Update app.py

app.py

@@ -1,64 +1,37 @@
+import numpy as np
+import pickle
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()
+from tensorflow.keras.models import load_model
+from tensorflow.keras.layers import TextVectorization
+
+# Load the saved vectorizer
+with open("/content/drive/MyDrive/Colab Notebooks/vectorizer.pkl", "rb") as f:
+    vectorizer = pickle.load(f)
+
+# Load the saved model
+model = load_model("/content/drive/MyDrive/Colab Notebooks/toxicity.h5")
+
+print("Model and vectorizer loaded successfully.")
+
+# Prediction function
+def predict_toxicity(text):
+    text_vectorized = vectorizer([text])
+    text_vectorized = np.array(text_vectorized)
+    text_vectorized = np.reshape(text_vectorized, (1, -1))
+    prediction = model.predict(text_vectorized)[0]
+    return {"Toxic": float(prediction[0]),
+            "Severe Toxic": float(prediction[1]),
+            "Obscene": float(prediction[2]),
+            "Threat": float(prediction[3]),
+            "Insult": float(prediction[4]),
+            "Identity Hate": float(prediction[5])}
+
+# Gradio Interface
+demo = gr.Interface(fn=predict_toxicity,
+                    inputs=gr.Textbox(placeholder="Enter a comment..."),
+                    outputs=gr.Label(),
+                    title="Toxic Comment Classifier",
+                    description="Enter a comment and see its toxicity levels.")
+
+# Launch the app
+demo.launch()