migrate to Open Ai

app_openAi.py

@@ -0,0 +1,94 @@
+import os
+import gradio as gr
+from openai import OpenAI
+
+# Initialize the OpenAI client
+client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))
+
+# Response generation with streaming
+def respond(
+    message,
+    history: list[dict],
+    system_message,
+    customer_profile,
+    customer_goals,
+    company_solution,
+    main_topic,
+    ask_for_topic_suggestions,
+    max_tokens,
+    temperature,
+    top_p,
+):
+    # Compose system message
+    enhanced_system_message = (
+        f"{system_message}\n\n"
+        f"Customer Profile: {customer_profile}\n"
+        f"Customer Goals, Pain Points, Obstacles, Wishes and Preferences: {customer_goals}\n"
+        f"Company solutions, products or services, value proposition and differentiators to solve customer problem: {company_solution}\n"
+        f"Main Topic: {main_topic}\n"
+    )
+    if ask_for_topic_suggestions:
+        enhanced_system_message += "\nThe user is also asking for topic suggestions to address their customer's needs."
+
+    # Format messages
+    messages = [{"role": "system", "content": enhanced_system_message}]
+    for m in history:
+        messages.append({"role": "user", "content": m["user"]})
+        if m.get("assistant"):
+            messages.append({"role": "assistant", "content": m["assistant"]})
+    messages.append({"role": "user", "content": message})
+
+    # Stream OpenAI response
+    response_text = ""
+    try:
+        response = client.chat.completions.create(
+            model="gpt-4o-mini",  # or other model as available
+            messages=messages,
+            max_tokens=max_tokens,
+            temperature=temperature,
+            top_p=top_p,
+            stream=True,
+        )
+        for chunk in response:
+            if chunk.choices and chunk.choices[0].delta.content:
+                token = chunk.choices[0].delta.content
+                response_text += token
+                yield response_text
+    except Exception as e:
+        yield f"❌ An error occurred: {str(e)}"
+
+# Gradio interface definition
+demo = gr.ChatInterface(
+    fn=respond,
+    additional_inputs=[
+        gr.Textbox(
+            value="You are a friendly Chatbot, a digital marketing expert and a talented copywriter. You are trying to help a user write a creative post to improve their SEO based on their input.",
+            label="Instructions to Bot",
+        ),
+        gr.Textbox(label="Your Customer Profile", placeholder="Describe your customer profile (e.g., age, interests, profession)"),
+        gr.Textbox(
+            label="Customer Goals, Pain Points, Obstacles, Wishes and Preferences",
+            placeholder="Describe your customer's goals, pain points, concerns, obstacles, wishes, and preferences",
+        ),
+        gr.Textbox(
+            label="Company solutions, products or services",
+            placeholder="Describe your company's solutions, products or services, value proposition and differentiators",
+        ),
+        gr.Textbox(label="Main Topic", placeholder="Enter the main topic of the post"),
+        gr.Checkbox(label="Ask for Topic Suggestions", value=False),
+        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
+        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
+    ],
+    title="SEO Assistant",
+    description=(
+        "This app provides customized content that resonates with your customers to improve your SEO. "
+        "Based on your input. Powered by OpenAI GPT-4o-mini and domain expertise. "
+        "Expand Additional Inputs by clicking on the arrow, input more details about your customers, then a message describing what you need the assistant to do for you. "
+        "Developed by wn. Disclaimer: AI can make mistakes. Use with caution and at your own risk!"
+    ),
+    type="messages",  # ensures compatibility with Gradio streaming ChatInterface
+)
+
+if __name__ == "__main__":
+    demo.launch()