Update requirements.txt

requirements.txt

@@ -1,65 +1,5 @@
-import gradio as gr
-from fastapi import FastAPI
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-# --- Model Setup ---
-model_path = "WhiteRabbitNeo/WhiteRabbitNeo-2.5-Qwen-2.5-Coder-7B"
-
-model = AutoModelForCausalLM.from_pretrained(
-    model_path,
-    torch_dtype=torch.float16,
-    device_map="auto",          # requires accelerate
-    trust_remote_code=True
-)
-
-tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
-
-# --- Backend function ---
-def generate_code(user_prompt, temperature=0.75, top_p=1.0, max_tokens=2048, top_k=50):
-    tokens = tokenizer.encode(user_prompt, return_tensors="pt").to("cuda" if torch.cuda.is_available() else "cpu")
-    with torch.no_grad():
-        output = model.generate(
-            input_ids=tokens,
-            max_length=tokens.shape[1] + max_tokens,
-            do_sample=True,
-            temperature=temperature,
-            top_p=top_p,
-            top_k=top_k,
-            num_return_sequences=1,
-            pad_token_id=tokenizer.eos_token_id,
-        )
-    generated_tokens = output[0][tokens.shape[1]:]
-    code_string = tokenizer.decode(generated_tokens, skip_special_tokens=True)
-    return f"```python\n{code_string}\n```"
-
-# --- FastAPI backend ---
-api = FastAPI()
-
-@api.get("/ping")
-async def ping():
-    return {"status": "pong"}
-
-# --- Gradio UI ---
-with gr.Blocks(title="Spec Kit Copilot") as demo:
-    with gr.Tab("AI Code Generation"):
-        gr.Markdown("## WhiteRabbitNeo AI Code Generator")
-        user_input = gr.Textbox(label="Describe code to generate", lines=4, placeholder="E.g., Python function to sort a list")
-        temperature = gr.Slider(0.0, 1.0, 0.75, label="Temperature")
-        top_p = gr.Slider(0.0, 1.0, 1.0, label="Top-p")
-        max_tokens = gr.Slider(256, 4096, 2048, step=128, label="Max Tokens")
-        top_k = gr.Slider(0, 100, 50, label="Top-k")
-        generate_btn = gr.Button("Generate Code")
-        preview = gr.Markdown()
-        
-        generate_btn.click(
-            fn=generate_code,
-            inputs=[user_input, temperature, top_p, max_tokens, top_k],
-            outputs=preview
-        )
-
-# --- Mount FastAPI inside Gradio (optional) ---
-demo = gr.mount_app(demo, api, path="/api")
-
-if __name__ == "__main__":
-    demo.launch(server_name="0.0.0.0", server_port=7860)
+torch
+transformers>=4.44.0
+accelerate
+bitsandbytes
+gradio>=4.38.0