Create app.py

app.py

@@ -0,0 +1,37 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+import torch
+
+# Base model (4-bit LLaMA)
+base_model_id = "unsloth/Llama-3.2-3B-Instruct-bnb-4bit"
+
+# Your LoRA adapter repo
+adapter_id = "Christi049/meal-gen-adapter"
+
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained(base_model_id)
+
+# Load base model
+base_model = AutoModelForCausalLM.from_pretrained(
+    base_model_id,
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+
+# Apply LoRA adapter
+model = PeftModel.from_pretrained(base_model, adapter_id)
+
+def generate_meal(prompt):
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    outputs = model.generate(**inputs, max_new_tokens=300, temperature=0.7)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+iface = gr.Interface(
+    fn=generate_meal,
+    inputs=gr.Textbox(label="Meal Request", placeholder="e.g., Generate a 7-day vegetarian meal plan"),
+    outputs=gr.Textbox(label="Generated Meal Plan"),
+    title="Weekly Meal Generator"
+)
+
+iface.launch()