Update app.py

app.py

@@ -1,71 +1,81 @@
-import gradio as gr
 import requests
+import gradio as gr
 
-# Azure OpenAI Configuration
-OPENAI_ENDPOINT = "https://<your-openai-resource-name>.openai.azure.com/"
-OPENAI_KEY = "GGdd0WBghN8iarseTni7x4SxRlINgXrCiQdExooi8qXXwQleIpnjJQQJ99AKACHYHv6XJ3w3AAABACOGHD3K"
-DEPLOYMENT_ID = "gpt-4"
+# Azure OpenAI and Cognitive Search Service Details
+OPENAI_ENDPOINT = "https://rag-openai-service-ak.openai.azure.com/openai/deployments/gpt-4-rag-ak/completions?api-version=2023-05-15"
+OPENAI_KEY = "B1XyCaz87o456EVD949oODcGC8KTAEQsNLI7Yq5cnYKk41SMY9PtJQQJ99AKACHYHv6XJ3w3AAABACOGAaCZ"
+SEARCH_ENDPOINT = "https://rag-search-service-ak.search.windows.net"
+SEARCH_KEY = "Kq2Ww1XBwGCvV4JXTMvWT6qo1O9HprGo74elTSNYHiAzSeDETx4y"
+INDEX_NAME = "rag-index-ak"
 
-# Function to Call Azure OpenAI API
-def call_openai_api(prompt):
-    url = f"{OPENAI_ENDPOINT}openai/deployments/{DEPLOYMENT_ID}/completions?api-version=2023-05-15"
+def query_openai(prompt):
     headers = {
         "Content-Type": "application/json",
-        "Authorization": f"Bearer {OPENAI_KEY}"
+        "api-key": OPENAI_KEY
     }
-    data = {
+    payload = {
         "prompt": prompt,
         "max_tokens": 500,
         "temperature": 0.7
     }
-    response = requests.post(url, headers=headers, json=data)
-
-    if response.status_code != 200:
-        return f"Error: {response.status_code} - {response.text}"
-
+    response = requests.post(OPENAI_ENDPOINT, headers=headers, json=payload)
+    if response.status_code == 401:
+        return "Error: Unauthorized. Please check your OpenAI API key or endpoint."
+    elif response.status_code == 429:
+        return "Error: Too Many Requests. Please try again later."
+    response.raise_for_status()
     return response.json()["choices"][0]["text"].strip()
 
-# Predefined Questions
-def get_predefined_questions():
-    return [
-        "What are the career opportunities in AI?",
-        "What is the scope of AI in engineering?",
-        "What are the key highlights of the Master of Engineering Handbook?",
-        "How to start a career in AI?",
-    ]
-
-# Function to Process Query
-def process_query(question):
-    predefined_responses = {
-        "What are the career opportunities in AI?": "AI offers diverse opportunities in data science, machine learning engineering, robotics, and more.",
-        "What is the scope of AI in engineering?": "AI in engineering includes predictive maintenance, process optimization, and smart systems.",
-        "What are the key highlights of the Master of Engineering Handbook?": "The handbook highlights program structure, course requirements, and career support for MEng students.",
-        "How to start a career in AI?": "Starting a career in AI involves learning programming, data science, and machine learning frameworks."
+def search_documents(query):
+    headers = {
+        "Content-Type": "application/json",
+        "api-key": SEARCH_KEY
+    }
+    payload = {
+        "search": query,
+        "top": 5
     }
+    response = requests.post(f"{SEARCH_ENDPOINT}/indexes/{INDEX_NAME}/docs/search?api-version=2021-04-30-Preview", headers=headers, json=payload)
+    if response.status_code != 200:
+        return "Error: Unable to fetch search results."
+    results = response.json()["value"]
+    content = "\n".join([doc["content"] for doc in results])
+    return content
 
-    if question in predefined_responses:
-        return predefined_responses[question]
+def process_query(query):
+    search_results = search_documents(query)
+    full_prompt = f"Context: {search_results}\n\nUser Question: {query}\n\nAnswer:"
+    return query_openai(full_prompt)
 
-    # Use Azure OpenAI for unlisted questions
-    return call_openai_api(question)
+# UI Functionality
+def chatbot_ui(predefined_question, user_question):
+    if predefined_question:
+        return process_query(predefined_question)
+    elif user_question:
+        return process_query(user_question)
+    else:
+        return "Please select or type a question to proceed."
 
-# Gradio UI
-def chatbot_ui(question):
-    response = process_query(question)
-    return response
+# Predefined Questions
+predefined_questions = [
+    "What are the career opportunities in AI?",
+    "What is the CPT start date?",
+    "What is the MEng Handbook about?",
+]
 
-def main():
-    predefined_questions = get_predefined_questions()
+# Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("# Azure-Powered RAG Chatbot")
+    with gr.Row():
+        predefined_dropdown = gr.Dropdown(label="Select a predefined question", choices=predefined_questions)
+        user_question_input = gr.Textbox(label="Or type your own question")
+    submit_button = gr.Button("Submit")
+    chatbot_response = gr.Textbox(label="Chatbot Response")
 
-    demo = gr.Interface(
-        fn=chatbot_ui,
-        inputs=gr.Dropdown(choices=predefined_questions, label="Select a predefined question"),
-        outputs=gr.Textbox(label="Chatbot Response"),
-        title="Azure-Powered RAG Chatbot",
-        live=True
+    submit_button.click(
+        chatbot_ui,
+        inputs=[predefined_dropdown, user_question_input],
+        outputs=chatbot_response
     )
 
-    demo.launch()
-
-if __name__ == "__main__":
-    main()
+demo.launch()