app improvement again

app.py

@@ -14,11 +14,6 @@ st.set_page_config(
     layout="wide"
 )
 
-# Initialize session state
-if 'history' not in st.session_state:
-    st.session_state.history = []
-if 'api_health' not in st.session_state:
-    st.session_state.api_health = None
 
 # Custom CSS
 st.markdown("""
@@ -43,13 +38,6 @@ st.markdown("""
     </style>
 """, unsafe_allow_html=True)
 
-def check_api_health():
-    """Check if the API is responsive"""
-    try:
-        response = requests.get("https://llepogam-hate-speech-detection-api.hf.space/health")
-        return response.status_code == 200
-    except:
-        return False
 
 def hate_speech_detection(text):
     """Make API call with error handling"""
@@ -86,8 +74,10 @@ def get_severity_class(probability):
 # Header Section
 st.title("🚫 Offensive Speech Detection")
 st.markdown("""
-This application helps identify potentially offensive or harmful content in text. 
-It uses a machine learning model to analyze text and determine if it contains offensive speech.
+This application helps identify potentially offensive oontent in text provided by an user. 
+
+It uses a machine learning model to analyze text and determine if it contains offensive speech. 
+
 
 **How it works:**
 1. Enter your text in the input box below
@@ -95,22 +85,13 @@ It uses a machine learning model to analyze text and determine if it contains of
 3. Results show both the classification and confidence level
 """)
 
-# API Status
-if st.button("Check API Status"):
-    with st.spinner("Checking API health..."):
-        st.session_state.api_health = check_api_health()
-    
-if st.session_state.api_health is not None:
-    status_color = "green" if st.session_state.api_health else "red"
-    status_text = "Online" if st.session_state.api_health else "Offline"
-    st.markdown(f"API Status: :{status_color}[{status_text}]")
 
 # Example inputs
 with st.expander("📝 Example Inputs"):
     st.markdown("""
     Try these example texts to test the model:
     1. "Have a great day!"
-    2. "I disagree with your opinion."
+    2. "You are the worst person in the world."
     3. "You're amazing!"
     
     Click on any example to copy it to the input box.
@@ -126,22 +107,23 @@ with st.expander("📝 Example Inputs"):
 with st.expander("❓ Frequently Asked Questions"):
     st.markdown("""
     **Q: What is considered offensive speech?**
-    - A: The model identifies content that could be harmful, insulting, or discriminatory.
+    - A: The model is using a dataset of tweets, which were tagged as offensive or not. More information on the dataset can be found here : https://huggingface.co/datasets/christophsonntag/OLID
+
+    **Q: How is the prediction done?**
+    - A: The model predicts a value between 1 and 0. The closer it is to 1, the more offensive is the prediction.  When the prediction is higher than 0.5, the text is considered as offensive
 
     **Q: How accurate is the detection?**
-    - A: The model provides a confidence score with each prediction. Higher scores indicate greater confidence.
+    - A: The model created has an accuracy of 73.1%, which means than prediction are correct almost 3 times out of four. When the targeted values is below 0.3 or higher than 0.7, it means than there is a high level of confidence in the prediction 
 
-    **Q: What happens to my input data?**
-    - A: Your text is only used for prediction and temporarily stored in your session history.
     """)
 
 # Text Input Section
-max_chars = 500
+max_chars = 140 
 user_input = st.text_area(
     "Enter text to analyze:",
     height=100,
     key="user_input",
-    help="Enter the text you want to analyze for offensive content. Maximum 500 characters.",
+    help="Enter the text you want to analyze for offensive content. Maximum 140 characters.",
     max_chars=max_chars
 )
 
@@ -166,7 +148,7 @@ if user_input:
                 st.error(f"Error: {error}")
             else:
                 # Format probability as percentage
-                probability_pct = result['probability'] * 100
+                probability_pct = result['probability']
                 
                 # Create prediction box with appropriate severity class
                 severity_class = get_severity_class(result['probability'])
@@ -188,9 +170,9 @@ if user_input:
                         'axis': {'range': [0, 100]},
                         'bar': {'color': "darkblue"},
                         'steps': [
-                            {'range': [0, 40], 'color': "lightgreen"},
-                            {'range': [40, 70], 'color': "orange"},
-                            {'range': [70, 100], 'color': "red"}
+                            {'range': [0, 0.3], 'color': "lightgreen"},
+                            {'range': [0.3, 0.7], 'color': "orange"},
+                            {'range': [0.7, 1], 'color': "red"}
                         ]
                     }
                 ))
@@ -231,7 +213,7 @@ if st.session_state.history:
 st.markdown("---")
 st.markdown("""
     <div style='text-align: center'>
-        <p>Developed with ❤️ for safer online communication</p>
+        <p>Developed with ❤️ by Louis Le Pogam</p>
     </div>
 """, unsafe_allow_html=True)