Create app.py

app.py

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+import streamlit as st
+import google.generativeai as genai
+import os
+import PyPDF2 as pdf
+from dotenv import load_dotenv
+
+load_dotenv()
+genai.configure(api_key=os.getenv('GOOGLE_API_KEY'))
+
+
+def get_gemeni_response(input_text):
+    model = genai.GenerativeModel('gemini-pro')
+    response = model.generate_content(input_text)
+    return response.text
+
+
+def input_pdf_text(upload_file):
+    reader = pdf.PdfReader(upload_file)
+    text = ""
+    for page in range(len(reader.pages)):
+        page_text = reader.pages[page].extract_text()
+        text += str(page_text)
+    return text
+
+
+# Prompt engineering
+input_prompt = """
+hey Act like a skilled or very experienced ATS (Application Tracking System) with a deep understanding of tech field, software engineering,
+data science, data analytics, and big data engineering. Your task is to evaluate the resume based on the given job description. You must 
+consider the job market is very competitive and provide the best assistance for improving the resume. Assign the percentage
+Matching based on JD (Job Description) and the missing keywords with high accuracy.
+resume: {text}
+description: {jd}
+
+I want the response in a structured JSON format like this:
+{{"JD Match":"%", "MissingKeywords":[], "Profile Summary":""}}
+"""
+
+# Streamlit app setup
+st.title('Smart ATS')
+st.text("Improve your Resume ATS")
+jd = st.text_area("Paste the Job Description")
+uploaded_file = st.file_uploader("Upload your Resume", type="pdf", help="Please upload your resume in PDF format")
+submit = st.button("Submit")
+
+if submit:
+    if uploaded_file is not None:
+        text = input_pdf_text(uploaded_file)
+        response = get_gemeni_response(input_prompt.format(text=text, jd=jd))
+
+        try:
+            # Parse the response into a dictionary
+            response_dict = eval(response)  # Be cautious with eval; use safer alternatives if possible
+
+            # Display structured output
+            st.subheader("Job Description Match")
+            st.write(f"{response_dict['JD Match']}")
+
+            st.subheader("Missing Keywords")
+            st.write(", ".join(response_dict["MissingKeywords"]))
+
+            st.subheader("Profile Summary")
+            st.text(response_dict["Profile Summary"])
+
+        except Exception as e:
+            st.error("Error parsing response. Check the response format.")
+            st.write(response)  # Display raw response for debugging
+    else:
+        st.warning("Please upload your resume.")