Update app.py

app.py

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+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load Hugging Face model and tokenizer
+model_name = "abrotech/Zora-ALM-7.2B-gguf"  # Your Hugging Face model space
+model = AutoModelForCausalLM.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Define function to handle user input and generate response
+def generate_response(user_input):
+    inputs = tokenizer(user_input, return_tensors="pt")
+    outputs = model.generate(input_ids=inputs["input_ids"], max_length=150, temperature=0.7)
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response
+
+# Set up the Gradio interface
+with gr.Blocks() as demo:
+    gr.HTML("<h1 style='text-align: center;'>Welcome to Zora Assistant</h1>")
+    gr.HTML("<p style='text-align: center;'>Ask anything and Zora will answer!</p>")
+
+    with gr.Row():
+        with gr.Column():
+            user_input = gr.Textbox(label="Enter your question", placeholder="Ask Zora anything...")
+            submit_btn = gr.Button("Get Answer")
+            response_output = gr.Textbox(label="Zora's Answer", interactive=False)
+
+    submit_btn.click(generate_response, inputs=user_input, outputs=response_output)
+
+# Launch the Gradio app
+demo.launch(share=True)