Update app.py

app.py

@@ -10,6 +10,13 @@ EMBEDDING_MODEL = "text-embedding-ada-002"
 
 openai.api_key = os.getenv("OPENAI_API_KEY")
 
+
+start_sequence = "\nAI:"
+restart_sequence = "\nHuman: "
+
+
+
+
 # 1) Preprocess the document library
 df = pd.read_csv("informacion_neo_tokenizado.csv")
 df = df.set_index(["title", "heading"])
@@ -84,8 +91,9 @@ def construct_prompt(question: str, context_embeddings: dict, df: pd.DataFrame)
             
         chosen_sections.append(SEPARATOR + document_section.content.replace("\n", " "))
         chosen_sections_indexes.append(str(section_index))
-    
-    header = """Answer the question as truthfully as possible using the provided context, and if the answer is not contained within the text below, say "I don't know."\n\nContext:\n"""
+
+    header = """Responde la pregunta con la mayor sinceridad posible utilizando primero el contexto proporcionado"\n\nContexto:\n"""
+    #header = """Answer the question as truthfully as possible using the provided context, and if the answer is not contained within the text below, say "I don't know."\n\nContext:\n"""
     
     return header + "".join(chosen_sections) + "\n\n Q: " + question + "\n A:"
 
@@ -125,5 +133,15 @@ def answer_query_with_context(
 def answer_question(query):
     return answer_query_with_context(query, df, document_embeddings)
 
+
+def chatgpt_clone(input, history):
+    history = history or []
+    s = list(sum(history, ()))
+    s.append(input)
+    inp = ' '.join(s)
+    output = openai_create(inp)
+    history.append((input, output))
+    return history, history
+
 iface = gr.Interface(fn=answer_question, inputs="text", outputs="text")
 iface.launch()