asked gpt to convert existing to app.py

app.py

@@ -7,22 +7,8 @@ from bs4 import BeautifulSoup
 import gradio as gr
 
 # Configure Gemini API key
-gemini_api_secret_name = 'AIzaSyA0yLvySmj8xjMd0sedSgklg1fj0wBDyyw'
-
-
-try:
-    GOOGLE_API_KEY = 'AIzaSyA0yLvySmj8xjMd0sedSgklg1fj0wBDyyw'
-    genai.configure(api_key=GOOGLE_API_KEY)
-except userdata.SecretNotFoundError as e:
-    print(f'Secret not found\n\nThis expects you to create a secret named {gemini_api_secret_name} in Colab\n\nVisit https://makersuite.google.com/app/apikey to create an API key\n\nStore that in the secrets section on the left side of the notebook (key icon)\n\nName the secret {gemini_api_secret_name}')
-    raise e
-except userdata.NotebookAccessError as e:
-    print(f'You need to grant this notebook access to the {gemini_api_secret_name} secret in order for the notebook to access Gemini on your behalf.')
-    raise e
-except Exception as e:
-    # unknown error
-    print(f"There was an unknown error. Ensure you have a secret {gemini_api_secret_name} stored in Colab and it's a valid key from https://makersuite.google.com/app/apikey")
-    raise e
+GOOGLE_API_KEY = 'AIzaSyA0yLvySmj8xjMd0sedSgklg1fj0wBDyyw'  # Replace with your API key
+genai.configure(api_key=GOOGLE_API_KEY)
 
 # Fetch lecture notes and model architectures
 def fetch_lecture_notes():
@@ -125,7 +111,7 @@ def generate_concise_response(prompt, context):
         return "An error occurred while generating the concise response."
 
 # Main function to execute the pipeline
-def chatbot(message , history):
+def chatbot(message, history):
     lecture_notes = fetch_lecture_notes()
     model_architectures = fetch_model_architectures()
 
@@ -139,7 +125,6 @@ def chatbot(message , history):
     # Initialize FAISS index
     faiss_index = initialize_faiss_index(np.array(embeddings))
 
-
     response, sources = handle_query(message, faiss_index, all_texts, embedding_model)
     print("Query:", message)
     print("Response:", response)
@@ -148,7 +133,7 @@ def chatbot(message , history):
         print("Sources:", sources)
         relevant_source = "" 
         for source in sources:
-          relevant_source += source +"\n"
+            relevant_source += source + "\n"
         total_text += "\n\nSources:\n" + relevant_source
             
     else:
@@ -161,23 +146,24 @@ def chatbot(message , history):
     concise_response = generate_concise_response(prompt, user_queries_summary)
     print("Concise Response:")
     print(concise_response)
+    
     return total_text
 
-iface = gr.ChatInterface(
-    chatbot,
+iface = gr.Interface(
+    fn=chatbot,
+    inputs="text",
+    outputs="text",
     title="LLM Research Assistant",
     description="Ask questions about LLM architectures, datasets, and training techniques.",
     examples=[
-        "What are some milestone model architectures in LLMs?",
-        "Explain the transformer architecture.",
-        "Tell me about datasets used to train LLMs.",
-        "How are LLM training datasets cleaned and preprocessed?",
-        "Summarize the user queries so far"
+        ["What are some milestone model architectures in LLMs?"],
+        ["Explain the transformer architecture."],
+        ["Tell me about datasets used to train LLMs."],
+        ["How are LLM training datasets cleaned and preprocessed?"],
+        ["Summarize the user queries so far"]
     ],
-    retry_btn="Regenerate",
-    undo_btn="Undo",
-    clear_btn="Clear",
+    allow_flagging="never"
 )
 
 if __name__ == "__main__":
-    iface.launch(debug=True)
+    iface.launch(server_name="0.0.0.0", server_port=7860)