Update app.py

app.py

@@ -13,49 +13,56 @@ openai.api_key = "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"
 graph1 = px.line(data, x="Month", y="Sales Forecasted")
 graph2 = px.line(data, x="Month", y="Marketing Budget")
 graph3 = px.line(data, x="Month", y="Sales Actual")
-graph4a = px.line(data, x="Month", y="Support Chats")
-graph4b = px.line(data, x="Month", y="Support Calls")
+graph4 = px.line(data, x="Month", y=["Support Chats", "Support Calls"])
+
+def query_gpt(prompt):
+    model_engine = "text-davinci-002"  # Use any available GPT model here
+    max_tokens = 50
+
+    response = openai.Completion.create(
+        engine=model_engine,
+        prompt=prompt,
+        max_tokens=max_tokens,
+        n=1,
+        stop=None,
+        temperature=0.7,
+    )
+
+    message = response.choices[0].text.strip()
+    return message
+
+
+def get_insights(question):
+    prompt = f"Please answer the question: {question}\n\nRemember, base your answer solely on the data provided in the dataset."
+    answer = query_gpt(prompt)
+    return answer
+
+
+markdown_data = "Mock Dataset Sales & Marketing Budget"
 
 iface = gr.Interface(
     fn=get_insights,
     inputs=[
         gr.inputs.Textbox(lines=2, label="Enter your question"),
     ],
-    outputs="text",
+    outputs=[
+        gr.outputs.Plot(plot=graph1),
+        gr.outputs.Plot(plot=graph2),
+        gr.outputs.Plot(plot=graph3),
+        gr.outputs.Plot(plot=graph4),
+    ],
     title="GPT-powered Q&A",
     description=markdown_data,
     examples=[
-        ("Are there any trends in sales forecast or actual sales?",),
-        ("How does the marketing budget correlate with the sales actual?",),
-        ("Can you identify any months with a significantly higher or lower number of support chats or calls compared to the overall average?",),
-        ("Are there any patterns that suggest a need for additional staff during specific periods based on support chats and calls?",),
+        "Are there any trends in sales forecast or actual sales?",
+        "How does the marketing budget correlate with the sales actual?",
+        "Can you identify any months with a significantly higher or lower number of support chats or calls compared to the overall average?",
+        "Are there any patterns that suggest a need for additional staff during specific periods based on support chats and calls?",
     ],
     allow_screenshot=False,
     theme="compact",
     layout="vertical",
-    outputs=[
-        gr.outputs.Graphic(type="plotly"),
-        gr.outputs.Graphic(type="plotly"),
-        gr.outputs.Graphic(type="plotly"),
-        gr.outputs.Graphic(type="plotly"),
-        gr.outputs.Graphic(type="plotly"),
-    ],
-)
-
-iface.configure_output(
-    0, label="Sales Forecasted", type="plotly", data=graph1
-)
-iface.configure_output(
-    1, label="Marketing Budget", type="plotly", data=graph2
-)
-iface.configure_output(
-    2, label="Sales Actual", type="plotly", data=graph3
-)
-iface.configure_output(
-    3, label="Support Chats", type="plotly", data=graph4a
-)
-iface.configure_output(
-    4, label="Support Calls", type="plotly", data=graph4b
+    article="",
 )
 
 iface.launch(inbrowser=True)