Create app.py

app.py

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+import streamlit as st
+from langchain.llms import OpenAI
+from langchain.prompts import PromptTemplate
+from langchain.chains import LLMChain
+from langchain.agents import initialize_agent
+from langchain.chat_models import ChatOpenAI
+import json
+import os
+
+openai_api_key = os.environ.get('OPENAI_API_KEY')
+# Initialize your OpenAI language model here
+llm = OpenAI(temperature=0.6, openai_api_key=openai_api_key, model_name="gpt-3.5-turbo-16k")
+
+def generate_questionnaire(title, description, llm):
+
+    question_template = """You are a member of the hiring committee of your company. Your task is to develop screening questions for each candidate, considering different levels of importance or significance assigned to the job description.
+                                   Here are the Details:
+                                   Job title: {title}
+                                   Job description: {description}
+
+                                Your Response should follow the following format:
+                                 "id":1, "Question":"Your Question will go here"\n,
+                                 "id":2, "Question":"Your Question will go here"\n,
+                                 "id":3, "Question":"Your Question will go here"\n
+                                 There should be at least 10 questions. Do output only the questions but in text."""
+            
+    screen_template = PromptTemplate(input_variables=["title", "description"], template=question_template)
+    questions_chain = LLMChain(llm=llm, prompt=screen_template)
+    
+    response = questions_chain.run({"title": title, "description": description})
+    
+    return response
+
+# Streamlit App
+def main():
+    st.title("Candidate Screening Questionnaire Generator")
+    
+    job_title = st.text_input("Enter Job Title:")
+    job_description = st.text_area("Enter Job Description:")
+    
+    if st.button("Generate Questionnaire"):
+        if job_title and job_description:
+            questionnaire = generate_questionnaire(job_title, job_description, llm)
+            
+            st.write("Generated Questions:")
+            st.write(questionnaire)
+            
+            question_strings = questionnaire.split('"id":')
+            questions = []
+            for q_string in question_strings[1:]:
+                question_id, question_text = q_string.split(', "Question":')
+                question = {
+                    "id": int(question_id.strip()),
+                    "Question": question_text.strip()[1:-1]  # Removing the surrounding quotes
+                    }
+                questions.append(question)
+            questionnaire_json = json.dumps(questions, indent=4)
+            
+            # Make the questionnaire_json downloadable
+            st.download_button(
+                label="Download JSON Questionnaire",
+                data=questionnaire_json,
+                file_name="questionnaire.json",
+                mime="application/json"
+            )
+
+        else:
+            st.warning("Please provide Job Title and Job Description.")
+
+if __name__ == "__main__":
+    main()