Create app.py

app.py

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+import gradio as gr
+import torch
+from torch.nn.functional import softmax
+import shap
+import requests
+from transformers import RobertaTokenizer, pipeline, RobertaModel
+model_dir = 'temp'
+tokenizer = RobertaTokenizer.from_pretrained(model_dir)
+model = RobertaModel.from_pretrained(model_dir)
+
+@app.route('/gradio_app')
+def gradio_app():
+    def process_text(input_text, input_file):
+        if input_text:
+            text = input_text
+        elif input_file is not None:
+            text = input_file.read().decode('utf-8')
+        inputs = tokenizer(text, return_tensors="pt")
+        with torch.no_grad():
+            logits = model(**inputs).logits
+        probs = softmax(logits, dim=1)
+        max_prob, predicted_class_id = torch.max(probs, dim=1)
+        prob = str(round(max_prob.item() * 100, 2))
+        label = model.config.id2label[predicted_class_id.item()]
+        final_label='Human' if model.config.id2label[predicted_class_id.item()]=='LABEL_0' else 'Chat-GPT'
+        processed_result = text
+
+        def search(text):
+            query = text
+            api_key = 'AIzaSyClvkiiJTZrCJ8BLqUY9I38WYmbve8g-c8'
+            search_engine_id = '53d064810efa44ce7'
+            url = f'https://www.googleapis.com/customsearch/v1?key={api_key}&cx={search_engine_id}&q={query}'
+
+            try:
+                response = requests.get(url)
+                data = response.json()
+                return data
+            except Exception as e:
+                return {'error': str(e)}
+
+        def find_plagiarism(text):
+            search_results = search(text)
+            if 'items' not in search_results:
+                return []
+            similar_articles = []
+            for item in search_results['items']:
+                title = item.get('title', '')
+                link = item.get('link', '')
+                similar_articles.append({'title': title, 'link': link})
+            return similar_articles[:5]
+
+        pipe = pipeline('text-classification', model=model, tokenizer=tokenizer)
+        prediction = pipe([text])
+        explainer = shap.Explainer(pipe)
+        shap_values = explainer([text])
+        text_plot = shap.plots.text(shap_values, display=True)
+        similar_articles = find_plagiarism(text)
+
+        return processed_result, prob, final_label, text_plot,similar_articles
+
+    text_input = gr.inputs.Textbox(label="Enter text")
+    file_input = gr.inputs.File(label="Upload a text file")
+    outputs = [gr.Textbox(label="Processed text"), gr.Textbox(label="Probability"), gr.Textbox(label="Label"), gr.HTML(label="SHAP Plot"),gr.Table(label="Similar Articles", columns=["Title", "Link"])]
+
+    gr.Interface(fn=process_text, inputs=[text_input, file_input], outputs=outputs).launch()
+
+    return ''
+
+if __name__ == '__main__':
+    app.run(debug=True)