Update app.py

app.py

@@ -30,7 +30,7 @@ model = genai.GenerativeModel(model_name="gemini-pro",
 
 def gemini(query):
     prompt_parts = [
-        f"input: \"Procurar {query} em df['Title'] que sejam mais relevantes\"",
+        f"input: \"Procurar e selecionar {query} em df['Title'] que, numa análise sintática, sejam as mais próximas possíveis\"",
         "output: ",
         f"input: {query}",
         "output: ",
@@ -70,17 +70,18 @@ def fetch_data_to_dataframe(query, limit=50, source="mercadolibre", token=None):
 
 def integrated_app(query):
     # Interpret the prompt using the Gemini model
-    interpreted_response = gemini(query)  # You'll need to adjust the gemini function to return a usable query term
+    interpreted_response = gemini(query)  
+
+    refined_query = extract_refined_query(interpreted_response) 
     
-    # Fetch data from Mercado Livre based on the interpreted query
-    df = fetch_data_to_dataframe(interpreted_response, 50, source="mercadolibre")
+    # Fetch data from Mercado Livre based on the refined query
+    df = fetch_data_to_dataframe(refined_query, 50, source="mercadolibre")
     
     if df.empty:
         return "No data found", pd.DataFrame()
     else:
         # Process the fetched data
         median_price = df['Price'].median()
-        # You could add more processing here based on the Gemini response
         
         # Return the processed data
         return median_price, df