Update app.py

app.py

@@ -5,13 +5,19 @@ import streamlit as st
 #def main():
 st.title("Scientific Question Generation")
 
-API_URL = "https://api-inference.huggingface.co/models/dhmeltzer/bart-large_askscience-qg"
-headers = {"Authorization": "Bearer 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"}
+checkpoints = ['dhmeltzer/bart-large_askscience-qg',
+          'dhmeltzer/flan-t5-base_askscience-qg',
+          'google/flan-t5-xxl']
 
-def query(payload):
+headers = {"Authorization": f"Bearer {st.secrets['HF_token']}"}
+
+def query(checkpoint, payload):
+    API_URL = f"https://api-inference.huggingface.co/models/{checkpoint}}"
+    
     response = requests.post(API_URL, 
                                 headers=headers, 
                                 json=payload)
+    
     return response.json()
 
 # User search
@@ -22,18 +28,17 @@ user_input = st.text_area("Question Generator",
 st.sidebar.markdown("**Filters**")
 
 temperature = st.sidebar.slider("Temperature", 0.0, 1.0, 0.0,.1)
-num_results = st.sidebar.slider("Number of search results", 1, 50, 1)
-
 vector = query([user_input])
 
 if user_input:
-
-    output = query({
-        "inputs": user_input,
-        "temperature":temperature,
-        "wait_for_model":True})
-
-    st.write(output)
+    for checkpoint in checkpoints:
+        output = query(checkpoint,{
+            "inputs": user_input,
+            "temperature":temperature,
+            "wait_for_model":True})[0][0]['generated_text']
+
+        model_name = checkpoints.split('/')[1]
+        st.write(f'Model {model_name}: output')
 
 
 #if __name__ == "__main__":