Update app.py

app.py

@@ -1,42 +1,37 @@
-from flask import Flask, request, jsonify
-from transformers import AutoModelForSeq2SeqLM, pipeline
-
-# Load the previously trained and saved model
-model = AutoModelForSeq2SeqLM.from_pretrained('product_model')
-
-# Load the translation models
-translator_ar_en = pipeline('translation_ar_to_en', model='Helsinki-NLP/opus-mt-ar-en')
-translator_en_ar = pipeline('translation_en_to_ar', model='Helsinki-NLP/opus-mt-en-ar')
-
-# Create the custom model pipeline
-custom_model = pipeline('text2text-generation', model='product_model')
-
-app = Flask(__name__)
-
-def translate(text, translator):
-    return translator(text)[0]['translation_text']  
-
-@app.route('/chat', methods=['POST'])
-def chat():
-    data = request.json
-    user_input = data.get('message')
-    
-    if not user_input:
-        return jsonify({'error': 'No input message provided'}), 400
-    
-    try:
-        # Translate user input from Arabic to English
-        translated_input = translate(user_input, translator_ar_en)
-        
-        # Generate prediction
-        prediction = custom_model(translated_input)[0]['generated_text']
-        
-        # Translate response from English to Arabic
-        translated_response = translate(prediction, translator_en_ar)
-        
-        return jsonify({'response': translated_response})
-    except Exception as e:
-        return jsonify({'error': str(e)}), 500
-
-if __name__ == '__main__':
-    app.run(host='0.0.0.0', port=5000)
+from transformers import AutoModelForSeq2SeqLM, pipeline
+
+# Load the previously trained and saved model
+model_path = r'C:\Users\muham\OneDrive\Desktop\certificates\product_model'
+model = AutoModelForSeq2SeqLM.from_pretrained(r'C:\Users\muham\OneDrive\Desktop\certificates\product_model')
+
+# Load the translation models
+translator_ar_en = pipeline('translation_ar_to_en', model='Helsinki-NLP/opus-mt-ar-en')
+translator_en_ar = pipeline('translation_en_to_ar', model='Helsinki-NLP/opus-mt-en-ar')
+# Create the custom model pipeline
+custom_model = pipeline('text2text-generation', model=r'C:\Users\muham\OneDrive\Desktop\certificates\product_model')
+
+def translate(text, translator):
+    return translator(text)[0]['translation_text']
+def chat_with_bot():
+    print("(:اهلا بحضرتك في تطبيق السمسار الالكتروني")
+    while True:
+        # Get user input
+        user_input = input("العميل: ")
+        if user_input.lower() == 'quit':
+            break
+        
+        # Translate user input from Arabic to English
+        try:
+            translated_input = translate(user_input, translator_ar_en)
+            
+            # Generate prediction
+            prediction = custom_model(translated_input)[0]['generated_text']         
+            # Translate response from English to Arabic
+            translated_response = translate(prediction, translator_en_ar)
+            print("السمسار:", translated_response)
+        except Exception as e:
+            print(f"An error occurred: {str(e)}")
+
+# Start chatting with the bot
+chat_with_bot()
+