Update app.py

app.py

@@ -2,16 +2,15 @@ import openai
 import os
 from paperqa import Docs
 import gradio as gr
-from langchain.document_loaders import PyPDFLoader
-from langchain.vectorstores import Chroma
-from langchain.embeddings.openai import OpenAIEmbeddings
-from langchain.document_loaders import UnstructuredPDFLoader
-from langchain.llms import OpenAI
-from langchain.chains.question_answering import load_qa_chain
-from langchain.chat_models import ChatOpenAI
+from dotenv import load_dotenv
 
-css_style = """
+# Load environment variables from .env file
+load_dotenv()
+
+# Get the OpenAI API key from the environment variable
+openai_api_key = os.getenv('OPENAI_API_KEY')
 
+css_style = """
 .gradio-container {
     font-family: "IBM Plex Mono";
 }
@@ -22,7 +21,6 @@ css_style = """
 }
 """
 
-
 def run(uploaded_files):
     all_files = []
     if uploaded_files is None:
@@ -33,18 +31,17 @@ def run(uploaded_files):
     print(all_files)
     return all_files
 
-
-def createAnswer(files, designation, openaikey):
-    os.environ['OPENAI_API_KEY'] = openaikey.strip()
-    docs = Docs(llm='gpt-3.5-turbo')
-    for d in files:
-        docs.add(d.name)
-    answer = docs.query(
-        f"Who is the best canidate to hire for {designation}. Provide a list with the candidate name. If you don't know, simply say None of the canidates are suited for the Job role.")
-    print(answer.formatted_answer)
-    print(type(answer))
-    return answer.answer
-
+def createAnswer(files, designation):
+    try:
+        os.environ['OPENAI_API_KEY'] = openai_api_key
+        docs = Docs(llm='gpt-3.5-turbo')
+        for d in files:
+            docs.add(d.name)
+        answer = docs.query(
+            f"Who is the best candidate to hire for {designation}. Provide a list with the candidate name. If you don't know, simply say None of the candidates are suited for the Job role.")
+        return answer.answer
+    except Exception as e:
+        return f"An error occurred: {str(e)}"
 
 with gr.Blocks(css=css_style) as demo:
     gr.Markdown(f"""
@@ -59,26 +56,28 @@ with gr.Blocks(css=css_style) as demo:
     for a single query. That is $0.10-0.20 per query, so please be careful!
     Porting it to Llama.cpp soon for saved cost.
 
-    1. Enter API Key ([What is that?](https://platform.openai.com/account/api-keys))
-    2. Upload your Resumes (Try a few resumes/cv to try < 5)
-    3. Provide Designation for which you are hiring
+    1. Upload your Resumes (Try a few resumes/cv to try < 5)
+    2. Provide Designation for which you are hiring
     """)
 
-    openaikey = gr.Text(
-        label='Your OpenAI Api Key', value="")
-    position = gr.Text(
-        label='Position/Designation for which you are hiring for', value="")
+    position = gr.Text(label='Position/Designation for which you are hiring for', value="")
 
     with gr.Tab('File Upload'):
-        uploaded_files = gr.File(
-            label="Resume Upload - ONLY PDF. (Doc File Support Coming Soon)", file_count="multiple", show_progress=True)
+        uploaded_files = gr.File(label="Resume Upload - ONLY PDF. (Doc File Support Coming Soon)", file_count="multiple", show_progress=True)
 
-    uploaded_files.change(
-        fn=run, inputs=[uploaded_files], outputs=[uploaded_files])
+    uploaded_files.change(fn=run, inputs=[uploaded_files], outputs=[uploaded_files])
     ask = gr.Button("Find Top Candidate")
-    answer = gr.Markdown(label="Result",  elem_classes='answerText')
-    ask.click(fn=createAnswer, inputs=[
-              uploaded_files, position, openaikey], outputs=[answer])
+    answer = gr.Markdown(label="Result", elem_classes='answerText')
+    loading_indicator = gr.Markdown(label="Loading", visible=False)
+
+    def on_ask_click():
+        loading_indicator.update(visible=True, value="Processing...")
+
+    ask.click(fn=on_ask_click, inputs=[], outputs=[loading_indicator]).then(
+        fn=createAnswer, inputs=[uploaded_files, position], outputs=[answer]
+    ).then(
+        fn=lambda: gr.Markdown.update(visible=False), inputs=[], outputs=[loading_indicator]
+    )
 
 demo.queue(concurrency_count=20)
 demo.launch(show_error=True)