Add gemini-2.0-flash-thinking

app.py

@@ -9,8 +9,9 @@ from langchain_openai.chat_models import ChatOpenAI
 from langchain.schema import HumanMessage, SystemMessage, AIMessage
 from langchain_anthropic import ChatAnthropic, ChatAnthropicMessages
 from langchain_google_genai import ChatGoogleGenerativeAI
-from langchain_groq import ChatGroq
+# from langchain_groq import ChatGroq
 import openai
+import google.generativeai as genai
 
 from langchain import hub
 from langchain_chroma import Chroma
@@ -100,18 +101,15 @@ languages = ["American English",
              "Polish",
              "Portuguese"]
 
-models = ["gpt-4-turbo", 
-          "gpt-4o",
-          "gpt-3.5-turbo", 
+models = ["gpt-4o",
           "claude-3-7-sonnet-latest",
-          "claude-3-sonnet-20240229", 
-          "claude-3-opus-20240229", 
           "claude-3-5-sonnet-20240620",
           "claude-3-5-sonnet-20241022",
-          "gemini-1.5-pro"
-          #"llama3-70b-8192",
+          "gemini-2.0-flash-thinking-exp-01-21",
           ]
 
+default_model = "gemini-2.0-flash-thinking-exp-01-21"
+
 openai.api_key = os.environ["OPENAI_API_KEY"]
 
 struct_copy_prompt = """Generate {nversions} versions of the product description for a product with the following information.
@@ -124,8 +122,8 @@ Do not include any part of the reference structure in the output.
 The structure of the output should follow the reference structure.
 Do not use the structure of the reference copy in the output. 
 Do not use any of the excluded words in the output.
-Include all included words in the output.
-Make sure to use product features and intended use in the output.
+Try to inlcude included words in the output when relevant.
+Use the relevant information from the product features and intended use in the output.
 Do not hallucinate any information about the product, use only the provided key features and intended use to write about the product.
 Note that the reference copy should be used for style and tone only, do not use any part of the reference copy in the output. 
 Do not hallucinate information about size and weight. Write about size and weight only if it is available in the list of features.
@@ -165,16 +163,27 @@ Return the result in the following JSON format:
 }}                              
 Make sure that the output is in JSON format, no extra text should be included in the output.                                
                                  
-Product information:
-Key features: {key_features}
+<product_information>
+<key_features>
+{key_features}
+</key_features>
 
-Reference structure: {structure}
+<reference_structure>
+{structure}
+</reference_structure>
 
-Reference copy: {copy}
+<reference_copy>
+{copy}
+</reference_copy>
 
-Included words: {included_phrases}
+<included_phrases>
+{included_phrases}
+</included_phrases>
 
-Excluded words: {excluded_phrases}"""
+<excluded_phrases>
+{excluded_phrases}
+</excluded_phrases>
+</product_information>"""
 
 
 copy_prompt = """Generate {nversions} versions of the product description for a product with the following information.
@@ -185,8 +194,8 @@ Make sure to use the tone of voice, rythm, cadence and style of the reference co
 Use markdown format for each output.
 Make sure that the structure of each output follows the structure of the reference copy.
 Do not use any of the excluded words in the output.
-Include all included words in the output.
-Make sure to use product features and intended use in the output.
+Try to inlcude included words in the output when relevant.
+Use the relevant information from the product features and intended use in the output.
 Do not hallucinate any information about the product, use only the provided key features and intended use to write about the product.
 Note that the reference copy should be used for style and tone only, do not use any part of the reference copy in the output. 
 Do not hallucinate information about size and weight. Write about size and weight only if it is available in the list of features.
@@ -225,14 +234,23 @@ Return the result in the following JSON format:
 }}
 Make sure that the output is in JSON format, no extra text should be included in the output.
 
-Product information:
-Key features: {key_features}
+<product_information>
+<key_features>
+{key_features}
+</key_features>
 
-Reference copy: {copy}
+<reference_copy>
+{copy}
+</reference_copy>
 
-Included words: {included_phrases}
+<included_phrases>
+{included_phrases}
+</included_phrases>
 
-Excluded words: {excluded_phrases}"""
+<excluded_phrases>
+{excluded_phrases}
+</excluded_phrases>
+</product_information>"""
 
 
 struct_prompt = """Generate {nversions} versions of the product description for a product with the following information.
@@ -243,8 +261,8 @@ Use markdown format for each output.
 Do not include any part of the reference structure in the output.
 Make sure that the structure of each output follows the reference structure.
 Do not use any of the excluded words in the output.
-Include all included words in the output.
-Make sure to use product features and intended use in the output.
+Try to inlcude included words in the output when relevant.
+Use the relevant information from the product features and intended use in the output.
 Do not hallucinate any information about the product, use only the provided key features and intended use to write about the product.
 Note that the reference structure should be used for structure only, do not use any part of the reference structure in the output. 
 Do not hallucinate information about size and weight. Write about size and weight only if it is available in the list of features.
@@ -283,14 +301,82 @@ Return the result in the following JSON format:
 }}                              
 Make sure that the output is in JSON format, no extra text should be included in the output.                                
                                  
-Product information:
-Key features: {key_features}
+<product_information>
+<key_features>
+{key_features}
+</key_features>
+
+<reference_structure>
+{structure}
+</reference_structure>
+
+<included_phrases>
+{included_phrases}
+</included_phrases>
+
+<excluded_phrases>
+{excluded_phrases}
+</excluded_phrases>
+</product_information>"""
+
+evaluation_prompt = """You will be given information of a product and a list of product descriptions.
+Evaluate the quality of the product descriptions based on the following criteria:
+    - how faithful it describes the product features. 
+    - how well it follows the reference structure. 
+    - how well it follows the tone of voice, rythm, cadence and style of the reference copy.
+    - how well it avoid the excluded words.                                 
+    - how well it includes the included words.                                 
+    - how creative the language is.    
+Give a score between 0 and 10 for each product description based on the above criteria.
+Return the result in the following JSON format:
+{{
+    "versions": [                                 
+        {{
+            "id": 1,
+            "content": The first product description,
+            "explanation": A less than 20 word explanation of the score of the first product description,                         
+            "score": The score of the first product description
+        }},
+        {{
+            "id": 2,
+            "content": The second product description,
+            "explanation": A less than 20 word explanation of the score of the first product description,                                     
+            "score": The score of the second product description
+        }},
+        ...                                 
+    ],                                      
+    "best_version": {{
+        "explanation": Explanation for why this version is the best,                                                              
+        "id": The id of the best version                                                                
+    }}                                                          
+}}                              
+Make sure that the output is in JSON format, no extra text should be included in the output.  
+
+<product_information>
+<key_features>
+{key_features}
+</key_features>
+
+<reference_structure>
+{structure}
+</reference_structure>
 
-Reference structure: {structure}
+<reference_copy>
+{copy}
+</reference_copy>
 
-Included words: {included_phrases}
+<included_phrases>
+{included_phrases}
+</included_phrases>
 
-Excluded words: {excluded_phrases}"""
+<excluded_phrases>
+{excluded_phrases}
+</excluded_phrases>
+</product_information>
+
+<product_descriptions>
+{product_descriptions}
+</product_descriptions>"""
 
 
 improve_structure_prompt = """You are given a structure for a product description. 
@@ -299,6 +385,36 @@ Return the reformatted structure only. Do not add any preceding or trailing char
 
 <structure>/n{structure}</structure>"""
 
+gemini_prompt = """You are given information of a product, a reference structure, and a reference copy. 
+Please analyze the structure, make a plan on how to follow the structure correctly, and write a product
+ description for the product. Use the tone of voice of the reference copy for the generated description. 
+Write from {min_length} to {max_length} words.
+Do not hallucinate, do not add information that is not in the product information. 
+Try your best to avoid using the excluded words and phrases.
+Try your best to include the included words and phrases.
+Do not enclose the output in html tags, quotes, braces, brackets or anything. 
+Return the product description only.
+
+<product_information>
+{key_features}
+</product_information>
+
+<reference_structure>
+{structure}
+</reference_structure>
+
+<reference_copy>
+{copy}
+</reference_copy>
+
+<excluded_phrases>
+{excluded_phrases}
+</excluded_phrases>
+
+<included_phrases>
+{included_phrases}
+</included_phrases>"""
+
 
 import base64
 import requests
@@ -435,10 +551,11 @@ def get_language(struct_lang, copy_lang):
 
 def post_process(text: str, guidance_prompt: str, language: str, chat: ChatOpenAI):
     messages = [
-        SystemMessage(content=f"""You are a helpful assistant that edit documents based on the guidlines provided. Make sure to write in {language} language."""),
+        SystemMessage(content=f"""You are a helpful assistant that edit documents based on the guidelines provided. 
+Make sure to write in {language} language."""),
         HumanMessage(content=f"""Given the following product description, your task is to
 make minimal modification to the product description such that the resulting description 
-follows the rules defined in the guidlines. Make sure to preserve the structure of the 
+follows the rules defined in the guidelines. Make sure to preserve the structure of the 
 original text as much as possible. Do not modify the structure of the original text.
 Do not change the language of the original text.                     
 Output only the modified text in markdown format.
@@ -451,6 +568,11 @@ Guidelines:
 {guidance_prompt}""")
     ]
 
+    if chat is None:
+        chat = ChatAnthropic(model_name="claude-3-7-sonnet-latest",
+                             anthropic_api_key=os.environ["ANTHROPIC_API_KEY"],
+                             max_tokens_to_sample=4096,
+                             temperature=0.0)
     response = chat.invoke(messages, temperature=0.0)
     text = response.content
     return text  
@@ -460,11 +582,17 @@ def get_model(model_name, temperature=0.0):
     if model_name.startswith("gpt"):
         chat = ChatOpenAI(model=model_name, max_tokens=4096, temperature=temperature)
     elif model_name.startswith("claude"):
-        chat = ChatAnthropic(model_name=model_name, anthropic_api_key=os.environ["ANTHROPIC_API_KEY"], max_tokens_to_sample=4096, temperature=temperature)
+        chat = ChatAnthropic(model_name=model_name, 
+                             anthropic_api_key=os.environ["ANTHROPIC_API_KEY"], 
+                             max_tokens_to_sample=4096, 
+                             temperature=temperature)
     elif model_name.startswith("gemini"):
-        chat = ChatGoogleGenerativeAI(model=model_name, api_key=os.environ["GOOGLE_API_KEY"], temperature=temperature)
+        # chat = ChatGoogleGenerativeAI(model=model_name, 
+        #                               api_key=os.environ["GOOGLE_API_KEY"])
+        chat = genai.GenerativeModel(model_name)
     else:
-        chat = ChatGroq(model_name=model_name, api_key=os.environ["GROQ_API_KEY"], temperature=temperature)
+        chat = None 
+        raise ValueError(f"Model {model_name} not supported")
     return chat
     
 
@@ -481,39 +609,6 @@ def build_glossary(glossary_file, fieldnames=None) -> VectorStoreRetriever:
     return retriever
 
 
-def glossary_rewrite(chat: ChatOpenAI, glossary: VectorStoreRetriever, text: str):
-    try:
-        terms = glossary.invoke(input=text)
-        print("\n".join([d.page_content for d in terms]))
-        glossary_str = "\n\n".join([d.page_content.replace('\n', '. ') for d in terms])
-
-        if len(terms) > 0:
-            messages = [
-                SystemMessage(content=f"""You are a helpful assistant that writes about products for ecommerce websites. Make sure to write in English language."""),
-                HumanMessage(content=f"""Rewrite the following text using the terms in the glossary. 
-Preserve the original text as much as possible.
-Replace the terms in original text that match the definition with the corresponding terms in the glossary.
-Output only the rewritten text in markdown format.                         
-
-Terms, Definitions
-{glossary_str}    
-
-Text to rewrite:
-{text}
-"""),]
-
-            print(f"HumanMessage={messages[1].content}")
-            response = chat.invoke(messages, temperature=0.0)
-            print(f"Response=\n{response.content}")
-            return response.content
-    except Exception as e:
-        print(e.__class__, e)
-        traceback.print_exc()
-        terms = []
-
-    return ""
-
-
 def improve_structure(chat: ChatOpenAI, structure: str):
     messages = [
         HumanMessage(content=improve_structure_prompt.format(structure=structure)),]
@@ -523,6 +618,28 @@ def improve_structure(chat: ChatOpenAI, structure: str):
     return response.content
 
 
+def evaluate(descriptions, 
+             reference_structure, 
+             reference_copy, 
+             key_features, 
+             included_phrases, 
+             excluded_phrases, 
+             language, 
+             chat):
+    messages = [
+        SystemMessage(content=f"""You are a helpful assistant that evaluates product descriptions based on the guidelines provided. Make sure to write in {language} language."""),
+        HumanMessage(content=evaluation_prompt.format(key_features=key_features, 
+                                                      structure=reference_structure, 
+                                                      copy=reference_copy, 
+                                                      included_phrases=included_phrases, 
+                                                      excluded_phrases=excluded_phrases, 
+                                                      product_descriptions=descriptions)),]
+    
+    response = chat.invoke(messages, temperature=0.0)
+    print(response)
+    return response
+
+
 def generate(*data):
     global visible
     print("visible", visible)
@@ -539,13 +656,6 @@ def generate(*data):
     print(f"{excluded_phrases=}")
     print(f"{included_phrases=}")
     print(f"{debug=}")
-    # print(f"{glossary=}")
-    print(f"{glossary_upload=}")
-    # print(f"{struct_ref=}")
-
-    glossary = None
-    if glossary_upload is not None:
-        glossary = build_glossary(glossary_upload)
 
     chat = get_model(model, temperature=temperature)
 
@@ -571,97 +681,116 @@ def generate(*data):
 
         key_features = key_features + ", " + detected_features + "\nIntended uses: " + intended_use
 
-    # if glossary:
-    #     print("Getting terms")
-    #     terms = glossary.invoke(input=feature + detected_features)
-    #     for term in terms:
-    #         print(term)
-
     batch = []
     min_length = 0
     max_length = 150
+    response = []
     for i in range(visible + 1):
         structure = struct_ref[2 * i]
         copy = struct_ref[2 * i + 1]  
-        if len((structure + copy).strip()) > 0:
-            if len(copy.strip()) > 0 and len(structure.strip()) > 0:    
-                print('------------')
-                print("Using both copy and structure")
-                # print("Improving structure")
-                # structure = improve_structure(chat=chat, structure=structure)
-                messages = [
-                    SystemMessage(content=f"""You are a helpful assistant that writes about products for ecommerce websites. Make sure to write in {languages[i]} language."""),
-                    HumanMessage(content=struct_copy_prompt.format(nversions=nversions, min_length=min_length, max_length=max_length, key_features=key_features, structure=structure, copy=copy, included_phrases=included_phrases, excluded_phrases=excluded_phrases)),]
-                
-            elif len(copy.strip()) > 0:
-                print('------------')
-                print("Using copy")
-                messages = [
-                    SystemMessage(content=f"""You are a helpful assistant that writes about products for ecommerce websites. Make sure to write in {languages[i]} language."""),
-                    HumanMessage(content=copy_prompt.format(nversions=nversions, min_length=min_length, max_length=max_length, key_features=key_features, structure=structure, copy=copy, included_phrases=included_phrases, excluded_phrases=excluded_phrases)),]
-                print(messages[1].content)
-                print('------------')
-
-            elif len(structure.strip()) > 0:
-                print('------------')
-                print("Using structure")
-                # print("Improving structure")
-                # structure = improve_structure(chat=chat, structure=structure)
+
+        if model.startswith("gemini"):
+            if len((structure + copy).strip()) > 0:
                 messages = [
-                    SystemMessage(content=f"""You are a helpful assistant that writes about products for ecommerce websites. Make sure to write in {languages[i]} language."""),
-                    HumanMessage(content=struct_prompt.format(nversions=nversions, min_length=min_length, max_length=max_length, key_features=key_features, structure=structure, copy=copy, included_phrases=included_phrases, excluded_phrases=excluded_phrases)),]
-                print(messages[1].content)
-                print('------------')
-            batch.append(messages)
+                    gemini_prompt.format(min_length=min_length, 
+                                         max_length=max_length, 
+                                         key_features=key_features, 
+                                         structure=structure, 
+                                         copy=copy, 
+                                         included_phrases=included_phrases, 
+                                         excluded_phrases=excluded_phrases)
+                ]
+
+                batch.append(messages)
+
+                ri = chat.generate_content(messages)
+                print("Gemini response: ", ri)
+                response.append(ri)
+        else:
+            if len((structure + copy).strip()) > 0:
+                if len(copy.strip()) > 0 and len(structure.strip()) > 0:    
+                    print('------------')
+                    print("Using both copy and structure")
+                    # print("Improving structure")
+                    # structure = improve_structure(chat=chat, structure=structure)
+                    messages = [
+                        SystemMessage(content=f"""You are a helpful assistant that writes about products for ecommerce websites. Make sure to write in {languages[i]} language."""),
+                        HumanMessage(content=struct_copy_prompt.format(nversions=nversions, min_length=min_length, max_length=max_length, key_features=key_features, structure=structure, copy=copy, included_phrases=included_phrases, excluded_phrases=excluded_phrases)),]
+                    
+                elif len(copy.strip()) > 0:
+                    print('------------')
+                    print("Using copy")
+                    messages = [
+                        SystemMessage(content=f"""You are a helpful assistant that writes about products for ecommerce websites. Make sure to write in {languages[i]} language."""),
+                        HumanMessage(content=copy_prompt.format(nversions=nversions, min_length=min_length, max_length=max_length, key_features=key_features, structure=structure, copy=copy, included_phrases=included_phrases, excluded_phrases=excluded_phrases)),]
+                    print(messages[1].content)
+                    print('------------')
+
+                elif len(structure.strip()) > 0:
+                    print('------------')
+                    print("Using structure")
+                    # print("Improving structure")
+                    # structure = improve_structure(chat=chat, structure=structure)
+                    messages = [
+                        SystemMessage(content=f"""You are a helpful assistant that writes about products for ecommerce websites. Make sure to write in {languages[i]} language."""),
+                        HumanMessage(content=struct_prompt.format(nversions=nversions, min_length=min_length, max_length=max_length, key_features=key_features, structure=structure, copy=copy, included_phrases=included_phrases, excluded_phrases=excluded_phrases)),]
+                    print(messages[1].content)
+                    print('------------')
+                batch.append(messages)
+
+            response = chat.batch(batch)
 
     descriptions = ""
-    
-    response = chat.batch(batch)
-    print(response)
 
-    parser = JsonOutputParser()
-    jresponse = [parser.parse(msg.content) for msg in response]
     descriptions = []
     descriptions_post = []
-    for i, jr in enumerate(jresponse):
-        print(f'{jr=}')
-        bestid = jr["best_version"]["id"]
-        for d in jr["versions"]:
-            if d["id"] == bestid:
-                bestd = d["content"] + (f"\n\nDebug info:\n\nScore: {d['score']}\n\nExplanation: {jr['best_version']['explanation']}" if debug else "")
-                bests  = d["score"]
-                break
-        # bests = 0
-        # bestd = ""
-        # for d in jr:
-        #     print(f'{d["score"]=}, {d["id"]=}, {bests=}')
-        #     if d["score"] > bests:
-        #         bests = d["score"]
-        #         bestd = d["content"] + (f"\n\nDebug info:\n\nScore: {d['score']}\n\nExplanation: {d['explanation']}" if debug else "")
-        #     elif d["score"] == bests and random.random() > 0.5:
-        #         bestd = d["content"] + (f"\n\nDebug info:\n\nScore: {d['score']}\n\nExplanation: {d['explanation']}" if debug else "")
-        #     if d["id"] == bestid:
-        #         bests = d["score"]
-        #         bestd = d["content"] + (f"\n\nDebug info:\n\nScore: {d['score']}\n\nExplanation: {d['explanation']}" if debug else "")
-        #         break
-    
-        bestd_post = post_process(text=bestd, guidance_prompt=guidance_prompt, language=languages[i], chat=chat)
+        
+    if model.startswith("gemini"):
+        descriptions = [msg.text for msg in response]
+        descriptions_post = [post_process(text=desc, 
+                                          guidance_prompt=guidance_prompt, 
+                                          language=languages[i], 
+                                          chat=None) for i, desc in enumerate(descriptions)]
+        
+        alt_texts_str = '\n\n### Alt text\n\n' + '\n- ' + '\n- '.join(alt_texts) if len(alt_texts) > 0 else ""
+        alt_text_dict = {k[0]: v for (k, v) in zip(image, alt_texts)} if len(alt_texts) > 0 else {}
+        result_json = {"outputs": descriptions, "alt_text": alt_text_dict}
+    else:
+        parser = JsonOutputParser()
+        jresponse = [parser.parse(msg.content) for msg in response]
+        for i, jr in enumerate(jresponse):
+            print(f'{jr=}')
+            bestid = jr["best_version"]["id"]
+            for d in jr["versions"]:
+                if d["id"] == bestid:
+                    bestd = d["content"] + (f"\n\nDebug info:\n\nScore: {d['score']}\n\nExplanation: {jr['best_version']['explanation']}" if debug else "")
+                    bests  = d["score"]
+                    break
+            
+            evaluated = evaluate(descriptions=jr["versions"], 
+                                reference_structure=struct_ref[2 * i], 
+                                reference_copy=struct_ref[2 * i + 1], 
+                                key_features=key_features, 
+                                included_phrases=included_phrases, 
+                                excluded_phrases=excluded_phrases, 
+                                language=languages[i], chat=chat)
+            print(f'{evaluated=}')
+        
+            bestd_post = post_process(text=bestd, 
+                                      guidance_prompt=guidance_prompt, 
+                                      language=languages[i], chat=chat)
 
-        descriptions.append(bestd)
-        descriptions_post.append(bestd_post)
+            descriptions.append(bestd)
+            descriptions_post.append(bestd_post)
 
-    # print("\n\nRewriting with glossary")
-    # rewrite = glossary_rewrite(chat=chat, glossary=glossary, text=descriptions[0])
-    # if rewrite != "":
-    #     descriptions[0] = "Original:\n\n" + descriptions[0] + "\n\nRewritten:\n\n" + rewrite
-    # print("\n\nDone rewriting with glossary\n\n")
+        alt_texts_str = '\n\n### Alt text\n\n' + '\n- ' + '\n- '.join(alt_texts) if len(alt_texts) > 0 else ""
+        alt_text_dict = {k[0]: v for (k, v) in zip(image, alt_texts)} if len(alt_texts) > 0 else {}
 
-    md_content = "\n\n---\n\n".join(descriptions)
+        result_json = {"outputs": jresponse if debug else descriptions, "alt_text": alt_text_dict}
 
-    alt_texts_str = '\n\n### Alt text\n\n' + '\n- ' + '\n- '.join(alt_texts) if len(alt_texts) > 0 else ""
+    md_content = "\n\n---\n\n".join(descriptions)
 
-    alt_text_dict = {k[0]: v for (k, v) in zip(image, alt_texts)} if len(alt_texts) > 0 else {}
-    result_json = {"outputs": jresponse if debug else descriptions, "alt_text": alt_text_dict}
+    
 
     # post_content = post_process(text=md_content, guidance_prompt=guidance_prompt, language=languages, chat=chat)
 
@@ -721,7 +850,7 @@ with gr.Blocks() as demo:
             garment_type = gr.Textbox(label="Garment Type", value="all", lines=1, interactive=True) 
             # language = gr.Dropdown(languages, value="American English", interactive=True, label="Language")
             with gr.Accordion(label="Advanced Options", open=False):
-                model = gr.Dropdown(models, value="claude-3-7-sonnet-latest", interactive=True, label="Model", visible=True)
+                model = gr.Dropdown(models, value=default_model, interactive=True, label="Model", visible=True)
                 temperature = gr.Slider(minimum=0., maximum=1.0, value=0., interactive=True, label="Temperature", visible=True)
                 nversions = gr.Slider(minimum=1, maximum=10, value=5, step=int, interactive=True, label="Number of versions", visible=True)
                 excluded_phrases = gr.Textbox(label="Excluded words", interactive=True, lines=2)

requirements.txt

@@ -8,4 +8,5 @@ bcrypt
 langchain_chroma
 langchainhub
 langchain_community
-langchain-google-genai
+langchain-google-genai
+google-generativeai

str2escaped.py

@@ -203,9 +203,40 @@ Return the result in in the following JSON format without any preceding or trail
     "category": the category of the garment, for example 'dress', 'shirt', 'pants', 'shoes', etc
 }}"""
 
+
+gemini_prompt = """You are given information of a product, a reference structure, and a reference copy. 
+Please analyze the structure, make a plan on how to follow the structure correctly, and write a product
+ description for the product. Use the tone of voice of the reference copy for the generated description. 
+Write from {min_length} to {max_length} words.
+Do not hallucinate, do not add information that is not in the product information. 
+Try your best to avoid using the excluded words and phrases.
+Try your best to include the included words and phrases.
+Do not enclose the output in html tags, quotes, braces, brackets or anything. 
+Return the product description only.
+
+<product_information>
+{key_features}
+</product_information>
+
+<reference_structure>
+{structure}
+</reference_structure>
+
+<reference_copy>
+{copy}
+</reference_copy>
+
+<excluded_phrases>
+{excluded_phrases}
+</excluded_phrases>
+
+<included_phrases>
+{included_phrases}
+</included_phrases>"""
+
 import codecs
 if __name__ == "__main__":
-    print(str2escaped(detect_feature))
+    print(str2escaped(gemini_prompt))
     # print()
     # print('"write_struct_copy": "' + str2escaped(both).strip() + '",')
     # print()