Create app.py

app.py

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+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# Load the model and tokenizer from Hugging Face
+model_name = "Rehman1603/airline_guidenece"  # Replace with your Hugging Face model name
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
+
+# Prepare the model for inference
+model.eval()
+
+# Define the Alpaca prompt format
+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.
+
+### Response:
+{}"""
+
+def chat_with_model(instruction):
+    # Format the input with the Alpaca prompt
+    formatted_input = alpaca_prompt.format(instruction)
+    
+    # Tokenize the input
+    inputs = tokenizer(
+        formatted_input,
+        return_tensors="pt",
+    ).to("cuda")
+    
+    # Generate the response
+    outputs = model.generate(**inputs, max_new_tokens=64, use_cache=True)
+    decoded_output = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
+    
+    # Extract the response part after "### Response:"
+    response_start = decoded_output.find("### Response:") + len("### Response:")
+    response_text = decoded_output[response_start:].strip()
+    
+    return response_text
+
+# Create a Gradio interface
+interface = gr.Interface(
+    fn=chat_with_model,
+    inputs=gr.Textbox(lines=2, placeholder="Enter your instruction here..."),
+    outputs="text",
+    title="Airline Guidance Chatbot",
+    description="Ask questions about airline guidance and get responses from the model.",
+)
+
+# Launch the Gradio app
+interface.launch()