Update app.py

app.py

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+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load the model and tokenizer
+model_name = "facebook/chameleon-7b"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Function to generate response
+def generate_response(input_text):
+    inputs = tokenizer(input_text, return_tensors="pt")
+    outputs = model.generate(inputs.input_ids, max_length=500, num_return_sequences=1)
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response
+
+# Custom CSS
+css = """
+body {
+    background-color: #2e3440;
+    color: #d8dee9;
+    font-family: 'Roboto', sans-serif;
+}
+.gradio-container {
+    background-color: #3b4252;
+    border: 2px solid #88c0d0;
+    border-radius: 10px;
+    padding: 20px;
+}
+.gr-button {
+    background-color: #5e81ac;
+    color: #d8dee9;
+    border-radius: 5px;
+}
+"""
+
+# Define the Gradio interface
+with gr.Blocks(css=css) as demo:
+    gr.Markdown("# Chat with Chameleon-7B", elem_id="header")
+    with gr.Row():
+        with gr.Column():
+            input_text = gr.Textbox(label="Your Input", placeholder="Type your message here...", lines=4)
+            output_text = gr.Textbox(label="Chameleon-7B Response", placeholder="The model's response will appear here...", lines=4)
+            submit_button = gr.Button("Submit")
+        with gr.Column():
+            gr.Markdown("### Instructions")
+            gr.Markdown("Enter your message in the input box and press submit to get a response from Chameleon-7B.")
+    submit_button.click(fn=generate_response, inputs=input_text, outputs=output_text)
+
+# Launch the Gradio app
+demo.launch()