Update app.py

app.py

@@ -140,33 +140,30 @@ def get_prompt(docs, input_query):
   return(prompt)
 
 def run_query(input_text, country, model_sel):
-    # docs = get_docs(input_text, country)
+    # first call the retriever function using selected filters
     docs = get_docs(input_text, country=country,vulnerability_cat=vulnerabilities_cat)
-    # st.write('Selected country: ', country) # Debugging country
+    # model selector (not currently being used)
     if model_sel == "chatGPT":
-
+        # instantiate ChatCompletion as a generator object (stream is set to True)
         response = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=[{"role": "user", "content": get_prompt(docs, input_text)}], stream=True)
-        # iterate through the stream of events
-
+        # iterate through the streamed output
         report = []
         for chunk in response:
+            # extract the object containing the text (totally different structure when streaming)
             chunk_message = chunk['choices'][0]['delta']
-            # collected_chunks.append(chunk)  # save the event response
+            # test to make sure there is text in the object (some don't have)
             if 'content' in chunk_message:
                 report.append(chunk_message.content) # extract the message
+                # add the latest text and merge it with all previous
                 result = "".join(report).strip()
-                result = result.replace("\n", "")
-                # res_box.markdown(f'{result}')
-                res_box.success(result)
+                res_box.success(result) # output to response text box
 
-        output = result
-        references = get_refs(docs, output)
+        references = get_refs(docs, result) # extract references from the generated text
     # else:
-    #     res = client.text_generation(get_prompt_llama2(docs, query=input_query), max_new_tokens=4000, temperature=0.01, model=model)
+    #     res = client.text_generation(get_prompt(docs, query=input_query), max_new_tokens=4000, temperature=0.01, model=model)
     #     output = res
     #     references = get_refs(docs, res)
-    # st.write('Response')
-    # st.success(output)
+    
     st.markdown("----")
     st.markdown('**REFERENCES:**')
     st.markdown('References are based on text automatically extracted from climate policy documents. These extracts may contain non-legible characters or disjointed text as an artifact of the extraction procedure')