Update app.py

app.py

@@ -1,3 +1,51 @@
 import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
 
-gr.load("models/meta-llama/Llama-3.2-3B").launch()
+# Load the model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-3B")
+model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-3B")
+
+def predict(input_text):
+    """Generate a response using the Llama model."""
+    inputs = tokenizer.encode(input_text, return_tensors="pt")
+    outputs = model.generate(inputs, max_length=150, num_return_sequences=1)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# Metadata
+title = "Llama 3.2-3B Model Demonstration"
+description = """
+## How to Use
+1. Enter a prompt in the input box.
+2. Click 'Submit' to get the model's response.
+3. Explore the examples provided for inspiration.
+
+## Model Details
+- **Name:** Llama 3.2-3B
+- **Capabilities:** Text generation, summarization, translation, etc.
+
+This Space demonstrates the capabilities of Meta's Llama 3.2-3B model.
+"""
+examples = [
+    ["Generate a summary for: Artificial intelligence is the simulation of human intelligence..."],
+    ["Translate the text: Hello, how are you? into French."],
+    ["What are the benefits of renewable energy sources?"],
+    ["Write a poem about the ocean."],
+]
+
+# Interface
+def create_interface():
+    """Create a Gradio interface for the Llama model."""
+    return gr.Interface(
+        fn=predict,
+        inputs=gr.Textbox(lines=5, placeholder="Type your input here...", label="Enter your prompt"),
+        outputs=gr.Textbox(label="Model Output"),
+        title=title,
+        description=description,
+        examples=examples,
+        theme="compact"
+    )
+
+# Launch the interface
+interface = create_interface()
+interface.launch(share=True, server_name="0.0.0.0")