Create app.py

app.py

@@ -0,0 +1,57 @@
+import gradio as gr
+import torch
+from unsloth import FastLanguageModel
+
+# --- Load Model ---
+max_seq_length = 4096
+dtype = torch.float16
+load_in_4bit = True
+
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name = "Ahmed-El-Sharkawy/Meta-Llama-3.1-8B-alpaca",  # directly load your uploaded model
+    max_seq_length = max_seq_length,
+    dtype = dtype,
+    load_in_4bit = load_in_4bit,
+)
+
+FastLanguageModel.for_inference(model)  # Enable 2x faster inference
+
+# Define Alpaca prompt
+alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
+### Instruction:
+{instruction}
+### Input:
+{input_text}
+### Response:
+"""
+
+def generate_response(instruction, input_text):
+    prompt = alpaca_prompt.format(instruction=instruction, input_text=input_text)
+    inputs = tokenizer([prompt], return_tensors="pt").to(model.device)
+
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=512,
+        use_cache=True
+    )
+
+    decoded_output = tokenizer.batch_decode(outputs, skip_special_tokens=True)
+    response = decoded_output[0].replace("<|begin_of_text|>", "").replace("<|end_of_text|>", "").strip()
+
+    # Optional: Remove the prompt part if model echoes it back
+    if prompt.strip() in response:
+        response = response.replace(prompt.strip(), "").strip()
+
+    return response
+
+# --- Gradio UI ---
+with gr.Blocks() as demo:
+    gr.Markdown("# 🌟 LLaMA-3 Alpaca Fine-tuned Chatbot")
+    with gr.Row():
+        instruction = gr.Textbox(label="Instruction", lines=2)
+        input_text = gr.Textbox(label="Input", lines=5)
+    output = gr.Textbox(label="Response", lines=10)
+    btn = gr.Button("Generate")
+    btn.click(generate_response, [instruction, input_text], output)
+
+demo.launch()