Update app.py

app.py

@@ -2,12 +2,16 @@ import gradio as gr
 import os
 import io
 import PyPDF2
+#from langchain.llms import OpenAIChat
 from langchain_openai import ChatOpenAI
+
 from langchain.chains import LLMChain
 from langchain.memory import ConversationBufferMemory
 from langchain import PromptTemplate
 
-from gradio.components import File, Textbox, Slider
+
+# Updated imports for Gradio components
+from gradio.components import File, Textbox
 
 def extract_text_from_pdf_binary(pdf_binary):
     text = ""
@@ -23,11 +27,14 @@ def extract_text_from_pdf_binary(pdf_binary):
     return text
 
 def format_resume_to_yaml(api_key, file_content):
+    # Set the API key for OpenAI
     os.environ['OPENAI_API_KEY'] = api_key
 
+    # Check if the file content is not empty
     if not file_content:
         raise ValueError("The uploaded file is empty.")
 
+    # Extract text from the uploaded PDF binary
     resume_text = extract_text_from_pdf_binary(file_content)
 
     template = """Format the provided resume to this YAML template:
@@ -82,30 +89,12 @@ def format_resume_to_yaml(api_key, file_content):
     res = llm_chain.predict(human_input=resume_text)
     return res
 
-def match_resume_to_job_description(api_key, resume_file_content, job_description):
-    os.environ['OPENAI_API_KEY'] = api_key
-
-    if not resume_file_content or not job_description:
-        raise ValueError("The uploaded file or job description is empty.")
-
-    resume_text = extract_text_from_pdf_binary(resume_file_content)
-
-    prompt = f"Given the following resume text:\n{resume_text}\n\nAnd the job description:\n{job_description}\n\nEvaluate how well the resume matches the job description and provide a matching score from 0 to 100, where 100 is a perfect match."
-
-    llm = ChatOpenAI(model="gpt-3.5-turbo")
-    response = llm.predict(prompt=prompt)
-
-    return response
-
 def main():
-    input_api_key = gr.Textbox(label="Enter your OpenAI API Key")
-    input_pdf_file = gr.File(label="Upload your PDF resume", type="binary")
-    input_job_description = gr.Textbox(label="Enter the job description", placeholder="Paste the job description here")
-    output_yaml = gr.Textbox(label="Formatted Resume in YAML")
-    output_match_score = gr.Textbox(label="Resume Match Score")
-
-    # Define separate interfaces for each function to simplify
-    format_resume_interface = gr.Interface(
+    input_api_key = Textbox(label="Enter your OpenAI API Key")
+    input_pdf_file = File(label="Upload your PDF resume", type="binary")
+    output_yaml = Textbox(label="Formatted Resume in YAML")
+
+    iface = gr.Interface(
         fn=format_resume_to_yaml,
         inputs=[input_api_key, input_pdf_file],
         outputs=output_yaml,
@@ -113,17 +102,7 @@ def main():
         description="Upload a PDF resume and enter your OpenAI API key to get it formatted to a YAML template.",
     )
 
-    match_resume_interface = gr.Interface(
-        fn=match_resume_to_job_description,
-        inputs=[input_api_key, input_pdf_file, input_job_description],
-        outputs=output_match_score,
-        title="Resume Matcher",
-        description="Upload a PDF resume, enter your OpenAI API key and job description to get the matching score.",
-    )
-
-    # Launch interfaces in parallel if needed, or redesign to choose which to display based on user input
-    format_resume_interface.launch(debug=True, share=True)
-    # match_resume_interface.launch(debug=True, share=True)  # Uncomment to run separately
+    iface.launch(debug=True, share=True)
 
 if __name__ == "__main__":
-    main()
+    main()