Create app.py

app.py

@@ -0,0 +1,81 @@
+import streamlit as st
+import requests
+
+# -----------------------------------------------------------
+# SETUP: Hugging Face API and Models
+# -----------------------------------------------------------
+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"
+
+# -----------------------------------------------------------
+# FUNCTION DEFINITIONS
+# -----------------------------------------------------------
+def detect_harmful_content(text):
+    """
+    Detect harmful content in the input text using Hugging Face API.
+    """
+    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()
+    
+    detected = []
+    threshold = 0.8  # Set confidence threshold
+    for result in results:
+        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.
+    """
+    if not detected_categories:
+        return "✅ Content appears safe. No harmful content detected."
+
+    categories_str = ", ".join([cat["category"] for cat in detected_categories])
+    prompt = (f"The following content has been flagged for {categories_str}:\n\n"
+              f"\"{text}\"\n\n"
+              "Please generate a respectful and informative moderation response.")
+
+    payload = {"inputs": prompt, "parameters": {"max_length": 150}}
+    response = requests.post(GENERATOR_API_URL, headers=HEADERS, json=payload)
+
+    if response.status_code != 200:
+        return "⚠️ Error: Could not generate a response."
+
+    generated = response.json()
+    return generated[0].get('generated_text', "No response generated.")
+
+# -----------------------------------------------------------
+# STREAMLIT USER INTERFACE
+# -----------------------------------------------------------
+st.title("🔍 AI-Powered Hate Speech Detection & Mitigation")
+st.markdown("Detects hate speech, misinformation, and cyberbullying in social media posts.")
+
+user_input = st.text_area("✏️ Enter the text to analyze:")
+
+if st.button("Analyze"):
+    if user_input.strip() == "":
+        st.error("⚠️ Please enter some text to 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}")
+        else:
+            st.write("✅ No harmful content detected.")
+
+        st.markdown("### 💡 Mitigation Response")
+        mitigation_response = generate_mitigation_response(user_input, detected)
+        st.write(mitigation_response)