Update app.py

app.py

@@ -6,10 +6,21 @@ model_name = "mohamedemam/QA_GeneraTor"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
 
+# Recommended words for users to choose from
+recommended_words = [
+    "which", "how", "when", "where", "who", "whom", "whose", "why",
+    "which", "who", "whom", "whose", "whereas",
+    "can", "could", "may", "might", "will", "would", "shall", "should",
+    "do", "does", "did", "is", "are", "am", "was", "were", "be", "being", "been",
+    "have", "has", "had", "if", "is", "are", "am", "was", "were", "do", "does", "did", "can", "could",
+    "will", "would", "shall", "should", "might", "may", "must",
+    "may", "might", "must",
+]
+
 # Function to generate questions and answers with configurable parameters
-def generate_qa(context, temperature, top_p):
-    input_text = f"what: {context}"
-    input_ids = tokenizer(input_text,max_length=400,truncation=True,padding="max_length",return_tensors='pt')
+def generate_qa(context, recommended_word, temperature, top_p):
+    input_text = f"{recommended_word}: {context}"
+    input_ids = tokenizer(input_text, max_length=400, truncation=True, padding="max_length", return_tensors='pt')
     
     # Generate with configurable parameters
     output = model.generate(
@@ -24,11 +35,15 @@ def generate_qa(context, temperature, top_p):
 # Create the Gradio interface with sliders for temperature and top-p
 iface = gr.Interface(
     fn=generate_qa,
-    inputs=["text", gr.inputs.Slider(minimum=0.2, maximum=2, default=1, step=0.1, label="Temperature"),
-            gr.inputs.Slider(minimum=0.1, maximum=1, default=0.8, step=0.1, label="Top-p")],
+    inputs=[
+        "text",
+        gr.inputs.Dropdown(recommended_words, label="Choose a Recommended Word"),
+        gr.inputs.Slider(minimum=0, maximum=4, default=2.2, step=0.5, label="Temperature"),
+        gr.inputs.Slider(minimum=0.1, maximum=1, default=0.2, step=0.5, label="Top-p")
+    ],
     outputs="text",
     title="Question Generation and Answering",
-    description="Enter a context, adjust temperature and top-p, and the model will generate a question and answer.",
+    description="Enter a context, choose a recommended word, and adjust temperature and top-p. The model will generate a question and answer.",
 )
 
 # Launch the interface