major design improvement

app.py

@@ -3,6 +3,8 @@ import json
 import os
 from mistralai.client import MistralClient
 from mistralai.models.chat_completion import ChatMessage
+import re
+
 
 client = MistralClient(api_key=os.environ["MISTRAL_API_KEY"])
 model = "open-mixtral-8x7b"
@@ -22,44 +24,155 @@ messages = {
     }
 }
 
+
 def make_request(client, model, system_message, user_message, temperature=0.7, top_p=0.6, max_tokens=50):
+    print(f"Making request with system message: {system_message}")
+    print(f"Making request with user message: {user_message}")
     messages = [
         ChatMessage(role="system", content=system_message),
         ChatMessage(role="user", content=user_message)
     ]
     response = client.chat(model=model, messages=messages, temperature=temperature, top_p=top_p, max_tokens=max_tokens)
+    print(f"Received response from the model: {response}")
     return response.choices[0].message.content if response.choices else ""
 
 def moderate_content(content, content_context, moderation_rules, language):
+    print(f"Moderating content: {content}")
+    print(f"Content context: {content_context}")
+    print(f"Moderation rules: {moderation_rules}")
+    print(f"Language: {language}")
+    
     system_message = messages[language]['system_message'].format("content", content_context)
     user_message = messages[language]['user_message'].format(content, content_context)
     
     response = make_request(client, model, system_message, user_message, temperature=0.3, top_p=0.6, max_tokens=50)
-    try:
-        response_json = json.loads(response)
-        if 'spam' in response_json:
-            spam_status = response_json['spam'].lower()
-            if spam_status == 'true':
-                return "Spam"
-            elif spam_status == 'false':
-                return "Not Spam"
-            elif spam_status == 'possible':
-                return "Possibly Spam"
-    except (json.JSONDecodeError, KeyError, ValueError):
-        pass
+    print(f"Received response: {response}")
+    
+    # Extract the JSON part of the response using regular expressions
+    json_match = re.search(r'{.*}', response, re.DOTALL)
+    if json_match:
+        json_response = json_match.group()
+        print(f"Extracted JSON response: {json_response}")
+        try:
+            response_json = json.loads(json_response)
+            print(f"Parsed JSON response: {response_json}")
+            if 'spam' in response_json:
+                spam_status = response_json['spam'].lower()
+                print(f"Spam status: {spam_status}")
+                if spam_status == 'true':
+                    return "Spam"
+                elif spam_status == 'false':
+                    return "Not Spam"
+                elif spam_status == 'possible':
+                    return "Possibly Spam"
+        except (json.JSONDecodeError, KeyError, ValueError) as e:
+            print(f"Error occurred while parsing JSON: {str(e)}")
+    else:
+        print("No JSON found in the response.")
+    
     return "Unable to determine spam status"
 
-content = gr.Textbox(label="Content")
-content_context = gr.Textbox(label="What is the context of this content?")
-moderation_rules = gr.Textbox(label="Moderation Rules")
-language = gr.Dropdown(["English", "Spanish", "French"], label="Language")
+def format_spam_status(spam_status):
+    print(f"Formatting spam status: {spam_status}")
+    if spam_status == "Spam":
+        return "<div style='background-color: #ff4d4d; color: white; padding: 10px; border-radius: 5px;'><strong>Spam</strong></div>"
+    elif spam_status == "Not Spam":
+        return "<div style='background-color: #66bb6a; color: white; padding: 10px; border-radius: 5px;'><strong>Not Spam</strong></div>"
+    elif spam_status == "Possibly Spam":
+        return "<div style='background-color: #ffa726; color: white; padding: 10px; border-radius: 5px;'><strong>Possibly Spam</strong></div>"
+    else:
+        return spam_status
+
 
-interface = gr.Interface(
-    fn=moderate_content,
-    inputs=[content, content_context, moderation_rules, language],
-    outputs="text",
-    title="Content Moderation",
-    description="Enter a piece of content, its context, and moderation rules to determine if it's spam.",
-)
+with gr.Blocks() as demo:
+    gr.Markdown(
+        """
+        # 🚨 Content Moderation Pilot 🚨
+        
+        This is a pilot application for content moderation using the Mistral AI API. The intended use of this moderation system is via an API integration. 🌐
+        
+        Please enter the content, context, moderation rules, and select the language to determine if the content is spam. 🕵️‍♀️
+        """
+    )
+    
+    with gr.Row():
+        with gr.Column():
+            content = gr.Textbox(label="Content", placeholder="Enter the content to moderate...")
+            content_context = gr.Textbox(label="What is the context of this content?", placeholder="Provide the context...")
+        with gr.Column():    
+            moderation_rules = gr.Textbox(label="Moderation Rules", placeholder="Enter the moderation rules...")
+            language = gr.Dropdown(["English", "Spanish", "French"], label="Language", value="English")
+    
+    submit_button = gr.Button("🚀 Moderate Content")
+    
+    gr.Markdown("---")
+    
+    with gr.Row():
+        spam_status = gr.HTML(label="Spam Status")
+
+    submit_button.click(
+        fn=moderate_content,
+        inputs=[content, content_context, moderation_rules, language],
+        outputs=spam_status
+    )
+    
+    gr.Markdown(
+        """
+        ## 📚 API Tutorial
+        
+        To use the content moderation system programmatically, you can follow these steps:
+        
+        1. Make a POST request to the `/call/moderate` endpoint with the following JSON payload:
+        
+        ```json
+        {
+            "data": [
+                "The content to moderate",
+                "The context of the content",
+                "The moderation rules to apply",
+                "The language of the content (English, Spanish, or French)"
+            ]
+        }
+        ```
+        
+        2. The POST request will return a unique `event_id` that you can use to retrieve the results.
+        
+        3. Make a GET request to the `/call/moderate/<event_id>` endpoint to stream the results using server-sent events.
+        
+        Here's an example using `curl`:
+        
+        1. POST request to submit the moderation task:
+        
+        ```bash
+        curl -X POST -H "Content-Type: application/json" -d '{
+            "data": [
+                "This is the content to moderate",
+                "Social media post",
+                "Detect spam and offensive language",
+                "English"
+            ]
+        }' https://huggingface.co/spaces/monsimas/SpamOrNot/call/moderate
+        ```
+        
+        2. GET request to retrieve the results:
+        
+        ```bash
+        curl -N https://huggingface.co/spaces/monsimas/SpamOrNot/call/moderate/<event_id>
+        ```
+        
+        Replace `<event_id>` with the actual `event_id` returned from the POST request.
+        
+        The GET request will stream the moderation results using server-sent events. The events will have the following structure:
+        
+        ```
+        event: completed
+        data: {"spam_status": "Spam" | "Not Spam" | "Possibly Spam"}
+        ```
+        
+        You can integrate this API into your content moderation workflow to automatically detect and flag spam content. 🚀
+        
+        Note: If you're using a programming language like Python, you can use libraries like `requests` to make the API requests and handle the server-sent events.
+        """
+    )
 
-interface.launch()
+demo.launch()