Upload 2 files

app.py

@@ -8,6 +8,7 @@ from utils import (
     get_sgpt_embedding_model,
     get_flan_t5_model,
     get_t5_model,
+    save_key,
 )
 
 from utils import (
@@ -16,8 +17,7 @@ from utils import (
     format_query,
     sentence_id_combine,
     text_lookup,
-    gpt3_qa,
-    gpt3_summary,
+    gpt3,
 )
 
 
@@ -62,7 +62,7 @@ encoder_model = st.selectbox("Select Encoder Model", encoder_models_choice)
 
 # Choose decoder model
 
-decoder_models_choice = ["FLAN-T5", "T5", "GPT3 (QA_davinci)", "GPT3 (summary_davinci)"]
+decoder_models_choice = ["FLAN-T5", "T5", "GPT3 - (text-davinci-003)"]
 
 decoder_model = st.selectbox("Select Decoder Model", decoder_models_choice)
 
@@ -120,25 +120,8 @@ if decoder_model == "GPT3 (summary_davinci)":
     )
     api_key = save_key(openai_key)
     openai.api_key = api_key
-    output_text = []
-    for context_text in context_list:
-        output_text.append(gpt3_summary(context_text))
-    generated_text = ". ".join(output_text)
-    st.write(gpt3_summary(generated_text))
-
-elif decoder_model == "GPT3 (QA_davinci)":
-    openai_key = st.text_input(
-        "Enter OpenAI key",
-        value=st.secrets["openai_key"],
-        type="password",
-    )
-    api_key = save_key(openai_key)
-    openai.api_key = api_key
-    output_text = []
-    for context_text in context_list:
-        output_text.append(gpt3_qa(query_text, context_text))
-    generated_text = ". ".join(output_text)
-    st.write(gpt3_qa(query_text, generated_text))
+    generated_text = gpt3(query_text, context_list)
+    st.write(gpt3(generated_text))
 
 elif decoder_model == "T5":
     t5_pipeline = get_t5_model()

utils.py

@@ -113,10 +113,15 @@ def text_lookup(data, sentence_ids):
     return context
 
 
-def gpt3_summary(text):
+def gpt3(query, result):
     response = openai.Completion.create(
         model="text-davinci-003",
-        prompt=text + "\n\nTl;dr",
+        prompt=f"""Context information is below. \n"
+    "---------------------\n"
+    "{result}"
+    "\n---------------------\n"
+    "Given the context information and prior knowledge, answer this question: {query}. \n"
+    "Try to include as many key details as possible and format the answer in points. \n" """,
         temperature=0.1,
         max_tokens=512,
         top_p=1.0,
@@ -126,20 +131,6 @@ def gpt3_summary(text):
     return response.choices[0].text
 
 
-def gpt3_qa(query, answer):
-    response = openai.Completion.create(
-        model="text-davinci-003",
-        prompt="Q: " + query + "\nA: " + answer,
-        temperature=0,
-        max_tokens=512,
-        top_p=1,
-        frequency_penalty=0.0,
-        presence_penalty=0.0,
-        stop=["\n"],
-    )
-    return response.choices[0].text
-
-
 # Transcript Retrieval