Create app.py

app.py

@@ -0,0 +1,47 @@
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+import gradio as gr
+
+model_name = "codellama/CodeLlama-7b-Instruct-hf"
+print("Loading model...")
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.float16,
+    device_map="auto")
+print("Model loaded.")
+
+generator = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    temperature=0.1,
+    top_p=0.95,
+    max_new_tokens=512,
+    repetition_penalty=1.05)
+
+def format_prompt(chat):
+    prompt = ""
+    for user_msg, ai_reply in chat:
+        prompt += f"<s>[INST] {user_msg.strip()} [/INST] {ai_reply.strip()}</s>\n"
+    return prompt
+
+def chat_fn(user_input, history):
+    history = history or []
+    prompt = format_prompt(history + [[user_input, ""]])
+    generated = generator(prompt, do_sample=True)[0]["generated_text"]
+    answer = generated[len(prompt):].strip()
+    history.append((user_input, answer))
+    return "", history
+
+with gr.Blocks() as demo:
+    gr.Markdown("# 🦙 CodeLlama Copilot\nFree & private code assistant.")
+    chatbot = gr.Chatbot(label="Developer Assistant").style(height=400)
+    with gr.Row():
+        msg = gr.Textbox(placeholder="Ask me coding questions", show_label=False).style(container=False)
+        clear = gr.Button("🔄 Clear Conversation")
+    msg.submit(chat_fn, [msg, chatbot], [msg, chatbot])
+    clear.click(lambda: ("", []), None, [msg, chatbot])
+
+if __name__ == "__main__":
+    demo.launch()