Update app.py

app.py

@@ -1,239 +1,309 @@
-import streamlit as st
-from langchain.chains import create_history_aware_retriever, create_retrieval_chain
-from langchain.chains.combine_documents import create_stuff_documents_chain
-
-from langchain_core.prompts import ChatPromptTemplate
-from langchain_groq import ChatGroq
-
-from langchain_huggingface import HuggingFaceEmbeddings
-from langchain_text_splitters import RecursiveCharacterTextSplitter
-from langchain_community.document_loaders import PyPDFLoader
-from langchain.vectorstores import FAISS
-import os
-from dotenv import load_dotenv
-import google.generativeai as genai
-import pandas as pd
-import json
-from io import BytesIO
-import tempfile
-
-# Load environment variables
-load_dotenv()
-
-# Set up embeddings
-HF_TOKEN = os.getenv('HF_TOKEN')
-embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
-
-# API keys
-api_key = os.getenv('API_KEY')
-GEMINI_API_KEY = os.getenv('GEMINI_API_KEY')
-genai.configure(api_key=GEMINI_API_KEY)
-
-# Set up generative model
-model = genai.GenerativeModel('gemini-pro')
-
-
-def create_job_post(job_title, location, exp):
-    prompt = (
-        f"Create a job opening post for platforms like Internshala, LinkedIn, and Naukri.com. "
-        f"The post should include the job title: {job_title}, location: {location}, and required experience: {exp}. "
-        f"Make it attractive, include skills (if possible but the skills in boxes and highlight them) required, who can apply, benefits, and other necessary details. "
-        f"The post should be 100-200 words."
-    )
-
-    try:
-        # Replace 'model.generate_content' with the actual method to generate content
-        response = model.generate_content(prompt)  # assuming 'model' is defined elsewhere
-        return response.text
-    except Exception as e:
-        return f"Error generating response: {e}"
-
-
-# Streamlit app title
-st.title("Recruitment AI")
-
-# Job title, location, and experience input fields
-job_title = st.text_input("Enter the job title you are looking for")
-location = st.text_input("Enter the location you are looking for")
-exp = st.text_input("Enter the experience you are looking for")
-
-# Button to generate job post
-if st.button("Create Job Post"):
-    if job_title and location and exp:
-        job_post = create_job_post(job_title, location, exp)
-        st.write(job_post)
-    else:
-        st.warning("Please fill in all fields to generate the job post.")
-
-job_post = create_job_post(job_title, location, exp)
-# Resume scoring section
-
-llm = ChatGroq(groq_api_key=api_key, model_name="llama-3.1-8b-instant")
-llm_2 = ChatGroq(groq_api_key=api_key, model_name="gemma-7b-it")
-
-
-
-jd=st.file_uploader("Upload Job Description", type="pdf")
-def get_jd(jd):
-    try:
-        # Use a temporary file to save the uploaded file
-        with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as temp_pdf:
-            temp_pdf.write(jd.getvalue())
-            temp_pdf_path = temp_pdf.name
-
-        # Load the PDF using PyPDFLoader
-        loader = PyPDFLoader(temp_pdf_path)
-        docs = loader.load()
-
-    finally:
-        # Remove the temporary file after processing
-        if os.path.exists(temp_pdf_path):
-            os.remove(temp_pdf_path)
-
-    # Text splitting for embeddings
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=5000, chunk_overlap=500)
-    splits = text_splitter.split_documents(docs)
-
-    # Create FAISS vectorstore for retrieval
-    vectorstore = FAISS.from_documents(splits, embeddings)
-    retriever = vectorstore.as_retriever()
-
-    # Define prompt and QA chain
-    system_prompt = (
-        f"Extract the Job description from the uploaded file in proper format."
-    )
-
-    qa_prompt = ChatPromptTemplate.from_messages(
-        [
-            ("system", system_prompt),
-            ("human", "{context}\n{input}"),
-        ]
-    )
-
-    # Create question-answering chain
-    question_answer_chain = create_stuff_documents_chain(llm_2, qa_prompt)
-    rag_chain = create_retrieval_chain(retriever, question_answer_chain)
-
-    try:
-        # Retrieve the job description using the chain
-        response = rag_chain.invoke({
-            "input": "Describe the job description in proper format"
-        })
-        job_description = response["answer"]
-
-        return job_description
-
-    except Exception as e:
-        raise RuntimeError(f"Error retrieving job description: {e}")
-
-if jd:
-    job_description = get_jd(jd)
-
-
-
-    # File uploader for PDF resumes
-uploaded_files = st.file_uploader("Choose PDF files", type="pdf", accept_multiple_files=True)
-
-
-# Function to process PDFs in batches of 4
-
-
-
-
-def process_pdfs_in_batches(files):
-    batch_size = 4
-    num_batches = (len(files) // batch_size) + (1 if len(files) % batch_size != 0 else 0)
-    all_json_data = []
-
-    for i in range(num_batches):
-        batch = files[i * batch_size: (i + 1) * batch_size]  # Select a batch of files
-        documents = []  # List to hold all document contents
-
-        for j, uploaded_file in enumerate(batch):
-            try:
-                # Use a temporary file to save the uploaded file
-                with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as temp_pdf:
-                    temp_pdf.write(uploaded_file.getvalue())
-                    temp_pdf_path = temp_pdf.name
-
-                # Load the PDF using PyPDFLoader
-                loader = PyPDFLoader(temp_pdf_path)
-                docs = loader.load()
-                documents.extend(docs)
-
-            finally:
-                # Remove the temporary file after processing
-                if os.path.exists(temp_pdf_path):
-                    os.remove(temp_pdf_path)
-
-        # Text splitting for embeddings
-        text_splitter = RecursiveCharacterTextSplitter(chunk_size=5000, chunk_overlap=500)
-        splits = text_splitter.split_documents(documents)
-
-        # Create FAISS vectorstore for retrieval
-        vectorstore = FAISS.from_documents(splits, embeddings)
-        retriever = vectorstore.as_retriever()
-
-        # Define prompt and QA chain
-        system_prompt = (
-            f"You are a smart AI agent tasked with evaluating resumes against the job description: "
-            f"Job Title: {job_title}, Location: {location}, Experience: {exp}. "
-            f"Your evaluation should provide a score (0-100) for each resume based on skills, experience, and other factors. "
-            f"Extract the following details from each uploaded PDF: Name, Contact Number, Email,Address and the calculated Score. "
-            "Output must be a JSON array of dictionaries, where each dictionary has the keys: 'Name', 'Contact Number', 'Email', 'Address' and 'Score' "
-
-
-        )
-
-        qa_prompt = ChatPromptTemplate.from_messages(
-            [
-                ("system", system_prompt),
-                ("human", "{context}\n{input}"),
-            ]
-        )
-
-        # Create question-answering chain
-        question_answer_chain = create_stuff_documents_chain(llm, qa_prompt)
-        rag_chain = create_retrieval_chain(retriever, question_answer_chain)
-
-        try:
-            # Button for scoring resumes
-            response = rag_chain.invoke({
-                "input": "Evaluate these resumes and provide scores scores should be very accurate and strictly evaluated as it would be used by recruiter, as given by system prompt ,"
-                         " just provide the json data only not anything else and make sure to be consistent with the output and generate text only. Output must be a JSON array of dictionaries in text format, "
-                         "where each dictionary has the keys: 'Name', 'Contact Number', 'Email','Address and 'Score' .Just provide the json data nothing else."
-                         "Also the generated data should be equal to uploaded resumes not more nor less"
-            })
-
-
-
-            json_data = json.loads(response["answer"])
-
-            # Append the JSON data to the all_json_data list
-
-            all_json_data.extend(json_data)
-
-            #st.write(json_data)
-
-        except Exception as e:
-            st.error(f"Error: {e}")
-
-    # Once all batches are processed, you can use all_json_data as needed
-    # For example, converting it into a DataFrame and displaying
-    df = pd.DataFrame(all_json_data)
-    st.dataframe(
-        df.style
-        # Highlight min values
-        .set_table_styles([
-            {'selector': 'thead th', 'props': [('background-color', '#4CAF50'), ('color', 'white')]},
-            # Table header style
-            {'selector': 'tbody td', 'props': [('border', '1px solid #ddd'), ('padding', '8px')]}  # Table body style
-        ])
-    )
-
-
-# Call the batch processing function if files are uploaded
-if uploaded_files:
-    process_pdfs_in_batches(uploaded_files)
-
-
+import streamlit as st
+from langchain.chains import create_history_aware_retriever, create_retrieval_chain
+from langchain.chains.combine_documents import create_stuff_documents_chain
+
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_groq import ChatGroq
+
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_community.document_loaders import PyPDFLoader
+from langchain.vectorstores import FAISS
+import os
+from dotenv import load_dotenv
+import google.generativeai as genai
+import pandas as pd
+import json
+from io import BytesIO
+import tempfile
+from st_aggrid import AgGrid
+import base64
+
+
+# Load environment variables
+load_dotenv()
+
+
+
+# CSS for background and alignment
+# CSS for background with reduced transparency
+
+
+# Set up embeddings
+HF_TOKEN = os.getenv('HF_TOKEN')
+embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
+
+# API keys
+api_key = os.getenv('API_KEY')
+GEMINI_API_KEY = os.getenv('GEMINI_API_KEY')
+genai.configure(api_key=GEMINI_API_KEY)
+
+# Set up generative model
+model = genai.GenerativeModel('gemini-pro')
+
+#st.sidebar.title("Select AI Model")
+
+# model_name = st.sidebar.selectbox("Select AI Model", [
+#     "gemma-7b-it",
+#     "gemma2-9b-it",
+#     "llama-3.1-70b-versatile",
+#     "llama-3.1-8b-instant",
+#     "llama-3.2-1b-preview",
+#     "llama-3.2-3b-preview",
+#     "llama-3.2-90b-text-preview",
+#     "llama-3.2-90b-vision-preview",
+#     "llama-guard-3-8b",
+#     "llama3-70b-8192",
+#     "llama3-8b-8192",
+#     "llama3-groq-70b-8192-tool-use-preview",
+#     "llama3-groq-8b-8192-tool-use-preview",
+#     "llava-v1.5-7b-4096-preview",
+#     "mixtral-8x7b-32768"
+# ])
+
+st.title("🕵️ Recruitment AI 🤖")
+
+def create_job_post(job_title, location, exp,salary_range,other_input):
+    prompt = (
+        f"Create a job opening post for platforms like Internshala, LinkedIn, and Naukri.com. "
+        f"The post should include the job title: {job_title}, location: {location}, and required experience: {exp},salart range:{salary_range} and other input:{other_input}. "
+        f"Make it attractive, include skills (if possible but the skills in boxes and highlight them) required, who can apply, benefits, and other necessary details. "
+        f"The post should be 100-200 words."
+    )
+
+    try:
+        # Replace 'model.generate_content' with the actual method to generate content
+        response = model.generate_content(prompt)  # assuming 'model' is defined elsewhere
+        return response.text
+    except Exception as e:
+        return f"Error generating response: {e}"
+
+
+# Streamlit app title
+
+
+with st.form("job_post_form"):
+    st.write("📝 Fill in the details to create a job post ✍🏼 :")
+    st.write("  ───────────────────────────────────────────────────────────")
+
+    left, right = st.columns(2)
+    with left:
+        # Job title, location, and experience input fields
+        job_title = st.text_input("Enter the job title you are looking for:")
+        location = st.text_input("Enter the location you are looking for:")
+    with right:
+        exp = st.text_input("Enter the experience you are looking for:")
+        salary_range = st.text_input("Enter the Salary range you are looking for:")
+
+    other_input = st.text_input("Enter any other details you want to add in the job post:")
+
+    st.write("  ───────────────────────────────────────────────────────────")
+
+    # Form submission button
+    submitted = st.form_submit_button("Create Job Post ✅")
+
+    if submitted:
+        if job_title and location and exp:
+            job_post = create_job_post(job_title, location, exp, salary_range, other_input)
+            st.write("### Generated Job Post:")
+            st.write(job_post)
+        else:
+            st.warning("Please fill in all required fields to generate the job post.")
+
+job_post = create_job_post(job_title, location, exp, salary_range,other_input)
+
+
+
+
+# Resume scoring section
+
+llm = ChatGroq(groq_api_key=api_key, model_name="gemma2-9b-it")
+llm_2 = ChatGroq(groq_api_key=api_key, model_name="gemma-7b-it")
+
+
+
+jd=st.file_uploader("📝 Upload Job Description 📜", type="pdf")
+def get_jd(jd):
+    try:
+        # Use a temporary file to save the uploaded file
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as temp_pdf:
+            temp_pdf.write(jd.getvalue())
+            temp_pdf_path = temp_pdf.name
+
+        # Load the PDF using PyPDFLoader
+        loader = PyPDFLoader(temp_pdf_path)
+        docs = loader.load()
+
+    finally:
+        # Remove the temporary file after processing
+        if os.path.exists(temp_pdf_path):
+            os.remove(temp_pdf_path)
+
+    # Text splitting for embeddings
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=5000, chunk_overlap=500)
+    splits = text_splitter.split_documents(docs)
+
+    # Create FAISS vectorstore for retrieval
+    vectorstore = FAISS.from_documents(splits, embeddings)
+    retriever = vectorstore.as_retriever()
+
+    # Define prompt and QA chain
+    system_prompt = (
+        f"Extract the Job description from the uploaded file in proper format. Also keep the value of the Job Location in mind as it will be required Later to match with resumes "
+    )
+
+    qa_prompt = ChatPromptTemplate.from_messages(
+        [
+            ("system", system_prompt),
+            ("human", "{context}\n{input}"),
+        ]
+    )
+
+    # Create question-answering chain
+    question_answer_chain = create_stuff_documents_chain(llm_2,qa_prompt)
+    rag_chain = create_retrieval_chain(retriever, question_answer_chain)
+
+    try:
+        # Retrieve the job description using the chain
+        response = rag_chain.invoke({
+            "input": "Describe the job description in proper format"
+        })
+        job_description = response["answer"]
+
+        return job_description
+
+    except Exception as e:
+        raise RuntimeError(f"Error retrieving job description: {e}")
+
+if jd:
+    job_description = get_jd(jd)
+
+
+
+    # File uploader for PDF resumes
+
+
+
+
+
+
+def process_pdfs_in_batches(files):
+    batch_size = 4
+    num_batches = (len(files) // batch_size) + (1 if len(files) % batch_size != 0 else 0)
+    all_json_data = []
+
+    for i in range(num_batches):
+        batch = files[i * batch_size: (i + 1) * batch_size]  # Select a batch of files
+        documents = []  # List to hold all document contents
+
+        for j, uploaded_file in enumerate(batch):
+            try:
+                # Use a temporary file to save the uploaded file
+                with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as temp_pdf:
+                    temp_pdf.write(uploaded_file.getvalue())
+                    temp_pdf_path = temp_pdf.name
+
+                # Load the PDF using PyPDFLoader
+                loader = PyPDFLoader(temp_pdf_path)
+                docs = loader.load()
+                documents.extend(docs)
+
+            finally:
+                # Remove the temporary file after processing
+                if os.path.exists(temp_pdf_path):
+                    os.remove(temp_pdf_path)
+
+        # Text splitting for embeddings
+        text_splitter = RecursiveCharacterTextSplitter(chunk_size=5000, chunk_overlap=500)
+        splits = text_splitter.split_documents(documents)
+
+        # Create FAISS vectorstore for retrieval
+        vectorstore = FAISS.from_documents(splits, embeddings)
+        retriever = vectorstore.as_retriever()
+
+        # Define prompt and QA chain
+        system_prompt = (
+            f"You are a smart AI agent tasked with evaluating resumes against the job description: "
+            f"Job Title: {job_title}, Location: {location}, Experience: {exp}. "
+            f"Your evaluation should provide a score (0-100) for each resume based on skills, experience, and other factors. "
+            f"Extract the following details from each uploaded PDF: Name, Contact Number, Email,Address and the calculated Score. "
+            "Output must be a JSON array of dictionaries, where each dictionary has the keys: 'Name', 'Contact Number', 'Email', 'Address' and 'Score' "
+            "Generate JSON data for, but present it as plain text in the response. Do not use a code block or any structured formatting like boxes."
+            "Do not include extra line breaks or whitespace outside the JSON array. The JSON must start with [ and end with ]"
+            f"if the Address of the candidate is too far (like more than 100 kms) away from {location} or the job location mentioned in the {job_description}, then the score should be 0."
+        )
+
+        qa_prompt = ChatPromptTemplate.from_messages(
+            [
+                ("system", system_prompt),
+                ("human", "{context}\n{input}"),
+            ]
+        )
+
+        # Create question-answering chain
+        question_answer_chain = create_stuff_documents_chain(llm, qa_prompt)
+        rag_chain = create_retrieval_chain(retriever, question_answer_chain)
+
+        try:
+            # Button for scoring resumes
+            response = rag_chain.invoke({
+                "input": (
+                    "You are a smart AI agent tasked with evaluating resumes based on a job description. "
+                    "Strictly follow the instructions below to generate the output:\n\n"
+                    "1. Extract the following details from each resume: Name, Contact Number, Email, Address, and Score.\n"
+                    "2. Output the data as a JSON array of dictionaries. Each dictionary must contain these keys: "
+                    "'Name', 'Contact Number', 'Email', 'Address', and 'Score'.\n"
+                    "3. The output must be plain text JSON without any extra formatting, syntax highlighting, or visual artifacts. "
+                    "Do not include explanations, metadata, or anything other than the JSON array.\n"
+                    "4. Ensure the JSON is well-formed and matches the number of resumes exactly.\n\n"
+                    "Generate only the JSON array in text format, ensuring it adheres to this structure."
+                    "Generate JSON data for, but present it as plain text in the response. Do not use a code block or any structured formatting like boxes."
+                    f"if the Address of the candidate is too far (like more than 100 kms) away from {location} or the job location mentioned in the {job_description}, then the score should be 0.but dont include comparison in the output"
+                )
+            })
+
+
+            json_data = json.loads(response["answer"])
+
+            # Append the JSON data to the all_json_data list
+
+            all_json_data.extend(json_data)
+            sorted_data = sorted(all_json_data, key=lambda x: x["Score"], reverse=True)
+
+            #st.write(json_data)
+
+        except Exception as e:
+            st.error(f"Error: {e}")
+
+    # Once all batches are processed, you can use all_json_data as needed
+    # For example, converting it into a DataFrame and displaying
+    df = pd.DataFrame(sorted_data)
+
+    st.dataframe(
+        df.style
+        # Highlight min values
+        .set_table_styles([
+            {'selector': 'thead th', 'props': [('background-color', '#4CAF50'), ('color', 'white')]},
+            # Table header style
+            {'selector': 'tbody td', 'props': [('border', '1px solid #ddd'), ('padding', '8px')]}  # Table body style
+        ])
+    )
+
+
+st.write("  ───────────────────────────────────────────────────────────")
+st.write("📂 Upload the resumes to score them 📑:")
+
+uploaded_files = st.file_uploader("Choose PDF files", type="pdf", accept_multiple_files=True)
+
+st.write("  ───────────────────────────────────────────────────────────")
+if st.button("Score Resumes✔️"):
+    if uploaded_files:
+        process_pdfs_in_batches(uploaded_files)
+    else:
+        st.warning("Please upload files to score.")
+
+
+# Call the batch processing function if files are uploaded
+
+
+