Update app.py

app.py

@@ -1,73 +1,20 @@
-import openai
-import os
-import json
 import gradio as gr
+import json
 
-with open('mykey.txt', 'r') as file:
-    openai_key = file.read()
-    
-os.environ['OPENAI_API_KEY'] = openai_key
-
-def get_completion_messages_gpt(messages, model="gpt-4o", temperature=0, presence_penalty=0, seed = 10, stream=False):
-    response = openai.chat.completions.create(
-        model=model,
-        messages=messages,
-        temperature=temperature, 
-        presence_penalty=presence_penalty,
-        seed=seed,
-        stream = stream
-    )
-    return response.choices[0].message.content
-
-system_message_simple = f"""
-You are a Blog editor at Analytics Vidhya. Analytics Vidhya is an edtech platform focussing on creating
-learning content for topics related to Data science, AI, Machine Learning and especially Generative AI.\
-You crtically evaluate for the following parameters in the blogs you edit:\
-
-1) Appropriate language and grammar structure
-2) Correct word usage
-3) Flow and struture of the article
-4) Technical soundness of the article
-5) Whether appropriate reference links are provided wherever required, especially where third party information is mentioned
-6) FAQs are relevant to the article and non-repetetive
-
-Rate the article on these parameters on a scale of 10 and provide suggestions to improve the respective parameter scores with \
-specific suggestions. 
-For the parameters - use the keys mentioned in the json format below. 
-For the above parameters use sub-keys - Score and Suggestions, to show the respective output.  
-
-Also provide Top 5 suggestions for improvement in the article.
-
-Finally provide the edited article incorporating all the suggestions. \
-While editing the article also check whether the content of the article is human written or generated by generative AI tools like chatGPT.
-Articles which have a high percentage of generative AI content rank lower on SEO, so your goal is to edit articles in a way\
-that the artcle is more than 90% human written even after editing. Give the percentatge of content written by Generative AI\
-before and after editing.
-
-Compare the original article with the edited article and in the edited article italicise the changes made compared \
-to the original article using markdown and mention the text which should be hyperlinked with a reference link within double exclamation marks.
-
-Provide your output in JSON format with the keys: 
-Word count, Language & Grammar, Word Usage, Flow & Structure, Technical Soundness, Reference Links, FAQs, Top5 Suggestions, Edited Article, \
-PreEditing GenAi Content%, PreEditing GenAi Content%. 
-Format the output such that the output can be directly used by python json library.
-
-"""
-
-# Function to process the input article
-def process_article(article):
+# Function to process all parameters including highlighted response
+def process_all_parameters(article):
     messages = [
-        {'role': 'system', 'content': system_message_simple},
+        {'role': 'system', 'content': system_message_1},
         {'role': 'user', 'content': article}
     ]
     
-    response = get_completion_messages_gpt(messages)
+    response = agent(messages)
     clean_response = response.strip('```json').strip('```')
-    
+
     try:
         parsed_response = json.loads(clean_response)
     except json.JSONDecodeError:
-        return ["Invalid response received from OpenAI"] * 10
+        return ["Invalid response received from OpenAI"] * 11
 
     # Extract different parts of the response
     word_count = json.dumps(parsed_response.get('Word count', {}), indent=2)
@@ -82,28 +29,31 @@ def process_article(article):
     pre_editing_genai = json.dumps(parsed_response.get('PreEditing GenAi Content%', {}), indent=2)
     post_editing_genai = json.dumps(parsed_response.get('PostEditing GenAi Content%', {}), indent=2)
     
-    return (word_count, language_grammar, word_usage, flow_structure, technical_soundness, 
-            reference_links, faqs, top_suggestions, edited_article, pre_editing_genai, post_editing_genai)
+    # Process highlighted response
+    messages2 = [
+        {'role': 'system', 'content': system_message_2},
+        {'role': 'user', 'content': f"This is the original article: {article}. This is the edited article: {edited_article}"}
+    ]
+    
+    highlighted_response = agent(messages2)
+    
+    return (word_count, language_grammar, word_usage, flow_structure, technical_soundness,
+            reference_links, faqs, top_suggestions, pre_editing_genai, post_editing_genai, 
+            edited_article, highlighted_response)
 
-# Gradio Interface with improved layout
+# Gradio Interface
 with gr.Blocks() as demo:
-    gr.Markdown("## AV Blog Editor V1")
-    gr.Markdown("### Guidelines (make sure to read these before using)")
+    gr.Markdown("## AV Blog Editor V2")
+    gr.Markdown("### Guidelines")
     gr.Markdown("""
-    1. Use your discretion while using this tool
-    2. (not perfect)The italicised content in the edited article represents the changes made by GPT. Make sure to review the changes.
-    3. (not perfect)The text under !! in the edited article shows approximate areas where reference link is required. The link it shows may not be correct.
-    4. The score and suggestions of various sections are good pointers to reflect back on your article.
-    5. The percentage of GPT generated content will not match what zerogpt.com says. Go with what zerogpt says.
-    6. Make sure to check zerogpt before using the edited article
-    7. Do share any suggestions for improvements in this tool with Apoorv. You can get some candies for that :)
+    1. Use your discretion while using this tool.
+    2. The suggestions will appear first and then the edited article with highlights. Expected run time: 3 min
+    3. The percentage of GPT generated content will not match what zerogpt.com says.
+    4. Do share any suggestions for improvements in this tool with Apoorv. You can get some candies for that :)
     """)
 
-    # Input article in a single row
-    with gr.Row():
-        article_input = gr.Textbox(label="Input Article", lines=15, placeholder="Enter your article here...")
+    article_input = gr.Textbox(label="Input Article", lines=15, placeholder="Enter your article here...")
 
-    # Outputs in two columns
     with gr.Row():
         with gr.Column():
             word_count = gr.Textbox(label="Word Count")
@@ -118,21 +68,26 @@ with gr.Blocks() as demo:
             top_suggestions = gr.Textbox(label="Top 5 Suggestions", lines=5)
             pre_editing_genai = gr.Textbox(label="PreEditing GenAI Content%", lines=1)
             post_editing_genai = gr.Textbox(label="PostEditing GenAI Content%", lines=1)
-    
-    # Edited article markdown
+
     gr.Markdown("### Edited Article")
-    edited_article = gr.Markdown(label="Edited Article")
+    highlighted_response = gr.Markdown(label="Highlighted Response")
 
-    # Button to submit and process the article
     submit_btn = gr.Button("Submit")
 
-    # Action on submit
+    # Handle everything in one go
+    def process(article):
+        return process_all_parameters(article)
+
+    # Process the article and get all outputs
     submit_btn.click(
-        process_article, 
-        inputs=[article_input], 
-        outputs=[word_count, language_grammar, word_usage, flow_structure, technical_soundness, 
-                 reference_links, faqs, top_suggestions, edited_article, pre_editing_genai, post_editing_genai]
+        process,
+        inputs=[article_input],
+        outputs=[
+            word_count, language_grammar, word_usage, flow_structure, technical_soundness, 
+            reference_links, faqs, top_suggestions, pre_editing_genai, post_editing_genai, 
+            gr.Textbox(label="Edited Article"), highlighted_response
+        ]
     )
 
 # Launch the app
-demo.launch()
+demo.launch()