Update app.py

app.py

@@ -10,59 +10,52 @@ data = pd.read_csv("RR.csv")
 # Set your OpenAI API key
 openai.api_key = "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"
 
-# Create four graphs using plotly express
 graph1 = px.line(data, x="Month", y="Sales Forecasted")
-graph2 = px.line(data, x="Month", y="Sales Actual")
-graph3 = px.line(data, x="Month", y="Marketing Budget")
-graph4 = px.line(data, x="Month", y="Support Chats and 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"
+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")
 
 iface = gr.Interface(
     fn=get_insights,
     inputs=[
         gr.inputs.Textbox(lines=2, label="Enter your question"),
     ],
-    outputs=[
-        gr.outputs.Plot(plot=graph1),
-        gr.outputs.Plot(plot=graph2),
-        gr.outputs.Plot(plot=graph3),
-        gr.outputs.Plot(plot=graph4),
-    ],
+    outputs="text",
     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",
-    article="",
+    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
 )
 
 iface.launch(inbrowser=True)