Create app.py

app.py

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+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+
+# Load DeepSeek model
+deepseek_tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-R1")
+deepseek_model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-R1", torch_dtype="auto", device_map="auto")
+deepseek_pipe = pipeline("text-generation", model=deepseek_model, tokenizer=deepseek_tokenizer)
+
+# Load LLaMA model
+llama_tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-4-Scout-17B-16E-Instruct")
+llama_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-4-Scout-17B-16E-Instruct", torch_dtype="auto", device_map="auto")
+llama_pipe = pipeline("text-generation", model=llama_model, tokenizer=llama_tokenizer)
+
+def generate_and_enhance_code(code_request: str, features: str):
+    # Step 1: Generate base code from DeepSeek
+    base_output = deepseek_pipe(code_request, max_new_tokens=512, do_sample=True, temperature=0.7)[0]["generated_text"]
+
+    # Step 2: Ask Llama to add features
+    enhancement_prompt = f"Hey Llama! can you please add some more features in my code?\n\nOriginal code:\n{base_output}\n\nFeatures to add:\n{features}\n\nAdd the features and pass me the code without any extra asking!"
+    enhanced_output = llama_pipe(enhancement_prompt, max_new_tokens=1024, do_sample=True, temperature=0.6)[0]["generated_text"]
+
+    return enhanced_output
+
+with gr.Blocks() as demo:
+    gr.Markdown("## AI Code Assistant with DeepSeek + LLaMA 4")
+    with gr.Row():
+        code_input = gr.Textbox(lines=5, label="What code do you want?")
+        feature_input = gr.Textbox(lines=3, label="What features should LLaMA add?")
+    submit_btn = gr.Button("Generate & Enhance Code")
+    output_box = gr.Code(label="Enhanced Code")
+
+    submit_btn.click(fn=generate_and_enhance_code, inputs=[code_input, feature_input], outputs=output_box)
+
+demo.launch()