Update app.py

app.py

@@ -1,5 +1,4 @@
 import streamlit as st
-from PIL import Image
 from ibm_watsonx_ai import APIClient
 from ibm_watsonx_ai import Credentials
 from ibm_watsonx_ai.foundation_models import ModelInference
@@ -7,29 +6,34 @@ from ibm_watsonx_ai.foundation_models.utils.enums import ModelTypes, DecodingMet
 from ibm_watsonx_ai.metanames import GenTextParamsMetaNames as GenParams
 import os
 
-# Setting up the page layout
-st.set_page_config(page_title="AI Product Design & Development", layout="wide")
+# Set up page configuration
+st.set_page_config(page_title="ProductProse - AI Product Description Generator", layout="wide")
 
-# Sidebar - User inputs for Product Specifications
-st.sidebar.title("Product Specifications")
+# Initialize session state to track API responses and query count
+if 'generated_description' not in st.session_state:
+    st.session_state.generated_description = None
+if 'translated_description' not in st.session_state:
+    st.session_state.translated_description = None
+if 'customized_description' not in st.session_state:
+    st.session_state.customized_description = None
+
+# Sidebar for product data input
+st.sidebar.title("Product Data Input")
 product_name = st.sidebar.text_input("Product Name", "Example Product")
-material = st.sidebar.selectbox("Material", ["Plastic", "Metal", "Wood", "Composite"])
-dimensions = st.sidebar.text_input("Dimensions (L x W x H in cm)", "10 x 5 x 3")
-constraints = st.sidebar.text_area("Design Constraints", "E.g., Must be lightweight, eco-friendly")
-budget = st.sidebar.number_input("Budget ($)", min_value=0, value=1000)
+features = st.sidebar.text_area("Product Features", "Feature 1, Feature 2, Feature 3")
+benefits = st.sidebar.text_area("Product Benefits", "Benefit 1, Benefit 2, Benefit 3")
+specifications = st.sidebar.text_area("Product Specifications", "Specification 1, Specification 2, Specification 3")
 
-st.sidebar.subheader("Project Info")
-st.sidebar.text("AI-Powered Product Design")
+# Select target language for translation
+target_language = st.sidebar.selectbox("Target Language for Translation", ["French", "Spanish", "German", "Chinese", "Japanese"])
 
 # Main app title and description
-st.title("InnoDesign: an AI Product Design & Development Tool")
+st.title("ProductProse - AI Product Description Generator")
 st.markdown("""
-Welcome to 'InnoDesign', the AI-powered product design and development tool. This app leverages generative AI to accelerate the design process, optimize products for manufacturing, and simulate product performance.
+Welcome to ProductProse, an AI-powered tool for generating and customizing product descriptions using IBM Granite LLMs.
+Simply input your product data and let the AI do the rest, including generating descriptions, translating them into multiple languages, and customizing them to match your brand tone and style.
 """)
 
-# Tabs for different sections of the app
-tabs = st.tabs(["Design Generation", "Simulation", "Optimization"])
-
 # IBM WatsonX API Setup
 project_id = os.getenv('WATSONX_PROJECT_ID')
 api_key = os.getenv('WATSONX_API_KEY')
@@ -39,35 +43,79 @@ if api_key and project_id:
     client = APIClient(credentials)
     client.set.default_project(project_id)
 
-    parameters = {
-        GenParams.DECODING_METHOD: DecodingMethods.GREEDY,
-        GenParams.MIN_NEW_TOKENS: 50,
-        GenParams.MAX_NEW_TOKENS: 200,
-        GenParams.STOP_SEQUENCES: ["\n"]
-    }
-
-    model_id = ModelTypes.GRANITE_13B_CHAT_V2
-    model = ModelInference(model_id=model_id, params=parameters, credentials=credentials, project_id=project_id)
-
-    # Design Generation Tab
-    with tabs[0]:
-        st.header("Generate Product Designs")
-        st.write("Input your product specifications in the sidebar and click below to generate design concepts.")
-        
-        if st.button("Generate Design Concepts"):
-            prompt = f"""You are an AI specialized in product design. Generate creative product design concepts based on the following details:\n
+    # Generate Product Description
+    st.header("Step 1: Generate Product Description")
+    if st.button("Generate Description"):
+        if product_name and features and benefits and specifications:
+            # Prompt engineering for Granite-13B-Instruct
+            prompt = f"""
+            You are an AI that generates high-quality product descriptions. Based on the following details, generate a detailed product description:\n
             Product Name: {product_name}\n
-            Material: {material}\n
-            Dimensions: {dimensions}\n
-            Constraints: {constraints}\n
-            Budget: {budget} USD\n
-            Provide detailed design concepts and explain their features."""
-            
+            Features: {features}\n
+            Benefits: {benefits}\n
+            Specifications: {specifications}\n
+            """
             try:
-                response = model.generate_text(prompt=prompt, params=parameters)
-                st.success("Generated Design Concepts:")
-                st.write(response)
+                model = ModelInference(model_id=ModelTypes.GRANITE_13B_INSTRUCT_V2, params={
+                    GenParams.DECODING_METHOD: DecodingMethods.GREEDY,
+                    GenParams.MIN_NEW_TOKENS: 50,
+                    GenParams.MAX_NEW_TOKENS: 200,
+                    GenParams.STOP_SEQUENCES: ["\n"]
+                }, credentials=credentials, project_id=project_id)
+
+                with st.spinner("Generating product description..."):
+                    description_response = model.generate_text(prompt=prompt)
+                    st.session_state.generated_description = description_response
+                    st.success("Product description generated!")
+                    st.write(description_response)
             except Exception as e:
-                st.error(f"An error occurred: {e}")
+                st.error(f"An error occurred while generating the description: {e}")
+        else:
+            st.warning("Please fill in all the product data fields before generating a description.")
+
+    # Translate Product Description
+    st.header("Step 2: Translate Product Description")
+    if st.session_state.generated_description and st.button("Translate Description"):
+        try:
+            # Translate the description using Granite-20B-Multilingual
+            prompt = f"Translate the following product description into {target_language}:\n{st.session_state.generated_description}"
+            model = ModelInference(model_id=ModelTypes.GRANITE_20B_MULTILINGUAL, params={
+                GenParams.DECODING_METHOD: DecodingMethods.GREEDY,
+                GenParams.MIN_NEW_TOKENS: 50,
+                GenParams.MAX_NEW_TOKENS: 200,
+                GenParams.STOP_SEQUENCES: ["\n"]
+            }, credentials=credentials, project_id=project_id)
+
+            with st.spinner(f"Translating product description to {target_language}..."):
+                translation_response = model.generate_text(prompt=prompt)
+                st.session_state.translated_description = translation_response
+                st.success(f"Product description translated to {target_language}!")
+                st.write(translation_response)
+        except Exception as e:
+            st.error(f"An error occurred while translating the description: {e}")
+
+    # Customize Product Description via Chat Interface
+    st.header("Step 3: Customize Product Description")
+    customization_prompt = st.text_input("Customize the product description (e.g., adjust tone, add brand-specific details)")
+
+    if st.session_state.generated_description and customization_prompt and st.button("Customize Description"):
+        try:
+            # Customize the description using Granite-13B-Chat
+            prompt = f"Customize the following product description based on the user's request:\n{st.session_state.generated_description}\nUser Request: {customization_prompt}"
+            model = ModelInference(model_id=ModelTypes.GRANITE_13B_CHAT_V2, params={
+                GenParams.DECODING_METHOD: DecodingMethods.GREEDY,
+                GenParams.MIN_NEW_TOKENS: 50,
+                GenParams.MAX_NEW_TOKENS: 200,
+                GenParams.STOP_SEQUENCES: ["\n"]
+            }, credentials=credentials, project_id=project_id)
+
+            with st.spinner("Customizing product description..."):
+                customization_response = model.generate_text(prompt=prompt)
+                st.session_state.customized_description = customization_response
+                st.success("Product description customized!")
+                st.write(customization_response)
+        except Exception as e:
+            st.error(f"An error occurred while customizing the description: {e}")
 
-# Simulation and Optimization tabs will be expanded in future steps.
+else:
+    st.error("IBM WatsonX API credentials are not set. Please check your environment variables.")