Update app.py

app.py

@@ -1,171 +1,4 @@
 
-# import os
-# import openai
-# from dotenv import load_dotenv
-# _ = load_dotenv()  # read local .env file
-
-# import gradio as gr
-# from langchain_chroma import Chroma
-# from langchain.chains import ConversationalRetrievalChain
-# from langchain_openai import OpenAIEmbeddings, ChatOpenAI
-
-# # Custom class to handle API routing for different models
-# class ChatOpenRouter(ChatOpenAI):
-#     openai_api_base: str
-#     openai_api_key: str
-#     model_name: str
-
-#     def __init__(self,
-#                  model_name: str,
-#                  openai_api_key: str = None,
-#                  openai_api_base: str = "https://openrouter.ai/api/v1",
-#                  **kwargs):
-#         openai_api_key = openai_api_key or os.getenv('OPENROUTER_API_KEY')
-#         super().__init__(openai_api_base=openai_api_base,
-#                          openai_api_key=openai_api_key,
-#                          model_name=model_name, **kwargs)
-
-# # Initialize embedding function here
-# embedding_function = OpenAIEmbeddings()
-
-# # Updated cbfs class with dynamic database and model selection
-# class cbfs:
-#     def __init__(self, persist_directory, model_name):
-#         self.chat_history = []
-#         self.answer = ""
-#         self.db_query = ""
-#         self.db_response = []
-#         self.panels = []
-#         # Initialize Chroma and the ConversationalRetrievalChain with the chosen database and model
-#         db = Chroma(persist_directory=persist_directory, embedding_function=embedding_function)
-#         retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": 3})
-        
-#         # Select model dynamically
-#         if model_name == "GPT-4":
-#             chosen_llm = ChatOpenAI(model_name="gpt-4-1106-preview", temperature=0)
-#         elif model_name == "GPT-3.5":
-#             chosen_llm = ChatOpenAI(model_name="gpt-3.5-turbo-0125", temperature=0)
-#         elif model_name == "Llama-3 8B":
-#             chosen_llm = ChatOpenRouter(model_name="meta-llama/llama-3-8b-instruct", temperature=0)
-#         elif model_name == "Gemini-1.5 Pro":
-#             chosen_llm = ChatOpenRouter(model_name="google/gemini-pro-1.5", temperature=0)
-#         elif model_name == "Claude 3 Sonnet":
-#             chosen_llm = ChatOpenRouter(model_name='anthropic/claude-3-sonnet', temperature=0)
-#         elif model_name == "Claude 3.5 Sonnet":
-#             chosen_llm = ChatOpenRouter(model_name='anthropic/claude-3.5-sonnet', temperature=0)
-#         else:
-#             # Default model
-#             chosen_llm = ChatOpenRouter(model_name="meta-llama/llama-3-70b-instruct", temperature=0)
-#             # chosen_llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0)
-
-#         self.qa = ConversationalRetrievalChain.from_llm(
-#             llm=chosen_llm,
-#             retriever=retriever,
-#             return_source_documents=True,
-#             return_generated_question=True,
-#         )
-
-#     def convchain(self, query):
-#         if not query:
-#             return [("User", ""), ("ChatBot", "")]
-#         result = self.qa.invoke({"question": query, "chat_history": self.chat_history})
-#         self.chat_history.append((query, result["answer"]))
-#         self.db_query = result["generated_question"]
-#         self.db_response = result["source_documents"]
-#         self.answer = result['answer']
-#         self.panels.append(["User", query])  # Ensure this is a list of two strings
-#         self.panels.append(["ChatBot", self.answer])  # Ensure this is a list of two strings
-#         return self.panels
-
-#     def clr_history(self):
-#         self.chat_history = []
-#         self.panels = []
-#         return self.panels  # Clear the chatbot display
-
-# # Create Gradio interface functions
-# def initialize_cbfs(db_choice, model_choice):
-#     """Initialize cbfs object based on the database and model selection and clear history."""
-#     if db_choice == "Governance Documents":
-#         return cbfs(persist_directory='docs/chroma_eg/', model_name=model_choice)
-#     elif db_choice == "Faculty Handbook":
-#         return cbfs(persist_directory='docs/chroma_hb/', model_name=model_choice)
-#     else:
-#         return None
-
-# def chat_history(query, db_choice, model_choice, cb):
-#     """Handles chat submissions. Reminds the user to select a document if none is selected."""
-#     # cb = initialize_cbfs(db_choice, model_choice)  # Reinitialize cbfs 
-#     if cb is None:  # If cb is not initialized, remind to select a document
-#         return [("ChatBot", "Please select a document from the dropdown menu before submitting your query.")], ""
-#     else:
-#         return cb.convchain(query), ""  # Clear input box by returning empty string
-
-# def clear_history(cb):
-#     # cb = initialize_cbfs(db_choice, model_choice)  # Reinitialize cbfs to clear history
-#     if cb is None:  # Check if cbfs instance is None
-#         return [], ""  # No error message, simply clear the UI components
-#     else:
-#         cb.clr_history()
-#         return [], ""
-
-# # Create Gradio UI layout
-# with gr.Blocks() as demo:
-#     # Full-width image at the top
-#     with gr.Row():
-#         gr.Image("isu_logo.jpg", elem_id="full_width_image", show_label=False)
-
-#     # Full-width text below the image
-#     with gr.Row():
-#         gr.Markdown("<h1 style='text-align: center; font-size: 3.5em;'>Department of Economics</h1>")
-        
-#     gr.Markdown("# Faculty Policies & Rules ChatBot")
-
-#     with gr.Row():
-#         db_choice = gr.Dropdown(["Governance Documents", "Faculty Handbook"], label="Select Document", scale=1)
-#         model_choice = gr.Dropdown(["GPT-3.5", "GPT-4", "Llama-3 70B", "Llama-3 8B", "Gemini-1.5 Pro", "Claude 3 Sonnet", "Claude 3.5 Sonnet"], 
-#                                    label="Select Model", scale=1, value = "Llama-3 70B")
-#         button_clearhistory = gr.Button("Clear History", scale=1)
-
-#     with gr.Row():
-#         inp = gr.Textbox(placeholder="Enter text here…", scale=8)
-#         button_submit = gr.Button("Submit", scale=1)
-
-#     output = gr.Chatbot()
-
-#     # Initialize cbfs instance
-#     cbfs_instance = gr.State(initialize_cbfs(db_choice.value, model_choice.value))
-
-#     # Update cbfs_instance and clear chat history when the dropdown values change
-#     def update_cbfs_and_clear_history(db_choice, model_choice):
-#         new_cbfs = initialize_cbfs(db_choice, model_choice)
-#         if new_cbfs:
-#             new_cbfs.clr_history()
-#         return new_cbfs, [], ""  # Clear the chatbot display and input box
-
-#     db_choice.change(
-#         fn=update_cbfs_and_clear_history,
-#         inputs=[db_choice, model_choice],
-#         outputs=[cbfs_instance, output, inp]
-#     )
-
-#     model_choice.change(
-#         fn=update_cbfs_and_clear_history,
-#         inputs=[db_choice, model_choice],
-#         outputs=[cbfs_instance, output, inp]
-#     )
-
-#     # Define interactions for both submit button and Enter key
-#     inp.submit(fn=chat_history, inputs=[inp, db_choice, model_choice, cbfs_instance], outputs=[output, inp])
-#     button_submit.click(fn=chat_history, inputs=[inp, db_choice, model_choice, cbfs_instance], outputs=[output, inp])
-#     button_clearhistory.click(fn=clear_history, inputs=cbfs_instance, outputs=[output, inp])
-
-
-
-# # Launch the Gradio app
-# demo.launch()
-
-
-import spaces
 import os
 import openai
 from dotenv import load_dotenv
@@ -175,18 +8,27 @@ import gradio as gr
 from langchain_chroma import Chroma
 from langchain.chains import ConversationalRetrievalChain
 from langchain_openai import OpenAIEmbeddings, ChatOpenAI
-from spaces import GPU  # Import GPU decorator
 
 # Custom class to handle API routing for different models
 class ChatOpenRouter(ChatOpenAI):
-    def __init__(self, model_name: str, openai_api_key: str = None, openai_api_base: str = "https://openrouter.ai/api/v1", **kwargs):
+    openai_api_base: str
+    openai_api_key: str
+    model_name: str
+
+    def __init__(self,
+                 model_name: str,
+                 openai_api_key: str = None,
+                 openai_api_base: str = "https://openrouter.ai/api/v1",
+                 **kwargs):
         openai_api_key = openai_api_key or os.getenv('OPENROUTER_API_KEY')
-        super().__init__(openai_api_base=openai_api_base, openai_api_key=openai_api_key, model_name=model_name, **kwargs)
+        super().__init__(openai_api_base=openai_api_base,
+                         openai_api_key=openai_api_key,
+                         model_name=model_name, **kwargs)
 
 # Initialize embedding function here
 embedding_function = OpenAIEmbeddings()
 
-@GPU  # Ensure this class runs on the GPU
+# Updated cbfs class with dynamic database and model selection
 class cbfs:
     def __init__(self, persist_directory, model_name):
         self.chat_history = []
@@ -214,6 +56,7 @@ class cbfs:
         else:
             # Default model
             chosen_llm = ChatOpenRouter(model_name="meta-llama/llama-3-70b-instruct", temperature=0)
+            # chosen_llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0)
 
         self.qa = ConversationalRetrievalChain.from_llm(
             llm=chosen_llm,
@@ -240,9 +83,8 @@ class cbfs:
         return self.panels  # Clear the chatbot display
 
 # Create Gradio interface functions
-
-def create_cbfs_instance(db_choice, model_choice):
-    """Create cbfs instance dynamically without using Gradio state."""
+def initialize_cbfs(db_choice, model_choice):
+    """Initialize cbfs object based on the database and model selection and clear history."""
     if db_choice == "Governance Documents":
         return cbfs(persist_directory='docs/chroma_eg/', model_name=model_choice)
     elif db_choice == "Faculty Handbook":
@@ -250,21 +92,21 @@ def create_cbfs_instance(db_choice, model_choice):
     else:
         return None
 
-def chat_history(query, db_choice, model_choice):
+def chat_history(query, db_choice, model_choice, cb):
     """Handles chat submissions. Reminds the user to select a document if none is selected."""
-    cb = create_cbfs_instance(db_choice, model_choice)
+    # cb = initialize_cbfs(db_choice, model_choice)  # Reinitialize cbfs 
     if cb is None:  # If cb is not initialized, remind to select a document
         return [("ChatBot", "Please select a document from the dropdown menu before submitting your query.")], ""
     else:
         return cb.convchain(query), ""  # Clear input box by returning empty string
 
-def clear_history(db_choice, model_choice):
-    """Clear history dynamically by creating a new cbfs instance."""
-    cb = create_cbfs_instance(db_choice, model_choice)
-    if cb is None:
+def clear_history(cb):
+    # cb = initialize_cbfs(db_choice, model_choice)  # Reinitialize cbfs to clear history
+    if cb is None:  # Check if cbfs instance is None
+        return [], ""  # No error message, simply clear the UI components
+    else:
+        cb.clr_history()
         return [], ""
-    cb.clr_history()
-    return [], ""
 
 # Create Gradio UI layout
 with gr.Blocks() as demo:
@@ -281,7 +123,7 @@ with gr.Blocks() as demo:
     with gr.Row():
         db_choice = gr.Dropdown(["Governance Documents", "Faculty Handbook"], label="Select Document", scale=1)
         model_choice = gr.Dropdown(["GPT-3.5", "GPT-4", "Llama-3 70B", "Llama-3 8B", "Gemini-1.5 Pro", "Claude 3 Sonnet", "Claude 3.5 Sonnet"], 
-                                   label="Select Model", scale=1, value="Llama-3 70B")
+                                   label="Select Model", scale=1, value = "Llama-3 70B")
         button_clearhistory = gr.Button("Clear History", scale=1)
 
     with gr.Row():
@@ -290,23 +132,36 @@ with gr.Blocks() as demo:
 
     output = gr.Chatbot()
 
-    # Define interactions for clearing history when dropdowns are changed
+    # Initialize cbfs instance
+    cbfs_instance = gr.State(initialize_cbfs(db_choice.value, model_choice.value))
+
+    # Update cbfs_instance and clear chat history when the dropdown values change
+    def update_cbfs_and_clear_history(db_choice, model_choice):
+        new_cbfs = initialize_cbfs(db_choice, model_choice)
+        if new_cbfs:
+            new_cbfs.clr_history()
+        return new_cbfs, [], ""  # Clear the chatbot display and input box
+
     db_choice.change(
-        fn=clear_history,
+        fn=update_cbfs_and_clear_history,
         inputs=[db_choice, model_choice],
-        outputs=[output, inp]
+        outputs=[cbfs_instance, output, inp]
     )
 
     model_choice.change(
-        fn=clear_history,
+        fn=update_cbfs_and_clear_history,
         inputs=[db_choice, model_choice],
-        outputs=[output, inp]
+        outputs=[cbfs_instance, output, inp]
     )
 
     # Define interactions for both submit button and Enter key
-    inp.submit(fn=chat_history, inputs=[inp, db_choice, model_choice], outputs=[output, inp])
-    button_submit.click(fn=chat_history, inputs=[inp, db_choice, model_choice], outputs=[output, inp])
-    button_clearhistory.click(fn=clear_history, inputs=[db_choice, model_choice], outputs=[output, inp])
+    inp.submit(fn=chat_history, inputs=[inp, db_choice, model_choice, cbfs_instance], outputs=[output, inp])
+    button_submit.click(fn=chat_history, inputs=[inp, db_choice, model_choice, cbfs_instance], outputs=[output, inp])
+    button_clearhistory.click(fn=clear_history, inputs=cbfs_instance, outputs=[output, inp])
+
+
 
 # Launch the Gradio app
 demo.launch()
+
+