Update app.py

app.py

@@ -7,41 +7,33 @@ import requests
 HUGGINGFACE_API_KEY = "your_huggingface_api_key"  # Replace with your API key from https://huggingface.co/settings/tokens
 HEADERS = {"Authorization": f"Bearer {HUGGINGFACE_API_KEY}"}
 
-# Hugging Face Model Endpoints (Replace with your fine-tuned models if needed)
-CLASSIFIER_API_URL = "https://api-inference.huggingface.co/models/unitary/unbiased-toxic-roberta"
-GENERATOR_API_URL = "https://api-inference.huggingface.co/models/facebook/blenderbot-3B"
+# Hugging Face Model Endpoints (Using Pretrained Models)
+CLASSIFIER_API_URL = "https://api-inference.huggingface.co/models/Hate-speech-CNERG/bert-base-uncased-hatexplain"
+GENERATOR_API_URL = "https://api-inference.huggingface.co/models/KAXY/GPT2-against-hate"
 
 # -----------------------------------------------------------
 # FUNCTION DEFINITIONS
 # -----------------------------------------------------------
 def detect_harmful_content(text):
-    """
-    Detect harmful content in the input text using Hugging Face API.
-    """
+    """Detects harmful content using a Hugging Face model."""
     payload = {"inputs": text}
     response = requests.post(CLASSIFIER_API_URL, headers=HEADERS, json=payload)
-    
+
     if response.status_code != 200:
         return [{"category": "Error", "score": 0, "message": "Failed to fetch response"}]
     
     results = response.json()
-    
-    if not isinstance(results, list):  # Handle unexpected response format
-        return [{"category": "Error", "score": 0, "message": "Invalid response format"}]
-    
+
     detected = []
-    threshold = 0.8  # Set confidence threshold
+    threshold = 0.5  # Adjust threshold for sensitivity
     for result in results:
-        if isinstance(result, dict) and 'label' in result and 'score' in result:
-            if result['score'] >= threshold:
-                detected.append({"category": result['label'], "score": result['score']})
+        if result.get('score', 0) >= threshold:
+            detected.append({"category": result.get('label', 'Unknown'), "score": result.get('score', 0)})
     
     return detected
 
 def generate_mitigation_response(text, detected_categories):
-    """
-    Generate a moderation response based on detected harmful categories.
-    """
+    """Generates a mitigation response based on detected harmful speech."""
     if not detected_categories:
         return "✅ Content appears safe. No harmful content detected."
 
@@ -54,23 +46,21 @@ def generate_mitigation_response(text, detected_categories):
     response = requests.post(GENERATOR_API_URL, headers=HEADERS, json=payload)
 
     if response.status_code != 200:
-        return "⚠️ Error: Could not generate a response. Please try again later."
+        return "⚠️ Error: Could not generate a response."
 
     generated = response.json()
-
-    # Ensure response contains the generated text
-    if isinstance(generated, list) and len(generated) > 0 and 'generated_text' in generated[0]:
-        return generated[0]['generated_text']
-    else:
-        return "⚠️ No valid response generated. Please try again."
+    return generated[0].get('generated_text', "No response generated.")
 
 # -----------------------------------------------------------
 # STREAMLIT USER INTERFACE
 # -----------------------------------------------------------
+st.set_page_config(page_title="Hate Speech Detector", layout="centered")
+
 st.title("🔍 AI-Powered Hate Speech Detection & Mitigation")
-st.markdown("Detects hate speech, misinformation, and cyberbullying in social media posts.")
+st.markdown("Detects **hate speech, misinformation, and cyberbullying** in social media posts.")
 
-user_input = st.text_area("✏️ Enter the text to analyze:")
+# User Input
+user_input = st.text_area("✏️ Enter the text to analyze:", height=150)
 
 if st.button("Analyze"):
     if user_input.strip() == "":
@@ -78,7 +68,7 @@ if st.button("Analyze"):
     else:
         st.markdown("### 📊 Analysis Results")
         detected = detect_harmful_content(user_input)
-        
+
         if detected and detected[0].get("category") != "Error":
             for d in detected:
                 st.write(f"**Category:** {d['category']} | **Confidence:** {d['score']:.2f}")
@@ -89,3 +79,4 @@ if st.button("Analyze"):
         mitigation_response = generate_mitigation_response(user_input, detected)
         st.write(mitigation_response)
 
+