Update app.py

app.py

@@ -3,43 +3,74 @@ import requests
 import pandas as pd
 from scipy import stats
 
-def fetch_data_to_dataframe(query, limit=50):
-    BASE_URL = "https://api.mercadolibre.com/sites/MLB/search"
-    params = {'q': query, 'limit': limit}
+def fetch_amazon_data(asin, token):
+    BASE_URL = "https://api.invertexto.com/v1/amazon/{}".format(asin)
+    params = {'token': token}
     response = requests.get(BASE_URL, params=params)
     data = response.json()
-    
-    if 'results' in data:
-        items = data['results']
+    # Assuming the response structure, adjust as necessary
+    if data:
+        df = pd.DataFrame([{
+            'Title': data.get('title'),
+            'Price': data.get('price'),
+            'Currency': 'USD',  # Assuming currency based on Amazon, adjust if needed
+            'Condition': 'new',  # Assuming new, adjust as needed
+            'Link': data.get('url')
+        }])
+        return df
+    else:
+        return pd.DataFrame()
+
+def fetch_fipe_data(brand_id, token):
+    BASE_URL = "https://api.invertexto.com/v1/fipe/models/{}".format(brand_id)
+    params = {'token': token}
+    response = requests.get(BASE_URL, params=params)
+    data = response.json()
+    # Assuming the response structure, adjust as necessary
+    if data:
+        items = data  # Assuming data is a list of models
         df = pd.DataFrame(items)
-        df = df[['title', 'price', 'currency_id', 'condition', 'permalink']]
-        df.columns = ['Title', 'Price', 'Currency', 'Condition', 'Link']
-        
-        # Calculate z-scores of `df['Price']`
-        df['z_score'] = stats.zscore(df['Price'])
-        # Filter out rows where z-score is greater than 2
-        df_filtered = df[df['z_score'] <= 2]
-        
-        # Further filter df_filtered to keep titles closely matching the query
-        # Split the query into keywords and check if each title contains them
-        keywords = query.lower().split()
-        # Assuming all keywords in the query must be present in the title for it to be considered relevant
-        for keyword in keywords:
-            df_filtered = df_filtered[df_filtered['Title'].str.lower().str.contains(keyword)]
-        
-        # Exclude results containing the word "kit"
-        df_filtered = df_filtered[~df_filtered['Title'].str.lower().str.contains("kit")]
-        
-        df_filtered = df_filtered.drop(columns=['z_score'])
-        
-        median_price = df_filtered['Price'].median()
-        return median_price, df_filtered
+        # Assuming the data structure, adjust the DataFrame creation as necessary
+        return df
     else:
-        return 0, pd.DataFrame()
+        return pd.DataFrame()
+
+def fetch_data_to_dataframe(query, limit=50, source="mercadolibre", token=None):
+    if source == "mercadolibre":
+        BASE_URL = "https://api.mercadolibre.com/sites/MLB/search"
+        params = {'q': query, 'limit': limit}
+        response = requests.get(BASE_URL, params=params)
+        data = response.json()
+        if 'results' in data:
+            items = data['results']
+            df = pd.DataFrame(items)
+            df = df[['title', 'price', 'currency_id', 'condition', 'permalink']]
+            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 gradio_app(query):
-    median_price, df = fetch_data_to_dataframe(query, 50)
-    return median_price, df
+    # Example: Fetch data from MercadoLibre, adjust to include other sources as needed
+    df = fetch_data_to_dataframe(query, 50, source="mercadolibre")
+    if df.empty:
+        return "No data found", pd.DataFrame()
+    else:
+        median_price = df['Price'].median()
+        return median_price, df
 
 iface = gr.Interface(fn=gradio_app,
                      inputs=gr.Textbox(label="Insira a consulta de pesquisa"),