Update app.py

app.py

@@ -3,37 +3,42 @@ import requests
 import pandas as pd
 from scipy import stats
 
-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()
-    # 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()
+import google.generativeai as genai
+
+# Proper configuration with your API key
+genai.configure(api_key="AIzaSyCm57IpC9_TTL7U3m8wvje9_3qtfxAASgI")  # Replace YOUR_API_KEY with the actual API key
+
+# Set up the model configuration
+generation_config = {
+    "temperature": 0.7,
+    "top_p": 1,
+    "top_k": 1,
+    "max_output_tokens": 2048,}
+
+# Safety settings
+safety_settings = [
+    {"category": "HARM_CATEGORY_HARASSMENT", "threshold": "BLOCK_MEDIUM_AND_ABOVE"},
+    {"category": "HARM_CATEGORY_HATE_SPEECH", "threshold": "BLOCK_MEDIUM_AND_ABOVE"},
+    {"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT", "threshold": "BLOCK_MEDIUM_AND_ABOVE"},
+    {"category": "HARM_CATEGORY_DANGEROUS_CONTENT", "threshold": "BLOCK_MEDIUM_AND_ABOVE"},
+]
+
+# Initialize the model
+model = genai.GenerativeModel(model_name="gemini-pro",
+                              generation_config=generation_config,
+                              safety_settings=safety_settings)
+
+def gemini(query):
+    prompt_parts = [
+        f"input: \"Informar o preço médio de {query} em Reais brasileiros e explicar as características que podem representar variação nos preços\"",
+        "output: ",
+        f"input: {query}",
+        "output: ",
+    ]
+
+    response = model.generate_content(prompt_parts)
+    return response.text
 
-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)
-        # Assuming the data structure, adjust the DataFrame creation as necessary
-        return df
-    else:
-        return pd.DataFrame()
 
 def fetch_data_to_dataframe(query, limit=50, source="mercadolibre", token=None):
     if source == "mercadolibre":
@@ -63,19 +68,30 @@ def fetch_data_to_dataframe(query, limit=50, source="mercadolibre", token=None):
 
     return df
 
-def gradio_app(query):
-    # Example: Fetch data from MercadoLibre, adjust to include other sources as needed
-    df = fetch_data_to_dataframe(query, 50, source="mercadolibre")
+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
+    
+    # Fetch data from Mercado Livre based on the interpreted query
+    df = fetch_data_to_dataframe(interpreted_response, 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
 
-iface = gr.Interface(fn=gradio_app,
-                     inputs=gr.Textbox(label="Insira a consulta de pesquisa"),
-                     outputs=[gr.Textbox(label="Preço mediano"), gr.Dataframe(label="Resultados da pesquisa")],
-                     title="Bens Móveis",
-                     description="App para avaliação de bens móveis")
 
-iface.launch()
+# Define the updated Gradio interface
+iface = gr.Interface(fn=integrated_app,
+                     inputs=gr.Textbox(label="Digite sua consulta"),
+                     outputs=[gr.Textbox(label="Preço Mediano"), gr.Dataframe(label="Resultados da Pesquisa")],
+                     title="Análise Integrada de Bens",
+                     description="Esta aplicação combina a interpretação de prompts via modelo Gemini com a busca de dados no Mercado Livre para oferecer uma análise de preços e características de bens.")
+
+# Launch the interface
+iface.launch()