Create app.py

app.py

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+import openai
+import gradio as gr
+import os
+# Set your OpenAI API key here
+
+
+openai.api_key = os.environ.get("openai_api_key")
+
+# Define a function to generate responses using GPT-3.5 Turbo
+def generate_response(user_prompt):
+
+    # Define the system message
+    system_msg = 'You are a helpful assistant.'
+
+# Define the user message
+    prompt= f'''I will give you a question and you detect which category does this question belong to. It should be from these categories - 
+        physical activity, sleep, nutrition and preventive care. Make sure you just reply with response in json format "category":"[sleep,nutrition]". 
+        Note that single question may belong to multiple categories. Dont add any opening lines just reply with json response. If there is no match return no category
+        Question: {user_prompt}'''
+    #user_msg = 'Create a small dataset about total sales over the last year. The format of the dataset should be a data frame with 12 rows and 2 columns. The columns should be called "month" and "total_sales_usd". The "month" column should contain the shortened forms of month names from "Jan" to "Dec". The "total_sales_usd" column should contain random numeric values taken from a normal distribution with mean 100000 and standard deviation 5000. Provide Python code to generate the dataset, then provide the output in the format of a markdown table.'
+
+# Create a dataset using GPT
+   
+    response = openai.ChatCompletion.create(
+        model="gpt-3.5-turbo",  # Use GPT-3.5 Turbo engine,
+        messages=[{"role": "system", "content": system_msg},
+                                         {"role": "user", "content": prompt}],
+        max_tokens=100,  # You can adjust this to limit the response length
+    )
+    return response["choices"][0]["message"]["content"]
+
+# Create a Gradio interface
+
+iface = gr.Interface(fn=generate_response,
+                     inputs=[gr.components.Textbox( label="prompt",
+                                                   value='Who is the target population for Abdominal Aortic Aneurysm (AAA) screening?')],
+                     outputs=[gr.JSON(label="category")]
+                    )
+
+# Start the Gradio interface
+iface.launch()