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| import streamlit as st | |
| from ibm_watsonx_ai import APIClient | |
| from ibm_watsonx_ai import Credentials | |
| from ibm_watsonx_ai.foundation_models import ModelInference | |
| from ibm_watsonx_ai.foundation_models.utils.enums import ModelTypes, DecodingMethods | |
| from ibm_watsonx_ai.metanames import GenTextParamsMetaNames as GenParams | |
| import os | |
| # Set up page configuration | |
| st.set_page_config(page_title="ProductProse - AI Product Description Generator", layout="wide") | |
| # Initialize session state to track API responses and user feedback | |
| 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 | |
| if 'feedback' not in st.session_state: | |
| st.session_state.feedback = None | |
| # Sidebar for product data input | |
| st.sidebar.title("Product Data Input") | |
| product_name = st.sidebar.text_input("Product Name", "Example Product") | |
| 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") | |
| # Select target language for translation | |
| target_language = st.sidebar.selectbox("Target Language for Translation", ["Arabic", "Chinese", "French", "German", "Japanese", "Portugese", "Russian", "Spanish", "Urdu"]) | |
| # Main app title and description | |
| st.title("ProductProse - AI Product Description Generator") | |
| st.markdown(""" | |
| 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. | |
| """) | |
| # IBM WatsonX API Setup | |
| project_id = os.getenv('WATSONX_PROJECT_ID') | |
| api_key = os.getenv('WATSONX_API_KEY') | |
| if api_key and project_id: | |
| credentials = Credentials(url="https://us-south.ml.cloud.ibm.com", api_key=api_key) | |
| client = APIClient(credentials) | |
| client.set.default_project(project_id) | |
| # Tone Selection for Description Customization | |
| tone_example = st.sidebar.selectbox("Select Example Tone (Feel free to modify)", ["Formal", "Casual", "Professional", "Playful"]) | |
| st.sidebar.markdown("_Example: You can choose a tone that best fits your brand's style._") | |
| # Keyword Input for SEO Optimization | |
| seo_keywords_example = st.sidebar.text_area("SEO Keywords (comma-separated, e.g., 'smart home, automation')", "smart home, intelligent, automation") | |
| st.sidebar.markdown("_Example: Add keywords to optimize for search engines._") | |
| # Step 1: 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 professional and engaging product description:\n | |
| Product Name: {product_name}\n | |
| Features: {features}\n | |
| Benefits: {benefits}\n | |
| Specifications: {specifications}\n | |
| Generate only the final product description text, without including any instruction or prompt context. | |
| """ | |
| try: | |
| 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.session_state.translated_description = None # Clear previous translations | |
| st.success("Product description generated!") | |
| st.write(description_response) | |
| except Exception as 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.") | |
| # Step 2: Translate Product Description | |
| st.header("Step 2: Translate Product Description") | |
| if st.session_state.generated_description: | |
| if 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}") | |
| # Display previous results | |
| if st.session_state.generated_description: | |
| st.subheader("Generated Product Description") | |
| st.write(st.session_state.generated_description) | |
| if st.session_state.translated_description: | |
| st.subheader(f"Translated Product Description ({target_language})") | |
| st.write(st.session_state.translated_description) | |
| # Step 3: Customize Product Description via Chat Interface | |
| st.header("Step 3: Customize Product Description") | |
| customization_prompt = st.text_input("Customize the product description (Feel free to modify the example tone and SEO keywords)") | |
| if st.session_state.generated_description and customization_prompt: | |
| if st.button("Customize Description"): | |
| try: | |
| # Customize the description using Granite-13B-Chat | |
| prompt = f"Customize the following product description with a {tone_example} tone, using the following SEO keywords: {seo_keywords_example}.\nProduct Description:\n{st.session_state.generated_description}\nCustomization Request: {customization_prompt}\nGenerate only the final customized product description." | |
| 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}") | |
| # Display customized result if available | |
| if st.session_state.customized_description: | |
| st.subheader("Customized Product Description") | |
| st.write(st.session_state.customized_description) | |
| # Step 4: Feedback and Quality Scoring | |
| st.header("Step 4: Provide Feedback") | |
| feedback = st.slider("Rate the quality of the generated product description (1 = Poor, 5 = Excellent)", 1, 5, 3) | |
| feedback_comments = st.text_area("Additional Comments") | |
| if st.button("Submit Feedback"): | |
| st.session_state.feedback = {"rating": feedback, "comments": feedback_comments} | |
| st.success("Thank you for your feedback!") | |
| st.write(st.session_state.feedback) | |
| else: | |
| st.error("IBM WatsonX API credentials are not set. Please check your environment variables.") |