Update app.py

app.py

@@ -1,13 +1,5 @@
 import streamlit as st
-from transformers import pipeline
-
-# Set up the LLaMA 3.2 model (using Hugging Face pipeline)
-@st.cache_resource
-def load_llama_model():
-    return pipeline('text-generation', model='meta-llama/Llama-3.2-7b-hf')
-
-# Load the model
-llama_generator = load_llama_model()
+import google.generativeai as genai
 
 # Streamlit app layout
 st.title('Personalized Product Description Writer')
@@ -18,26 +10,6 @@ product_name = st.text_input('Product Name', '')
 product_features = st.text_area('Product Features (comma separated)', '')
 target_audience = st.text_input('Target Audience', '')
 
-# Button to trigger the description generation
-if st.button('Generate Description'):
-    if product_name and product_features and target_audience:
-        # Construct the prompt for the LLaMA model
-        prompt = (f"Write a product description for a product called '{product_name}' targeting {target_audience}. "
-                  f"Features include: {product_features}.")
-        
-        # Generate the description using LLaMA 3.2 model
-        description = llama_generator(prompt, max_length=100, num_return_sequences=1)[0]['generated_text']
-        
-        # Display the generated description
-        st.subheader("Generated Product Description:")
-        st.write(description)
-    else:
-        st.warning("Please fill in all the fields.")
-
-# Additional optional features
-st.sidebar.subheader("Customize Description")
-length = st.sidebar.slider('Max Length', 50, 200, 100)
-
 # Footer
 st.sidebar.markdown("---")
 st.sidebar.markdown("Built with 🧠 by Hruday & Ollama")