Create app.py

app.py

@@ -0,0 +1,37 @@
+import gradio as gr
+from transformers import AutoTokenizer, FastLanguageModel
+
+# Load the model and tokenizer
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name="lora_model",  # Replace with your trained model name
+    max_seq_length=512,
+    dtype="float16",
+    load_in_4bit=True,
+)
+FastLanguageModel.for_inference(model)
+
+# Define the inference function
+def generate_response(user_input):
+    # Prepare the input for the model
+    labeled_prompt = (
+        "Please provide the response with the following labels:\n"
+        f"User Input: {user_input}\n"
+        "Response:"
+    )
+
+    inputs = tokenizer(
+        [labeled_prompt],
+        return_tensors="pt",
+        padding=True,
+        truncation=True,
+        max_length=512,
+    ).to("cuda")
+
+    response = model.generate(input_ids=inputs.input_ids, attention_mask=inputs.attention_mask, max_new_tokens=100, pad_token_id=tokenizer.eos_token_id)
+    return tokenizer.decode(response[0], skip_special_tokens=True)
+
+# Create a Gradio interface
+iface = gr.Interface(fn=generate_response, inputs="text", outputs="text", title="Chatbot Interface", description="Enter your message below:")
+
+# Launch the app
+iface.launch()