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| from huggingface_hub import InferenceClient | |
| model_name = "Qwen/Qwen2.5-72B-Instruct" | |
| client = InferenceClient(model_name) | |
| def llm_inference(user_sample): | |
| output = client.chat.completions.create( | |
| messages=[ | |
| {"role": "system", "content": "You are an expert in english texts evaluation.\n" | |
| "Evaluate the text (find an answer) focusing on metrics. Based your answer on the reasoning below\n" | |
| "Answers: A1, A2, B1, B2, C1, C2\n" | |
| f"Text: {user_sample}\n" | |
| "Metrics:\n" | |
| "1. The number of sentences in the text\n" | |
| "2. The number of words in the text\n" | |
| "3. The number of long words (3 syllables and more)\n" | |
| "4. The number of syllables in the text\n" | |
| "5. FRE\n" | |
| "6. FKGL\n" | |
| "7. LIX\n" | |
| "8. SMOG\n" | |
| "Reasoning: Use FRE, FKGL, LIX and SMOG interpretations to choose an answer\n" | |
| "Write an answer using the format: This text has <answer> level.\n" | |
| "Explain in few words (10-15 words) why did you choose this level based on FRE, FKGL, LIX and SMOG interpretations\n" | |
| "If it is a text on another language, say that you could not evaluate it"}], | |
| stream=False, | |
| max_tokens=650, | |
| temperature=0.5 | |
| ) | |
| return output.choices[0].get('message')['content'] | |
| import gradio as gr | |
| interface = gr.Interface(fn=llm_inference, | |
| inputs=gr.Textbox(lines=2, placeholder="Write your text here..."), | |
| outputs="text", | |
| css=".gradio-container {background-image: url('https://i.pinimg.com/originals/9b/6a/a8/9b6aa8867dbe29f2d475b7a550e06490.jpg')}", | |
| title="ENGLISH TEXT EVALUATION") | |
| interface.launch(debug=True) | |