Add application file

app.py

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+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# ✅ Load your model
+model_name = "microsoft/DialoGPT-medium"  # Replace with your model if different
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# ✅ Keyword filter (from your notebook)
+allowed_keywords = [
+    "عجوة", "Ajwa", "جالكسي", "Galaxy", "مجدول", "Medjool", "منيفي",
+    "Munifi", "سكري", "Sukkari", "تمر", "تمرية", "نخيل", "نخلة", "مزرعة", "بلح", "ماء", "ري", "نخل", "dates", "date"
+]
+
+# ✅ Chat function with filtering
+def chatbot_fn(message, history=[]):
+    # Keyword check
+    if not any(keyword.lower() in message.lower() for keyword in allowed_keywords):
+        response = "أنا أجيب فقط على الأسئلة المتعلقة بالنخيل والتمور، هل يمكنك تحديد سؤالك أكثر؟"
+        history.append((message, response))
+        return history, history
+
+    # Encode and generate
+    inputs = tokenizer.encode(message + tokenizer.eos_token, return_tensors="pt")
+    outputs = model.generate(inputs, max_length=500, pad_token_id=tokenizer.eos_token_id)
+    response = tokenizer.decode(outputs[:, inputs.shape[-1]:][0], skip_special_tokens=True)
+
+    history.append((message, response))
+    return history, history
+
+# ✅ Gradio UI
+demo = gr.ChatInterface(fn=chatbot_fn, title="Lina Chatbot")
+
+demo.launch()