Create prod-desc-app.py

prod-desc-app.py

@@ -0,0 +1,43 @@
+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-2-7b-hf')
+
+# Load the model
+llama_generator = load_llama_model()
+
+# Streamlit app layout
+st.title('Personalized Product Description Writer')
+
+# Input fields for the product details
+st.subheader("Enter Product Details:")
+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")