Create app.py

app.py

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+pip install gradio
+
+import gradio as gr
+import openai
+from wandb.integration.openai import autolog
+
+openai_api_key = "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"
+
+# start logging to W&B
+autolog({"project":"Joe1", "job_type": "introduction"})
+
+import openai
+import gradio as gr
+
+# Add your OpenAI API key here
+openai.api_key = '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'
+
+# Initialize the conversation history
+conversation_history = [
+    {
+        "role": "system",
+        "content": "Your name is Joe Chip, a world class poker player. Keep your answers succinct but cover important areas."
+        "If you need more context ask for it."
+        " Make sure you know what the effective stack is and whether its a cash game or mtt"
+        "Concentrate more on GTO play rather than exploiting other players."
+        "Consider blockers when applicable" 
+        "Always discuss how to play your range, not just the hand in question"
+        "Remember to keep your answers brief"
+        "Only answer questions on poker topics"
+    }
+]
+
+def ask_joe(text):
+    # Add the user's message to the conversation history
+    conversation_history.append({
+        "role": "user",
+        "content": text
+    })
+    
+    # Use the conversation history as the input to the model
+    response = openai.ChatCompletion.create(
+        model="gpt-4", 
+        messages=conversation_history,
+        max_tokens=500, 
+        temperature=0.5
+    )
+    
+    # Extract the model's message from the response
+    model_message = response.choices[0].message['content'].strip()
+    
+    # Add the model's message to the conversation history
+    conversation_history.append({
+        "role": "assistant",
+        "content": model_message
+    })
+    
+    return model_message
+
+iface = gr.Interface(fn=ask_joe, inputs="text", outputs="text")
+
+iface.launch()
+
+
+# iface.launch(share=True)