Update app.py

app.py

@@ -1,7 +1,50 @@
 import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from datasets import load_dataset
 
-gr.load("models/meta-llama/Llama-3.2-1B").launch()
+# Load LLaMA model and tokenizer from Hugging Face
+model_name = "meta-llama/Llama-3.2-1B"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
 
-from datasets import load_dataset
+# Load financial dataset to enrich responses
+dataset = load_dataset("gbharti/finance-alpaca")
+
+# Helper function to extract dataset info (optional enhancement)
+def get_insight_from_dataset():
+    sample = dataset["train"].shuffle(seed=42).select([0])[0]
+    return f"Example insight: {sample['text']}"
+
+# Function to process user input and generate financial advice
+def financial_advisor(user_input):
+    # Tokenize the user input
+    inputs = tokenizer(user_input, return_tensors="pt")
+    
+    # Generate response using the LLaMA model
+    outputs = model.generate(**inputs, max_length=256, num_return_sequences=1)
+    advice = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    
+    # Get additional insight from dataset to enrich advice (optional)
+    insight = get_insight_from_dataset()
+
+    # Combine the advice and the insight
+    full_response = f"Advice: {advice}\n\n{insight}"
+    return full_response
+
+# Create Gradio Interface
+interface = gr.Interface(
+    fn=financial_advisor,
+    inputs=gr.Textbox(lines=5, placeholder="Enter your financial question..."),
+    outputs="text",
+    title="AI Financial Advisor",
+    description="Ask me anything related to finance, investments, savings, and more.",
+    examples=[
+        "Should I invest in stocks or real estate?",
+        "How can I save more money on a tight budget?",
+        "What are some good investment options for retirement?",
+    ]
+)
 
-ds = load_dataset("gbharti/finance-alpaca")
+# Launch the Gradio app
+if __name__ == "__main__":
+    interface.launch()