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from flask import Flask, send_file, request, jsonify |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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import torch |
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import gradio as gr |
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app = Flask(__name__) |
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model = None |
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tokenizer = None |
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def load_model(): |
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global model, tokenizer |
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if model is None: |
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print("جاري تحميل النموذج...") |
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tokenizer = AutoTokenizer.from_pretrained("amd/AMD-OLMo-1B") |
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model = AutoModelForCausalLM.from_pretrained( |
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"amd/AMD-OLMo-1B", |
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torch_dtype=torch.float16, |
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device_map="auto" |
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) |
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print("تم تحميل النموذج بنجاح!") |
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def generate_response(prompt): |
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"""Generate response from the model""" |
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global model, tokenizer |
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try: |
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if model is None: |
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load_model() |
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device) |
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with torch.no_grad(): |
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outputs = model.generate( |
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**inputs, |
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max_length=200, |
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num_return_sequences=1, |
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temperature=0.7, |
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top_p=0.9, |
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repetition_penalty=1.2, |
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pad_token_id=tokenizer.eos_token_id |
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) |
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response = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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return response.replace(prompt, "").strip() |
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except Exception as e: |
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print(f"خطأ في توليد الرد: {str(e)}") |
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return "عذراً، حدث خطأ في معالجة رسالتك." |
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@app.route('/') |
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def home(): |
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return send_file('index.html') |
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@app.route('/api/chat', methods=['POST']) |
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def chat(): |
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try: |
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data = request.json |
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if not data: |
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return jsonify({"response": "لم يتم استلام أي بيانات"}), 400 |
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user_message = data.get('message', '') |
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if not user_message: |
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return jsonify({"response": "الرسالة فارغة"}), 400 |
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print(f"رسالة مستلمة: {user_message}") |
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response = generate_response(user_message) |
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print(f"الرد: {response}") |
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return jsonify({"response": response}) |
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except Exception as e: |
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print(f"خطأ في معالجة الرسالة: {str(e)}") |
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return jsonify({"response": "عذراً، حدث خطأ في معالجة رسالتك"}), 500 |
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if __name__ == "__main__": |
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app.run() |
