Create app.py

app.py

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+import openai
+from IPython.display import Markdown, display
+import os
+import json
+import gradio as gr
+
+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):
+    messages = [
+        {'role': 'system', 'content': system_message_simple},
+        {'role': 'user', 'content': article}
+    ]
+    
+    response = get_completion_messages_gpt(messages)
+    clean_response = response.strip('```json').strip('```')
+    
+    try:
+        parsed_response = json.loads(clean_response)
+    except json.JSONDecodeError:
+        return ["Invalid response received from OpenAI"] * 10
+
+    # Extract different parts of the response
+    word_count = json.dumps(parsed_response.get('Word count', {}), indent=2)
+    language_grammar = json.dumps(parsed_response.get('Language & Grammar', {}), indent=2)
+    word_usage = json.dumps(parsed_response.get('Word Usage', {}), indent=2)
+    flow_structure = json.dumps(parsed_response.get('Flow & Structure', {}), indent=2)
+    technical_soundness = json.dumps(parsed_response.get('Technical Soundness', {}), indent=2)
+    reference_links = json.dumps(parsed_response.get('Reference Links', {}), indent=2)
+    faqs = json.dumps(parsed_response.get('FAQs', {}), indent=2)
+    top_suggestions = json.dumps(parsed_response.get('Top5 Suggestions', []), indent=2)
+    edited_article = parsed_response.get('Edited Article', "No edited article found")
+    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)
+
+# Gradio Interface with improved layout
+with gr.Blocks() as demo:
+    gr.Markdown("## AV Blog Editor V1")
+    gr.Markdown("### Guidelines (make sure to read these before using)")
+    gr.Markdown("""
+    1. Use your discretion while using this tool
+    2. The italicised content in the edited article represents the changes made by GPT. Make sure to review the changes.
+    3. 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 :)
+    """)
+
+    # Input article in a single row
+    with gr.Row():
+        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")
+            language_grammar = gr.Textbox(label="Language & Grammar", lines=3)
+            word_usage = gr.Textbox(label="Word Usage", lines=3)
+            flow_structure = gr.Textbox(label="Flow & Structure", lines=3)
+            technical_soundness = gr.Textbox(label="Technical Soundness", lines=3)
+
+        with gr.Column():
+            reference_links = gr.Textbox(label="Reference Links", lines=3)
+            faqs = gr.Textbox(label="FAQs", lines=3)
+            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")
+
+    # Button to submit and process the article
+    submit_btn = gr.Button("Submit")
+
+    # Action on submit
+    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]
+    )
+
+# Launch the app
+# demo.launch()