Update app.py

app.py

@@ -1,19 +1,44 @@
 import streamlit as st
-from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
 
 # App header
-st.header("Know Your Medicine")
+st.header("Know Your Medicine - Multiplication Table Generator")
 
 # Load the model and tokenizer
 @st.cache_resource
-def load_model():
-    model_name = "meta-llama/Llama-3.2-1B"  # Replace with correct model ID from Hugging Face if needed
+def load_model_direct():
+    model_name = "meta-llama/Llama-3.2-1B"
     tokenizer = AutoTokenizer.from_pretrained(model_name)
     model = AutoModelForCausalLM.from_pretrained(model_name)
     return model, tokenizer
 
-model, tokenizer = load_model()
+# Load the model
+model, tokenizer = load_model_direct()
 
-st.write("LLaMA 3.2 model integrated!")
+# Input for the number to generate the multiplication table
+number = st.number_input("Enter a number:", min_value=1, max_value=100, value=5)
 
-# You can now add future steps for input and interaction later
+# Define the prompt for generating the multiplication table
+prompt = f"Give me the multiplication table of {number} up to 12."
+
+# Generate text based on the input
+if st.button("Generate Multiplication Table"):
+    # Tokenize the input prompt
+    tokenized_input = tokenizer(prompt, return_tensors="pt")
+    input_ids = tokenized_input["input_ids"].cuda()  # If running on GPU
+    attention_mask = tokenized_input["attention_mask"].cuda()  # If running on GPU
+
+    # Generate the response from the model
+    response_token_ids = model.generate(
+        input_ids,
+        attention_mask=attention_mask,
+        max_new_tokens=150,
+        pad_token_id=tokenizer.eos_token_id
+    )
+
+    # Decode the generated tokens to text
+    generated_text = tokenizer.decode(response_token_ids[0], skip_special_tokens=True)
+
+    # Display the generated multiplication table
+    st.write("Generated Multiplication Table:")
+    st.write(generated_text)