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app.py

@@ -94,7 +94,7 @@ col1, col2 = st.columns([3, 3], gap="medium")
 with col1:
     query_text = st.text_area(
         "Input Query",
-        value="What was discussed regarding Wearables revenue performance in Q1 2020?",
+        value="How has the growth been for AMD in the PC market in 2020?",
     )
 
 # Extracting Document Entities from Question
@@ -209,8 +209,12 @@ if decoder_model == "GPT-3.5 Turbo":
                 generated_text = gpt_turbo_model(edited_prompt)
 
                 st.subheader("Answer:")
-                regex_pattern_sentences = "(?<!\w\.\w.)(?<![A-Z][a-z]\.)(?<=\.|\?)\s"
-                generated_text_list = re.split(regex_pattern_sentences, generated_text)
+                regex_pattern_sentences = (
+                    "(?<!\w\.\w.)(?<![A-Z][a-z]\.)(?<=\.|\?)\s"
+                )
+                generated_text_list = re.split(
+                    regex_pattern_sentences, generated_text
+                )
                 for answer_text in generated_text_list:
                     answer_text = f"""{answer_text}"""
                     st.write(
@@ -234,7 +238,6 @@ if decoder_model == "Vicuna-7B":
             )
 
 
-
 tab1, tab2 = st.tabs(["Retrieved Text", "Retrieved Documents"])
 
 

utils/entity_extraction.py

@@ -79,7 +79,7 @@ def year_quarter_range(start_quarter, start_year, end_quarter, end_year):
                 year_quarter_range_list.append(
                     (quarters[quarter_index], str(year))
                 )
-
+        print(year_quarter_range_list)
         return year_quarter_range_list