Create app.py

app.py

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+import streamlit as st
+import os
+import logging
+import dotenv
+import yaml
+import PyPDF2
+from langchain_community.vectorstores import FAISS
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain.prompts import PromptTemplate
+from langchain.llms import HuggingFaceHub
+import os
+dotenv.load_dotenv()
+
+# Load configuration from YAML
+def load_config():
+    with open("config.yaml", "r") as f:
+        return yaml.safe_load(f)
+
+config = load_config()
+hf_token = os.getenv("Gem") # Store API token in .env
+logging.basicConfig(level=logging.INFO)
+
+# Load embedding model
+embeddings_model = HuggingFaceEmbeddings(model_name=config["embedding_model"])
+
+# Extract text from PDFs
+def extract_text_from_pdf(file):
+    reader = PyPDF2.PdfReader(file)
+    text = ""
+    for page in reader.pages:
+        text += page.extract_text() or ""
+    return text.strip()
+
+# Get interview questions and assess responses
+def get_interview_response(jd_text, resume_text, candidate_response=None):
+    prompt_template = """
+    You are an AI interviewer assessing a candidate for a job role.
+    
+    JOB DESCRIPTION:
+    {jd_text}
+
+    CANDIDATE PROFILE:
+    {resume_text}
+
+    1. Start by asking an **introductory question**: "Tell me about yourself."
+    2. Then, based on the job description, ask a **technical question**.
+    3. If the candidate has already responded, evaluate their answer and provide constructive feedback.
+
+    Maintain a professional yet friendly tone.
+    """
+
+    prompt = PromptTemplate(
+        input_variables=["jd_text", "resume_text"], 
+        template=prompt_template
+    ).format(jd_text=jd_text, resume_text=resume_text)
+
+    if candidate_response:
+        prompt += f"\n\nCANDIDATE RESPONSE: {candidate_response}\n\nAssess the response and provide feedback."
+
+    llm = HuggingFaceHub(
+        repo_id=config["model_name"],
+        model_kwargs={"temperature": config["temperature"], "max_length": 200},
+        huggingfacehub_api_token=hf_token
+    )
+
+    return llm(prompt).strip()
+
+# Streamlit UI
+st.set_page_config(page_title="AI Interviewer", layout="centered")
+
+st.title("🤖 AI Interview Chatbot")
+st.write("Upload a Job Description and Resume to start the interview.")
+
+jd_file = st.file_uploader("Upload Job Description (PDF)", type=["pdf"])
+resume_file = st.file_uploader("Upload Candidate Resume (PDF)", type=["pdf"])
+
+if jd_file and resume_file:
+    jd_text = extract_text_from_pdf(jd_file)
+    resume_text = extract_text_from_pdf(resume_file)
+
+    if "interview_history" not in st.session_state:
+        st.session_state["interview_history"] = []
+        first_question = get_interview_response(jd_text, resume_text)
+        st.session_state["interview_history"].append(("AI", first_question))
+
+    for role, msg in st.session_state["interview_history"]:
+        st.chat_message(role).write(msg)
+
+    query = st.chat_input("Your Response:")
+    
+    if query:
+        response = get_interview_response(jd_text, resume_text, query)
+        st.session_state["interview_history"].append(("You", query))
+        st.session_state["interview_history"].append(("AI", response))
+        st.rerun()