Create app.py

app.py

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+import os
+import openai
+
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+os.environ["OPENAI_API_KEY"]
+os.environ["GROQ_API_KEY"]
+
+global agent
+
+
+def create_agent():
+
+    from langchain_groq import ChatGroq
+    from langchain.chains.conversation.memory import ConversationSummaryBufferMemory
+    from langchain.chains import ConversationChain
+
+    global agent
+
+    llm = ChatGroq(temperature=0, model_name="mixtral-8x7b-32768")
+    memory = ConversationSummaryBufferMemory(llm=llm, max_token_limit=1000)
+    agent = ConversationChain(llm=llm, memory=memory, verbose=True)
+
+    return "Successful!"
+
+
+def formatted_response(docs, question, response, state):
+
+    formatted_output = response + "\n\nSources"
+
+    for i, doc in enumerate(docs):
+        source_info = doc.metadata.get("source", "Unknown source")
+        page_info = doc.metadata.get("page", None)
+
+        doc_name = source_info.split("/")[-1].strip()
+
+        if page_info is not None:
+            formatted_output += f"\n{doc_name}\tpage no {page_info}"
+        else:
+            formatted_output += f"\n{doc_name}"
+
+    state.append((question, formatted_output))
+    return state, state
+
+
+def search_docs(prompt, question, state,k):
+
+    from langchain_openai import OpenAIEmbeddings
+    from langchain.vectorstores import FAISS
+    from langchain.callbacks import get_openai_callback
+
+    global agent
+    agent = agent
+
+    state = state or []
+
+    embeddings = OpenAIEmbeddings()
+    docs_db = FAISS.load_local(
+        "/content/drive/MyDrive/Art Story Merged DB", 
+        embeddings, 
+        allow_dangerous_deserialization=True
+    )
+    docs = docs_db.similarity_search(question,int(k))
+
+    prompt += "\n\n"
+    prompt += question
+    prompt += "\n\n"
+    prompt += str(docs)
+
+    with get_openai_callback() as cb:
+        response = agent.predict(input=prompt)
+        print(cb)
+
+    return formatted_response(docs, question, response, state)
+
+import gradio as gr
+
+css = """
+.col{
+    max-width: 75%;
+    margin: 0 auto;
+    display: flex;
+    flex-direction: column;
+    justify-content: center;
+    align-items: center;
+}
+"""
+
+with gr.Blocks(css=css) as demo:
+    gr.Markdown("## <center>Your AI Medical Assistant</center>")
+
+    with gr.Tab("Your AI Assistant"):
+        with gr.Column(elem_classes="col"):
+
+            with gr.Tab("Query Documents"):
+                with gr.Column():
+                    create_agent_button = gr.Button("Create Agent")
+                    create_agent_output = gr.Textbox(label="Output")
+
+                    docs_prompt_input = gr.Textbox(label="Custom Prompt")
+                    k=gr.Textbox(label="Number of Chunks")
+
+                    docs_chatbot = gr.Chatbot(label="Chats")
+                    docs_state = gr.State()
+
+                    docs_search_input = gr.Textbox(label="Question")
+                    docs_search_button = gr.Button("Search")
+
+                    gr.ClearButton(
+                        [docs_prompt_input, docs_search_input, create_agent_output]
+                    )
+
+    #########################################################################################################
+
+    create_agent_button.click(create_agent, inputs=None, outputs=create_agent_output)
+
+    docs_search_button.click(
+        search_docs,
+        inputs=[docs_prompt_input, docs_search_input, docs_state,k],
+        outputs=[docs_chatbot, docs_state],
+    )
+
+    #########################################################################################################
+
+demo.queue()
+demo.launch(debug=True)