using Hugging Face's Inference Provider
+    """
+    try:
+        # Format the conversation history
+        messages = []
+        for human, assistant in history:
+            messages.append({"role": "user", "content": human})
+            messages.append({"role": "assistant", "content": assistant})
+        messages.append({"role": "user", "content": message})
+
+        # Get response from the model
+        completion = client.chat.completions.create(
+            model="deepseek-ai/DeepSeek-V3-0324",
+            messages=messages,
+            temperature=0.7,
+            max_tokens=1000
+        )
+
+        return completion.choices[0].message.content
+
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+# Create Gradio interface
+with gr.Blocks(title="DeepSearch - AI Search Assistant") as demo:
+    gr.Markdown("# DeepSearch")
+    gr.Markdown("Ask anything and get AI-powered responses using state-of-the-art language models.")
+    
+    chatbot = gr.ChatInterface(
+        fn=chat,
+        examples=[
+            "What is the capital of France?",
+            "Explain quantum computing in simple terms",
+            "Write a short poem about artificial intelligence"
+        ],
+        title="DeepSearch Chat",
+        description="Ask me anything!",
+        theme=gr.themes.Soft(),
+        retry_btn=None,
+        undo_btn=None,
+        clear_btn="Clear",
+    )
+
+if __name__ == "__main__":
+    demo.launch(share=True) 

requirements.txt

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+gradio>=4.0.0
+huggingface-hub>=0.20.0
+python-dotenv>=1.0.0
+requests>=2.31.0