Update app.py

app.py

@@ -25,7 +25,7 @@ benefits = st.sidebar.text_area("Product Benefits", "Benefit 1, Benefit 2, Benef
 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", "Urdu", "Russian", "French", "Spanish", "German", "Chinese", "Japanese"])
+target_language = st.sidebar.selectbox("Target Language for Translation", ["French", "Spanish", "German", "Chinese", "Japanese"])
 
 # Main app title and description
 st.title("ProductProse - AI Product Description Generator")
@@ -49,11 +49,12 @@ if api_key and project_id:
         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
+            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={
@@ -75,47 +76,63 @@ if api_key and project_id:
 
     # 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}")
+    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)
 
     # 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}")
+    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 based on the user's request:\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)
 
 else:
     st.error("IBM WatsonX API credentials are not set. Please check your environment variables.")