Create app.py

app.py

@@ -0,0 +1,46 @@
+import gradio as gr
+import requests
+import os
+
+# Hugging Face API Token (Set this in Hugging Face Spaces)
+HF_API_TOKEN = os.getenv("HF_API_TOKEN")
+
+# Model endpoints for Hindi and Telugu toxicity detection
+MODEL_ENDPOINTS = {
+    "Hindi": "https://api-inference.huggingface.co/models/LingoIITGN/mBERT_toxic_hindi",
+    "Telugu": "https://api-inference.huggingface.co/models/LingoIITGN/mBERT_toxic_telugu"
+}
+
+# Function to send a request to the Hugging Face API
+def get_toxicity_prediction(text, language):
+    if language not in MODEL_ENDPOINTS:
+        return "Error: Model not found for the selected language"
+
+    url = MODEL_ENDPOINTS[language]
+    headers = {"Authorization": f"Bearer {HF_API_TOKEN}"}
+    payload = {"inputs": text}
+
+    response = requests.post(url, headers=headers, json=payload)
+
+    if response.status_code == 200:
+        result = response.json()[0]  # Extract first prediction
+        toxicity_score = result["score"] * 100  # Convert to percentage
+        is_toxic = "Toxic" if result["label"].lower() == "toxic" else "Non-Toxic"
+        return f"Toxicity Score: {toxicity_score:.2f}%\nClassification: {is_toxic}"
+    else:
+        return f"Error: {response.text}"
+
+# Define Gradio interface
+with gr.Blocks() as app:
+    gr.Markdown("# 🛡️ ToxiGuard - Multilingual Toxicity Detector")
+    
+    text_input = gr.Textbox(label="Enter your text")
+    language_dropdown = gr.Dropdown(choices=["Hindi", "Telugu"], label="Select Language", value="Hindi")
+    
+    submit_button = gr.Button("Check Toxicity")
+    output_text = gr.Textbox(label="Result")
+
+    submit_button.click(fn=get_toxicity_prediction, inputs=[text_input, language_dropdown], outputs=output_text)
+
+# Launch the app
+app.launch()