Update app.py

app.py

@@ -103,38 +103,39 @@ def load_recommender(paths, start_page=1):
     recommender.fit(chunks)
     return 'Corpus Loaded.'
 
-def generate_text(messages, engine="gpt-3.5-turbo"):
-    response = openai.ChatCompletion.create(
+def generate_text(prompt, engine='gpt-3.5-turbo', max_tokens=2050, temperature=0.8):
+     response = openai.ChatCompletion.create(
         model=engine,
-        messages=messages,
-        max_tokens=512,
-        temperature=0.7,
-    )
-    message = response['choices'][0]['message']['content']
-    return message
+        messages=[{"role": "system", "content": "You are a research assistant"},
+              {"role": "user", "content": prompt}],
+        max_tokens=max_tokens,
+        n=1,
+        temperature=temperature
+        )
+        return response.choices[0].message['content']
+    return None
 
 def generate_answer(question):
     topn_chunks = recommender(question)
     
-    system_message = {
-        'role': 'system',
-        'content': 'Compose a comprehensive reply to the query using the search results given. Only include information found in the results. If the text does not relate to the query, simply state "Text Not Found in Body of Knowledge".'
-    }
-    user_message = {
-        'role': 'user',
-        'content': 'Query: ' + question
-    }
+    messages = [
+        {
+            'role': 'system',
+            'content': 'You are a research assistant. Compose a comprehensive reply to the query using the search results given. Only include information found in the results. If the text does not relate to the query, simply state "Text Not Found in Body of Knowledge".'
+        },
+        {
+            'role': 'user',
+            'content': 'Query: ' + question
+        },
+    ]
     
     for c in topn_chunks:
-        search_result_message = {
+        messages.append({
             'role': 'assistant',
             'content': c
-        }
-        system_message['content'] += '\n\n' + search_result_message['content']
+        })
     
-    messages = [system_message, user_message]
-    
-    answer = generate_text(messages, "gpt-3.5-turbo")
+    answer = generate_text(messages)
     return answer
 
 def question_answer(urls, file, question):