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README.md

@@ -1,12 +1,49 @@
 ---
 title: Toxic Speech Classifier
-emoji: 💻
-colorFrom: indigo
-colorTo: pink
+emoji: 🤖
+colorFrom: red
+colorTo: yellow
 sdk: gradio
-sdk_version: 6.6.0
+sdk_version: 4.0.0
 app_file: app.py
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Toxic Speech Classifier 🤖
+
+A fine-tuned Gemma3-1B model for detecting and classifying toxic, insulting, or harmful language in text.
+
+## Description
+
+This demo uses a fine-tuned language model to analyze text and determine whether it contains toxic or harmful content. The model returns structured information including toxicity label, tags, severity, and reasoning.
+
+## Usage
+
+Simply enter any text, and the model will classify whether it is toxic or non-toxic, along with detailed structured output.
+
+## Examples
+
+- "You are absolutely worthless and no one will ever love you."
+- "Shut up you brainless moron, nobody asked for your stupid opinion."
+- "The weather today is really nice, I enjoyed my walk in the park."
+- "Thank you for your help, I really appreciate everything you did."
+
+## Model
+
+- **Base Model**: Gemma3-1B
+- **Fine-tuned on**: Insult and toxic speech classification data
+- **Model ID**: berkeruveyik/toxic-speech-finetune-with-gemma-3-1b-v1
+
+## Output Fields
+
+| Field | Description |
+|-------|-------------|
+| `is_toxic` | Whether the text is toxic (true/false) |
+| `label` | Classification label (e.g., insult, threat, neutral) |
+| `tags` | Relevant tags describing the type of toxicity |
+| `reason` | Explanation for the classification |
+| `severity` | Severity level of the toxic content |
+
+## License
+
+Please check the model license on the Hugging Face model page.

app.py

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+import gradio as gr
+import json
+import time
+import spaces
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+
+MODEL_PATH = 'berkeruveyik/toxic-speech-finetune-with-gemma-3-1b-v1'
+
+# Load model and tokenizer
+loaded_model = AutoModelForCausalLM.from_pretrained(
+    MODEL_PATH,
+    torch_dtype='auto',
+    device_map='auto',
+    attn_implementation='eager'
+)
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
+
+loaded_model_pipeline = pipeline(
+    'text-generation',
+    model=loaded_model,
+    tokenizer=tokenizer
+)
+
+@spaces.GPU
+def pred_on_text(input_text):
+    """Generate prediction from input text"""
+    start_time = time.time()
+
+    raw_output = loaded_model_pipeline(
+        text_inputs=[{'role': 'user', 'content': input_text}], 
+        max_new_tokens=256
+    )
+
+    end_time = time.time()
+    total_time = round(end_time - start_time, 4)
+
+    generated_text = raw_output[0]['generated_text'][1]['content']
+
+    return generated_text, raw_output, total_time
+
+def parse_generated_text(text):
+    """Parse the generated text and format it nicely"""
+    try:
+        data = json.loads(text)
+        return data
+    except:
+        try:
+            text = text.strip()
+            if text.startswith('{') and text.endswith('}'):
+                data = eval(text)
+                return data
+        except:
+            pass
+    return {"raw_output": text}
+
+def format_output(input_text, parsed_output, total_time):
+    """Format output as readable text with each field on new line"""
+    output_lines = []
+    output_lines.append(f"📝 Input: {input_text}")
+    output_lines.append("")
+    output_lines.append("━" * 50)
+    output_lines.append("")
+
+    if "is_toxic" in parsed_output:
+        emoji = "🚨" if parsed_output["is_toxic"] else "✅"
+        output_lines.append(f"{emoji} is_toxic: {parsed_output['is_toxic']}")
+
+    if "label" in parsed_output:
+        output_lines.append(f"🏷️ label: {parsed_output['label']}")
+
+    if "tags" in parsed_output:
+        output_lines.append(f"🔖 tags: {parsed_output['tags']}")
+
+    if "reason" in parsed_output:
+        output_lines.append(f"💬 reason: {parsed_output['reason']}")
+
+    if "severity" in parsed_output:
+        output_lines.append(f"⚡ severity: {parsed_output['severity']}")
+
+    if "raw_output" in parsed_output:
+        output_lines.append(f"📄 raw_output: {parsed_output['raw_output']}")
+
+    output_lines.append("")
+    output_lines.append("━" * 50)
+    output_lines.append(f"⏱️ processing_time: {total_time} seconds")
+
+    return "\n".join(output_lines)
+
+def gradio_predict(input_text):
+    """Wrapper function for Gradio"""
+    if not input_text.strip():
+        return "Please enter some text."
+
+    generated_text, raw_output, total_time = pred_on_text(input_text)
+    parsed_output = parse_generated_text(generated_text)
+
+    formatted_output = format_output(input_text, parsed_output, total_time)
+
+    return formatted_output
+
+# Gradio interface
+demo = gr.Interface(
+    fn=gradio_predict,
+    inputs=gr.Textbox(
+        label="Input Text",
+        placeholder="Enter your text here...",
+        lines=3
+    ),
+    outputs=gr.Textbox(
+        label="Model Output",
+        lines=12
+    ),
+    title="🤖 Toxic Speech Classifier",
+    description="Analyze whether a given text contains toxic, insulting, or harmful language using a fine-tuned Gemma3 model.",
+    examples=[
+        ["You are absolutely worthless and no one will ever love you."],
+        ["I hope you get hit by a bus, you disgusting excuse for a person."],
+        ["The weather today is really nice, I enjoyed my walk in the park."],
+        ["Shut up you brainless moron, nobody asked for your stupid opinion."],
+        ["Thank you for your help, I really appreciate everything you did."],
+        ["You are such a pathetic loser, get out of my sight."],
+        ["I just finished reading a great book, it was very inspiring."],
+    ],
+    theme=gr.themes.Soft()
+)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+transformers
+torch
+gradio
+accelerate