Create app.py

app.py

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+import gradio as gr
+import openai
+import pandas as pd
+
+# Load your CSV file
+data = pd.read_csv("RR.csv")
+
+# Set your OpenAI API key
+openai.api_key = "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"
+
+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"Given the following dataset:\n\n{data.to_string(index=False)}\n\n{question}"
+    answer = query_gpt(prompt)
+    return answer
+
+
+markdown_data = f"Mock Dataset For GDS Social MBR :\n```\n{data.head(10).to_string(index=False)}\n```\n"
+
+iface = gr.Interface(
+    fn=get_insights,
+    inputs=[
+        gr.inputs.Textbox(lines=2, label="Enter your question"),
+    ],
+    outputs="text",
+    title="GPT-powered Q&A",
+    description=markdown_data,
+    examples=[
+        ("Are there any trends or seasonality in calls handled?",),
+        ("Are there any patterns that suggest a need for additional staff during specific periods?",),
+        ("Can you identify any months with a significantly higher or lower number of calls handled compared to the overall average?",),
+    ("Are there any patterns that suggest a need for additional staff during specific periods?",),
+    ],
+    allow_screenshot=False,
+    theme="compact",
+    layout="vertical",
+)
+
+iface.launch(inbrowser=True)