checking qa generator

app.py

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+import streamlit as st
+from transformers import AutoTokenizer, T5ForConditionalGeneration
+  
+model_name = "allenai/t5-small-squad2-question-generation"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+@st.cache
+def load_model(model_name):
+    model = T5ForConditionalGeneration.from_pretrained(model_name)
+    return model
+
+model = load_model(model_name)
+
+
+def run_model(input_string, **generator_args):
+    input_ids = tokenizer.encode(input_string, return_tensors="pt")
+    res = model.generate(input_ids, **generator_args)
+    output = tokenizer.batch_decode(res, skip_special_tokens=True)
+    # print(output)
+    return output
+
+
+default_value = "Nicejob has increased our revenue 80% since signing up"
+
+#prompts
+st.title("Question Generation")
+st.write("Placeholder for some other texts, like instructions...")
+
+sent = st.text_area("Text", default_value, height = 150)
+max_length = st.sidebar.slider("Max Length", min_value = 10, max_value=50)
+temperature = st.sidebar.slider("Temperature", value = 1.0, min_value = 0.0, max_value=1.0, step=0.05)
+num_return_sequences = st.sidebar.slider("Num Return Sequences", min_value = 1, max_value=4, value = 1)
+num_beams = st.sidebar.slider("Num Beams", min_value = 4, max_value=10, value = 4)
+top_k = st.sidebar.slider("Top-k", min_value = 0, max_value=100, value = 90)
+top_p = st.sidebar.slider("Top-p", min_value = 0.0, max_value=1.0, step = 0.05, value = 0.9)
+
+encoded_prompt = tokenizer.encode(sent, add_special_tokens=False, return_tensors="pt")
+if encoded_prompt.size()[-1] == 0:
+    input_ids = None
+else:
+    input_ids = encoded_prompt
+
+
+output_sequences = run_model(input_ids, max_length=max_length,num_return_sequences=num_return_sequences,
+                        num_beams=num_beams,
+                        temperature=temperature, top_k=top_k, top_p=top_p)
+
+st.write('\n'.join(output_sequences))

requirements.txt

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+transformers
+streamlit
+torch