added webapp

app.py

@@ -0,0 +1,64 @@
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+  
+
+
+@st.cache(allow_output_mutation=True, max_entries=1)
+def get_model():
+    tokenizer = AutoTokenizer.from_pretrained("SoLID/sgd-response-generator")
+    model = AutoModelForSeq2SeqLM.from_pretrained("SoLID/sgd-response-generator")
+    return (model, tokenizer)
+
+
+def lexicalize_plan(
+    model, tokenizer, output_plan, temperature=1.0, num_beams=1
+):
+    
+    input_ids = tokenizer(output_plan, return_tensors="pt").input_ids
+    output = model.generate(
+        input_ids,
+        max_length=512,
+        do_sample=True,
+        top_p=0.95,
+        pad_token_id=tokenizer.pad_token_id,
+        eos_token_id=tokenizer.eos_token_id,
+        early_stopping=True,
+        temperature=temperature,
+        num_beams=int(num_beams),
+    )
+    output_str = tokenizer.decode(output[0], skip_special_tokens=True).strip()
+    return output_str
+
+def run():
+    st.set_page_config(page_title="Schema Guided Dialogue Response Generation")
+    # sidebar
+    st.sidebar.title("SGD Response Generator Demo")
+    st.sidebar.image(
+        "https://aeiljuispo.cloudimg.io/v7/https://s3.amazonaws.com/moonup/production/uploads/1628568174585-6049d8edbaa99e90d94ee67c.png",
+        caption="SoLID at UNCC Logo",
+    )
+    st.sidebar.markdown("### Controls:")
+    temperature = st.sidebar.slider(
+        "Temperature",
+        min_value=0.5,
+        max_value=1.5,
+        value=0.8,
+        step=0.1,
+    )
+    num_beams = st.sidebar.slider(
+        "Num beams",
+        min_value=1,
+        max_value=4,
+        step=1,
+    )
+    # main body
+    model, tokenizer = get_model()
+    output_plan = st.text_input("Output Plan: ", value = "[AC:Request [IN:FindRestaurants [SL:location] ] ] [AC:Request [IN:FindRestaurants [SL:category] ] ]", help ="Type in the output plan used by the system to generate a response in English.")
+    submit_button = st.button("Generate Response")
+    if submit_button:
+        text = st.text("Generating Response...")
+        response = lexicalize_plan (model, tokenizer, output_plan, temperature, num_beams)
+        text.empty()
+        st.text("Generated Response: " + str(response))
+
+if __name__ == "__main__":
+    run()

requirements.txt

@@ -0,0 +1,2 @@
+torch
+transformers