Update app.py

app.py

@@ -38,7 +38,34 @@ def gemini(query):
 
     response = model.generate_content(prompt_parts)
     return response.text
-
+    
+def extract_refined_query(gemini_output):
+    """
+    Parses the Gemini model's output to extract a refined query for Mercado Livre.
+    
+    Args:
+    - gemini_output (str): The output text from the Gemini model.
+    
+    Returns:
+    - str: A refined query term extracted from the Gemini output.
+    """
+    # Placeholder for parsing logic. This should be replaced with actual parsing
+    # based on the structure of the Gemini model's output.
+    # For demonstration, let's assume the model's output is a descriptive text
+    # that includes keywords or phrases that can be used for a more focused search.
+    
+    # Example parsing logic (simple and needs to be adapted):
+    # Assuming the output might contain phrases like "focus on [keyword] for best results"
+    # This is a very basic example and should be replaced with actual parsing logic.
+    refined_query = "default query"  # Fallback query if parsing fails or finds no specific keywords
+    
+    # Example: Search for a phrase indicating a refined query within the model's output
+    if "focus on" in gemini_output:
+        start_index = gemini_output.find("focus on") + len("focus on ")
+        end_index = gemini_output.find(" for best results", start_index)
+        refined_query = gemini_output[start_index:end_index] if end_index != -1 else gemini_output[start_index:start_index+20]  # Extract the keyword or use a substring as a fallback
+    
+    return refined_query
 
 def fetch_data_to_dataframe(query, limit=50, source="mercadolibre", token=None):
     if source == "mercadolibre":
@@ -53,30 +80,17 @@ def fetch_data_to_dataframe(query, limit=50, source="mercadolibre", token=None):
             df.columns = ['Title', 'Price', 'Currency', 'Condition', 'Link']
         else:
             df = pd.DataFrame()
-    elif source == "amazon":
-        df = fetch_amazon_data(query, token)
-    elif source == "fipe":
-        df = fetch_fipe_data(query, token)
     else:
         df = pd.DataFrame()
-
-    # Process the DataFrame similarly for all sources if applicable
-    # This is an example for MercadoLibre data; adjust processing as needed for other sources
     if not df.empty:
-        # Additional processing here, like calculating z-scores, filtering, etc.
         pass
 
     return df
 
 def integrated_app(query):
-    # Interpret the prompt using the Gemini model
-    interpreted_response = gemini(query)  
-
-    refined_query = extract_refined_query(interpreted_response) 
-    
-    # Fetch data from Mercado Livre based on the refined query
+    gemini_output = gemini(query)  
+    refined_query = extract_refined_query(gemini_output)
     df = fetch_data_to_dataframe(refined_query, 50, source="mercadolibre")
-    
     if df.empty:
         return "No data found", pd.DataFrame()
     else: