Create app_gpu2.py

app_gpu2.py

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+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
+from spaces import GPU  # Import GPU decorator
+
+# 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
+@GPU  # Ensure this class runs on the GPU
+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)
+
+        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
+cbfs_instances = {}  # Dictionary to store instances based on db_choice and model_choice
+
+def initialize_cbfs(db_choice, model_choice):
+    """Initialize cbfs object based on the database and model selection and clear history."""
+    key = (db_choice, model_choice)
+    if key not in cbfs_instances:
+        if db_choice == "Governance Documents":
+            cbfs_instances[key] = cbfs(persist_directory='docs/chroma_eg/', model_name=model_choice)
+        elif db_choice == "Faculty Handbook":
+            cbfs_instances[key] = cbfs(persist_directory='docs/chroma_hb/', model_name=model_choice)
+    return cbfs_instances[key]
+
+def chat_history(query, db_choice, model_choice):
+    """Handles chat submissions. Reminds the user to select a document if none is selected."""
+    cb = initialize_cbfs(db_choice, model_choice)
+    if not cb:  # 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):
+    cb = initialize_cbfs(db_choice, model_choice)
+    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()
+
+    # Update cbfs_instance and clear chat history when the dropdown values change
+    db_choice.change(
+        fn=clear_history,
+        inputs=[db_choice, model_choice],
+        outputs=[output, inp]
+    )
+
+    model_choice.change(
+        fn=clear_history,
+        inputs=[db_choice, model_choice],
+        outputs=[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])
+
+# Launch the Gradio app
+demo.launch()