Switch to gradio app

app.py

@@ -1,81 +1,61 @@
-from flask import Flask, request, jsonify, render_template
-from flask_wtf import FlaskForm
-from wtforms import StringField, SubmitField
-from wtforms.validators import DataRequired
+import gradio as gr
 import requests
+import os
 
-app = Flask(__name__)
-app.config["SECRET_KEY"] = "secret123"
-
-class CommentForm(FlaskForm):
-    comment = StringField("Comment", validators=[DataRequired()])
-    submit = SubmitField("Predict")
+# Load API token from environment variable
+HF_TOKEN = os.environ.get("HF_TOKEN")
 
 API_URL = "https://api-inference.huggingface.co/models/Hate-speech-CNERG/dehatebert-mono-english"
-HEADERS = {"Authorization": "Bearer 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"}
 
 def query(text):
-    response = requests.post(API_URL, headers=HEADERS, json={"inputs": text})
+    headers = {"Authorization": f"Bearer {HF_TOKEN}"}
+    response = requests.post(API_URL, headers=headers, json={"inputs": text})
     return response.json()
 
-@app.route("/", methods=["GET", "POST"])
-def home():
-    form = CommentForm()
-    prediction = None
-    prediction_prob = None
-
-    if form.validate_on_submit():
-        text = form.comment.data
-        result = query(text)
-        if isinstance(result, list):
-            result = result[0][0]
-            label = result["label"]
-            score = result["score"]
-            if label == "HATE":
-                prediction = "Hate Speech"
-            else:
-                prediction = "No Hate Speech"
-                score = 1 - score
-            prediction_prob = round(score * 100, 2)
-        else:
-            prediction = "Error - try again"
-            prediction_prob = 0
-
-    return render_template("index.html", form=form, prediction=prediction, prediction_prob=prediction_prob)
-
-@app.route("/predict", methods=["GET", "POST"])
-def predict():
-    data = request.get_json()
-    text = data["comment"]
-    result = query(text)
-    if isinstance(result, list):
-        result = result[0][0]
-        label = result["label"]
-        score = result["score"]
-        if label == "HATE":
-            prediction = "Hate Speech"
-        else:
-            prediction = "No Hate Speech"
-            score = 1 - score
-        return jsonify({"prediction": prediction, "probability": round(score, 4)})
-    return jsonify({"error": "model loading"})
-
-@app.route("/prediction_by_api", methods=["GET", "POST"])
-def prediction_by_api():
-    data = request.get_json()
-    text = data["comment"]
-    result = query(text)
+def predict(comment):
+    if not comment.strip():
+        return "⚠️ Please enter some text.", ""
+    
+    result = query(comment)
+    
     if isinstance(result, list):
         result = result[0][0]
         label = result["label"]
         score = result["score"]
+        
         if label == "HATE":
-            prediction = "Hate Speech"
+            prediction = "🚨 Hate Speech Detected"
+            probability = round(score * 100, 2)
         else:
-            prediction = "No Hate Speech"
-            score = 1 - score
-        return jsonify({"prediction": prediction, "probability": round(score, 4)})
-    return jsonify({"error": "model loading"})
+            prediction = "✅ No Hate Speech"
+            probability = round((1 - score) * 100, 2)
+        
+        return prediction, f"{probability}%"
+    
+    return "❌ Error - Model loading, try again.", ""
+
+# Gradio UI
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Textbox(
+        lines=4,
+        placeholder="Enter a comment to analyze...",
+        label="Comment"
+    ),
+    outputs=[
+        gr.Textbox(label="Prediction"),
+        gr.Textbox(label="Confidence")
+    ],
+    title="🛡️ Hate Speech Detector",
+    description="Enter any text to check if it contains hate speech.",
+    examples=[
+        ["I love everyone and believe in equality!"],
+        ["I hate those people, they should be banned."],
+        ["This is a great day to be alive!"],
+        ["Those people are disgusting and should leave."]
+    ],
+    theme=gr.themes.Soft()
+)
 
 if __name__ == "__main__":
-    app.run(debug=True)
+    demo.launch()

requirements.txt

@@ -1,84 +1,2 @@
-absl-py==1.4.0
-asttokens==2.2.1
-astunparse==1.6.3
-backcall==0.2.0
-cachetools==5.3.1
-certifi==2023.5.7
-charset-normalizer==3.2.0
-click==8.1.4
-cloudpickle==2.2.1
-colorama==0.4.6
-comm==0.1.3
-debugpy==1.6.7
-decorator==5.1.1
-executing==1.2.0
-Flask==1.1.2
-Flask-WTF==0.14.3
-flatbuffers==23.5.26
-gast==0.4.0
-google-auth==2.22.0
-google-auth-oauthlib==0.4.6
-google-pasta==0.2.0
-grpcio==1.56.0
-gunicorn==20.1.0
-h5py==3.8.0
-idna==3.4
-importlib-metadata==6.7.0
-ipykernel==6.16.2
-ipython==7.34.0
-itsdangerous==2.0.1
-jedi==0.18.2
-Jinja2==3.0.0
-jupyter_client==8.0.0a1
-jupyter_core==5.0.0rc2
-keras==2.10.0
-Keras-Preprocessing==1.1.2
-libclang==16.0.0
-Markdown==3.4.3
-MarkupSafe==2.1.3
-matplotlib-inline==0.1.6
-nest-asyncio==1.5.6
-numpy==1.21.6
-oauthlib==3.2.2
-opt-einsum==3.3.0
-packaging==23.1
-parso==0.8.3
-pickleshare==0.7.5
-platformdirs==3.8.1
-prompt-toolkit==3.0.39
-protobuf==3.19.6
-psutil==5.9.5
-pure-eval==0.2.2
-pyasn1==0.5.0
-pyasn1-modules==0.3.0
-Pygments==2.15.1
-python-dateutil==2.8.2
-python-dotenv==0.21.1
-pyzmq==25.1.0
-requests==2.31.0
-requests-oauthlib==1.3.1
-rsa==4.9
-six==1.16.0
-spyder-kernels==2.2.0
-stack-data==0.6.2
-tensorboard==2.10.1
-tensorboard-data-server==0.6.1
-tensorboard-plugin-wit==1.8.1
-tensorflow==2.10.0
-tensorflow-estimator==2.10.0
-tensorflow-hub==0.13.0
-tensorflow-io-gcs-filesystem==0.31.0
-tensorflow-text==2.10.0
-termcolor==2.3.0
-tornado==6.2
-traitlets==5.9.0
-typing_extensions==4.7.1
-urllib3==1.26.16
-wcwidth==0.2.6
-Werkzeug==2.0.3
-wrapt==1.15.0
-WTForms==2.3.3
-zipp==3.15.0
-transformers
-torch
-sentencepiece
+gradio 
+requests