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app.py

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+import streamlit as st
+from PyPDF2 import PdfReader
+import google.generativeai as genai
+import re
+
+# Configure the Generative AI API
+genai.configure(api_key="AIzaSyCtN-Ontp3bOU0g209_dXbDCMCfCULMFCQ")
+
+# Updated Prompt Template
+input_prompt = """
+You are an AI question generator. Generate a technical mock test with 30 questions based on the following instructions:
+1. Analyze the resume to identify the candidate's skills, experience, projects, and certifications.
+2. Generate questions that thoroughly test the candidate's knowledge and expertise in these areas.
+3. Analyze the job description to extract key requirements and generate questions that evaluate whether the candidate meets these requirements.
+4. Ensure the questions assess the candidate's capability and alignment with the job's expectations, aiding the hiring team in evaluating the validity of the candidate's claims.
+Resume:
+{resume_text}
+Job Description:
+{jd_text}
+"""
+
+answer_prompt_template = """
+You are an AI evaluator. Below are questions and a student's answers. Score each answer on a scale of 0 to 1, where 1 is completely correct and 0 is incorrect. Provide the correct answer for each question as well.
+Questions and Answers:
+{questions_and_answers}
+Provide your response in the following format:
+1. [Score] - [Probable Correct Answer]
+2. [Score] - [Probable Correct Answer]
+...
+"""
+
+# Function to extract text from PDF
+def input_pdf_text(file):
+    try:
+        pdf = PdfReader(file)
+        text = "".join(page.extract_text() for page in pdf.pages)
+        return text
+    except Exception as e:
+        return f"Error extracting text: {e}"
+
+# Function to generate questions using Generative AI
+def get_gemini_response(prompt):
+    model = genai.GenerativeModel('gemini-pro')
+    response = model.generate_content(prompt)
+    if hasattr(response.candidates[0], "content") and hasattr(response.candidates[0].content, "parts"):
+        return response.candidates[0].content.parts[0].text
+    else:
+        return None
+
+# Initialize session state for persistence
+if "questions" not in st.session_state:
+    st.session_state.questions = None
+if "user_answers" not in st.session_state:
+    st.session_state.user_answers = {}
+if "test_submitted" not in st.session_state:
+    st.session_state.test_submitted = False
+
+# Streamlit UI
+st.title("Technical Mock Test Generator")
+
+st.write("Upload a resume and a job description to generate a technical mock test based on the skills and requirements.")
+
+resume_file = st.file_uploader("Upload Resume (PDF)", type=["pdf"])
+jd_file = st.file_uploader("Upload Job Description (PDF)", type=["pdf"])
+
+if resume_file and jd_file:
+    # Reset session state when new files are uploaded
+    if st.session_state.questions is None or st.session_state.test_submitted:
+        resume_text = input_pdf_text(resume_file)
+        jd_text = input_pdf_text(jd_file)
+
+        if "Error" in resume_text or "Error" in jd_text:
+            st.error("Error reading one or both files. Please check the files and try again.")
+        else:
+            prompt = input_prompt.format(resume_text=resume_text, jd_text=jd_text)
+            st.write("Generating questions...")
+
+            try:
+                # Generate questions
+                questions_content = get_gemini_response(prompt)
+
+                if not questions_content:
+                    st.error("Unexpected response format. Please check the Generative AI configuration.")
+                else:
+                    # Extract questions using regular expression
+                    questions = re.findall(r'\d+\.\s.*', questions_content)
+                    questions = [re.sub(r'^\d+\.\s', '', question).strip() for question in questions]
+
+                    # Save questions to session state
+                    st.session_state.questions = questions
+                    st.session_state.user_answers = {idx + 1: "" for idx in range(len(questions))}
+                    st.session_state.test_submitted = False
+
+            except Exception as e:
+                st.error(f"Error generating questions: {e}")
+
+    # Display questions
+    if st.session_state.questions:
+        st.write("### Mock Test Questions")
+        for idx, question in enumerate(st.session_state.questions, 1):
+            st.write(f"**Q{idx}:** {question}")
+            st.session_state.user_answers[idx] = st.text_input(
+                f"Your Answer for Q{idx}:", 
+                value=st.session_state.user_answers[idx], 
+                key=f"q_{idx}"
+            )
+
+        # Submit button
+        if st.button("Submit Test"):
+            # Prepare input for LLM evaluation
+            qa_pairs = "\n".join(
+                f"{idx}. Q: {question} A: {st.session_state.user_answers[idx]}" 
+                for idx, question in enumerate(st.session_state.questions, 1)
+            )
+            eval_prompt = answer_prompt_template.format(questions_and_answers=qa_pairs)
+
+            try:
+                # Get LLM evaluation
+                eval_response = get_gemini_response(eval_prompt)
+
+                # Debug: Display raw response
+                st.write("### Raw Evaluation Response")
+                st.write(eval_response)
+
+                # Parse response
+                results = re.findall(r'(\d+)\.\s\[([\d\.]+)\]\s-\s(.*)', eval_response)
+
+                # Display score and feedback
+                if results:
+                    total_score = sum(float(score) for _, score, _ in results)
+                    attempted = sum(1 for idx in st.session_state.user_answers.values() if idx.strip())
+                    st.subheader(f"Your Score: {total_score:.2f} / {len(results)}")
+                    st.write(f"Total Questions Attempted: {attempted} / {len(results)}")
+
+                    # Display probable correct answers
+                    st.subheader("Probable Correct Answers")
+                    for idx, (_, score, correct_answer) in enumerate(results, 1):
+                        st.write(f"Q{idx}: {correct_answer} (Score: {score})")
+
+                else:
+                    st.error("Unexpected evaluation response format.")
+
+            except Exception as e:
+                st.error(f"Error evaluating answers: {e}")

requirements.txt

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+fastapi
+uvicorn
+google-generativeai
+PyPDF2
+python-dotenv
+fpdf
+python-multipart
+streamlit