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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, user feedback, and history | |
| 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_history' not in st.session_state: | |
| st.session_state.feedback_history = [] | |
| # Sidebar for product data input | |
| with st.sidebar: | |
| st.title("Product Data Input") | |
| product_name = st.text_input("Product Name", placeholder="e.g., Smart Home Hub") | |
| features = st.text_area("Product Features", placeholder="e.g., Voice Control, Energy Efficient, Compact Design") | |
| benefits = st.text_area("Product Benefits", placeholder="e.g., Saves time, Reduces energy usage, Easy to install") | |
| specifications = st.text_area("Product Specifications", placeholder="e.g., Dimensions: 10x5x3 inches, Weight: 1.5 lbs") | |
| # Select target language for translation | |
| target_language = st.selectbox("Target Language for Translation", ["Arabic", "Chinese", "French", "German", "Japanese", "Portugese", "Russian", "Spanish", "Urdu"]) | |
| # Main app title and description | |
| st.markdown("# 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. | |
| """) | |
| # UI Enhancement: Color-coded sections for clarity | |
| def section_header(title, color): | |
| st.markdown(f'<h2 style="color:{color};">{title}</h2>', unsafe_allow_html=True) | |
| # 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("Tone Example (Modify as needed)", ["Formal", "Casual", "Professional", "Playful"]) | |
| st.sidebar.markdown("_Example: Choose a tone to match your brand's style._") | |
| # Keyword Input for SEO Optimization | |
| seo_keywords_example = st.sidebar.text_area("SEO Keywords (comma-separated)", placeholder="e.g., wireless, fast charging, Bluetooth") | |
| st.sidebar.markdown("_Example: Add keywords that enhance search engine optimization._") | |
| # Step 1: Generate Product Description | |
| section_header("Step 1: Generate Product Description", "blue") | |
| 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.session_state.customized_description = None # Clear previous customizations | |
| 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 | |
| section_header("Step 2: Translate Product Description", "green") | |
| 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 | |
| section_header("Step 3: Customize Product Description", "orange") | |
| customization_prompt = st.text_input("Customize the product description", placeholder="e.g., Make the tone more playful and mention our eco-friendly packaging") | |
| 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) | |
| # Option to translate the customized description if it hasn't been translated yet | |
| if st.session_state.translated_description: | |
| if st.button("Translate Customized Description"): | |
| try: | |
| # Translate the customized description using Granite-20B-Multilingual | |
| prompt = f"Translate the following customized product description into {target_language}:\n{st.session_state.customized_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 customized product description to {target_language}..."): | |
| customized_translation_response = model.generate_text(prompt=prompt) | |
| st.session_state.translated_customized_description = customized_translation_response | |
| st.success(f"Customized product description translated to {target_language}!") | |
| st.write(customized_translation_response) | |
| except Exception as e: | |
| st.error(f"An error occurred while translating the customized description: {e}") | |
| # Display the translated customized description if available | |
| if 'translated_customized_description' in st.session_state: | |
| st.subheader(f"Translated Customized Product Description ({target_language})") | |
| st.write(st.session_state.translated_customized_description) | |
| # Step 4: Feedback and Quality Scoring | |
| section_header("Step 4: Provide Feedback", "purple") | |
| feedback_rating = 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"): | |
| # Save the feedback in session state | |
| feedback_entry = { | |
| "rating": feedback_rating, | |
| "comments": feedback_comments, | |
| "description": st.session_state.generated_description, | |
| "customized_description": st.session_state.customized_description if st.session_state.customized_description else "N/A", | |
| "translated_description": st.session_state.translated_description if st.session_state.translated_description else "N/A" | |
| } | |
| st.session_state.feedback_history.append(feedback_entry) | |
| st.success("Thank you for your feedback!") | |
| # Display the feedback summary | |
| st.subheader("Feedback Summary") | |
| for i, feedback in enumerate(st.session_state.feedback_history, 1): | |
| st.write(f"**Feedback {i}:**") | |
| st.write(f"Rating: {feedback['rating']}") | |
| st.write(f"Comments: {feedback['comments']}") | |
| st.write(f"Generated Description: {feedback['description']}") | |
| st.write(f"Customized Description: {feedback['customized_description']}") | |
| st.write(f"Translated Description: {feedback['translated_description']}") | |
| st.markdown("---") | |
| else: | |
| st.error("IBM WatsonX API credentials are not set. Please check your environment variables.") |