Update app.py

app.py

@@ -1,255 +1,139 @@
 import streamlit as st
+from langchain.chat_models import ChatOpenAI
 from langchain.chains import LLMChain
 from langchain.prompts import PromptTemplate
-from langchain.llms import HuggingFaceHub
-import fitz  # PyMuPDF for PDF extraction
-from PIL import Image
-import os
-import pytesseract
-import re
-
-# Set Hugging Face API Key (Set this in Hugging Face Secrets)
-os.environ["HUGGINGFACEHUB_API_TOKEN"] = st.secrets["HF_TOKEN"]
-
-# Load Free LLM from Hugging Face
-llm = HuggingFaceHub(repo_id="mistralai/Mistral-7B-Instruct-v0.3", model_kwargs={"temperature": 0.5})
-
-# Streamlit App Configuration
-st.set_page_config(page_title="DocuMentorAI", layout="wide", page_icon="📄")
-st.title("📄 DocuMentorAI")
-st.write("Generate professional application documents with ease!")
-
-# Custom CSS for better UI
-st.markdown("""
-<style>
-    .stTextArea textarea { font-size: 16px !important; }
-    .stButton button { width: 100%; background-color: #4CAF50; color: white; }
-    .stDownloadButton button { width: 100%; background-color: #008CBA; color: white; }
-    .stMarkdown { font-size: 18px; }
-</style>
-""", unsafe_allow_html=True)
-
-# Text Input for Job Opening Details
-st.subheader("📢 Enter Opening Details")
-job_opening_text = st.text_area(
-    "Paste the job/research opening details here...",
-    height=150,
-    placeholder="Example: 'We are hiring a Research Assistant at XYZ University. The ideal candidate has experience in machine learning and data analysis...'"
+from langchain.memory import ConversationBufferMemory
+
+# Initialize OpenAI LLM
+llm = ChatOpenAI(model="gpt-4", temperature=0.7)
+
+# Define Prompts
+email_prompt = PromptTemplate(
+    input_variables=["professor_name", "university_name", "research_interests", "reason", "resume_text"],
+    template="""
+        Write a professional cold email to {professor_name} at {university_name} expressing interest in their research. 
+        Highlight relevant research interests: {research_interests}. 
+        Explain why you want to work with them: {reason}. Keep it concise and professional.
+    """
 )
-
-# Upload CV/Resume
-st.subheader("📄 Upload CV/Resume")
-cv_resume_file = st.file_uploader(
-    "Upload your CV/Resume (PDF or Image)",
-    type=["pdf", "png", "jpg", "jpeg"],
-    help="Upload a PDF or image of your CV/Resume for text extraction."
+cover_letter_prompt = PromptTemplate(
+    input_variables=["job_title", "company", "key_skills", "resume_text"],
+    template="""
+        Write a professional cover letter for the {job_title} position at {company}. 
+        Highlight key skills: {key_skills}. Keep it formal and tailored to the job.
+    """
+)
+research_statement_prompt = PromptTemplate(
+    input_variables=["research_background", "key_projects", "future_goals"],
+    template="""
+        Write a research statement based on the following:
+        - Background: {research_background}
+        - Key Projects: {key_projects}
+        - Future Research Goals: {future_goals}
+        Keep it structured and professional.
+    """
+)
+sop_prompt = PromptTemplate(
+    input_variables=["motivation", "academic_background", "research_experiences", "career_goals", "why_this_program"],
+    template="""
+        Write a Statement of Purpose (SOP) with:
+        - Motivation: {motivation}
+        - Academic Background: {academic_background}
+        - Research Experiences: {research_experiences}
+        - Career Goals: {career_goals}
+        - Why this program: {why_this_program}
+        Ensure it is compelling and well-structured.
+    """
 )
 
-# Function to extract text from PDF
-def extract_text_from_pdf(pdf_file):
-    pdf_bytes = pdf_file.read()
-    with fitz.open(stream=pdf_bytes, filetype="pdf") as doc:
-        return " ".join([page.get_text() for page in doc])
-
-# Function to extract text from Image using OCR
-def extract_text_from_image(image_file):
-    image = Image.open(image_file)
-    return pytesseract.image_to_string(image)
-
-# Function to extract text from uploaded files
-def extract_text(uploaded_file):
-    if uploaded_file:
-        file_type = uploaded_file.type
-        if file_type == "application/pdf":
-            return extract_text_from_pdf(uploaded_file)
-        else:
-            return extract_text_from_image(uploaded_file)
-    return ""
-
-# Extract text from CV/Resume
-cv_resume_text = extract_text(cv_resume_file)
-
-# Display Extracted Text
-if job_opening_text:
-    with st.expander("🔍 View Entered Opening Details"):
-        st.markdown(f"**Job Opening Details:**\n\n{job_opening_text}")
-
-if cv_resume_text:
-    with st.expander("🔍 View Extracted CV/Resume Details"):
-        st.markdown(f"**CV/Resume Details:**\n\n{cv_resume_text}")
-
-# Function to extract professor name, designation, and university
-def extract_professor_details(text):
-    professor_pattern = r"(Dr\.|Professor|Prof\.?)\s+([A-Z][a-z]+\s[A-Z][a-z]+)"
-    university_pattern = r"(University|Institute|College|School of [A-Za-z]+)"
-    
-    professor_match = re.search(professor_pattern, text)
-    university_match = re.search(university_pattern, text)
-
-    professor_name = professor_match.group(0) if professor_match else "Not Found"
-    university_name = university_match.group(0) if university_match else "Not Found"
-
-    return professor_name, university_name
-
-# Extract professor details if job opening is uploaded
-professor_name, university_name = extract_professor_details(job_opening_text)
-
-# LLM Prompt Templates
-email_template = PromptTemplate.from_template("""
-Write a professional cold email for a research position.
-- Address the professor formally.
-- Introduce yourself and academic background.
-- Express interest in their research.
-- Highlight key skills from your CV.
-- Conclude with a polite request.
-### Input:
-- Professor: {professor_name}
-- University: {university_name}
-- Research Interests: {research_interests}
-- Why This Lab: {reason}
-- CV Highlights: {resume_text}
-### Output:
-A well-structured, professional cold email.
-""")
-
-cover_letter_template = PromptTemplate.from_template("""
-Write a compelling job application cover letter.
-- Address the employer formally.
-- Mention job title and where you found it.
-- Highlight key skills and experiences.
-- Relate background to the company.
-- Conclude with enthusiasm.
-### Input:
-- Job Title: {job_title}
-- Company: {company}
-- Key Skills: {key_skills}
-- CV Highlights: {resume_text}
-### Output:
-A strong, well-formatted cover letter.
-""")
-
-research_statement_template = PromptTemplate.from_template("""
-Write a research statement for Ph.D. applications.
-- Discuss research background and motivation.
-- Explain key research experiences and findings.
-- Outline future research interests and goals.
-- Highlight contributions to the field.
-### Input:
-- Research Background: {research_background}
-- Key Research Projects: {key_projects}
-- Future Goals: {future_goals}
-### Output:
-A well-structured, professional research statement.
-""")
-
-sop_template = PromptTemplate.from_template("""
-Write a compelling Statement of Purpose (SOP).
-- Introduce motivation for graduate studies.
-- Discuss academic background.
-- Explain relevant experiences and research.
-- Outline career goals.
-- Justify fit for the program.
-### Input:
-- Motivation: {motivation}
-- Academic Background: {academic_background}
-- Research & Projects: {research_experiences}
-- Career Goals: {career_goals}
-- Why This Program: {why_this_program}
-### Output:
-A well-structured SOP.
-""")
-
-# LangChain Chains
-email_chain = LLMChain(llm=llm, prompt=email_template)
-cover_letter_chain = LLMChain(llm=llm, prompt=cover_letter_template)
-research_statement_chain = LLMChain(llm=llm, prompt=research_statement_template)
-sop_chain = LLMChain(llm=llm, prompt=sop_template)
-
-# User Inputs
-st.subheader("📩 Generate Application Documents")
-tab1, tab2, tab3, tab4 = st.tabs(["Cold Email", "Cover Letter", "Research Statement", "SOP"])
-
-# Cold Email Generation
-with tab1:
-    st.write(f"🧑‍🏫 **Detected Professor:** {professor_name} at {university_name}")
-    research_interests = st.text_area("Research Interests", placeholder="Example: Machine Learning, Data Analysis, etc.")
-    reason = st.text_area("Why this professor/lab?", placeholder="Example: I am particularly interested in your work on...")
-    
-    if st.button("Generate Cold Email"):
-        if not job_opening_text or not cv_resume_text:
-            st.error("Please provide job opening details and upload your CV/Resume.")
-        else:
-            with st.spinner("Generating Cold Email..."):
-                email = email_chain.run({
-                    "professor_name": professor_name,
-                    "university_name": university_name,
-                    "research_interests": research_interests,
-                    "reason": reason,
-                    "resume_text": cv_resume_text
-                })
-                st.markdown("**Generated Cold Email:**")
-                st.markdown(email)
-                st.download_button("Download Email", email, file_name="cold_email.txt")
-
-# Cover Letter Generation
-with tab2:
-    job_title = st.text_input("Job Title", placeholder="Example: Research Assistant")
-    company_name = university_name if university_name != "Not Found" else st.text_input("Company/University", placeholder="Example: XYZ University")
-    key_skills = st.text_area("Key Skills", placeholder="Example: Python, Machine Learning, Data Analysis")
+# Create LLM Chains
+email_chain = LLMChain(llm=llm, prompt=email_prompt, memory=ConversationBufferMemory(memory_key="history"))
+cover_letter_chain = LLMChain(llm=llm, prompt=cover_letter_prompt, memory=ConversationBufferMemory(memory_key="history"))
+research_statement_chain = LLMChain(llm=llm, prompt=research_statement_prompt, memory=ConversationBufferMemory(memory_key="history"))
+sop_chain = LLMChain(llm=llm, prompt=sop_prompt, memory=ConversationBufferMemory(memory_key="history"))
+
+# Streamlit App
+st.title("AI-Powered Academic & Job Application Generator")
+
+# File Upload for Resume
+st.sidebar.header("Upload Your Resume (Optional)")
+uploaded_file = st.sidebar.file_uploader("Upload a .txt file", type=["txt"])
+
+cv_resume_text = ""
+if uploaded_file is not None:
+    cv_resume_text = uploaded_file.read().decode("utf-8")
+
+# Tabs for Different Documents
+tabs = st.tabs(["Cold Email", "Cover Letter", "Research Statement", "Statement of Purpose"])
+
+# Cold Email Generator
+with tabs[0]:
+    st.header("Generate a Cold Email")
+    professor_name = st.text_input("Professor's Name")
+    university_name = st.text_input("University Name")
+    research_interests = st.text_area("Your Research Interests")
+    reason = st.text_area("Why do you want to work with this professor?")
+
+    if st.button("Generate Email"):
+        email_output = email_chain.run({
+            "professor_name": professor_name,
+            "university_name": university_name,
+            "research_interests": research_interests,
+            "reason": reason,
+            "resume_text": cv_resume_text
+        }).strip()
+        st.markdown(email_output)
+        st.download_button("Download Email", email_output, file_name="cold_email.txt")
+
+# Cover Letter Generator
+with tabs[1]:
+    st.header("Generate a Cover Letter")
+    job_title = st.text_input("Job Title")
+    company_name = st.text_input("Company Name")
+    key_skills = st.text_area("Key Skills for the Job")
 
     if st.button("Generate Cover Letter"):
-        if not job_opening_text or not cv_resume_text:
-            st.error("Please provide job opening details and upload your CV/Resume.")
-        else:
-            with st.spinner("Generating Cover Letter..."):
-                cover_letter = cover_letter_chain.run({
-                    "job_title": job_title,
-                    "company": company_name,
-                    "key_skills": key_skills,
-                    "resume_text": cv_resume_text
-                })
-                st.markdown("**Generated Cover Letter:**")
-                st.markdown(cover_letter)
-                st.download_button("Download Cover Letter", cover_letter, file_name="cover_letter.txt")
-
-# Research Statement Generation
-with tab3:
-    research_background = st.text_area("Research Background", placeholder="Example: My research focuses on...")
-    key_projects = st.text_area("Key Research Projects", placeholder="Example: Developed a machine learning model for...")
-    future_goals = st.text_area("Future Research Goals", placeholder="Example: I aim to explore...")
+        cover_letter_output = cover_letter_chain.run({
+            "job_title": job_title,
+            "company": company_name,
+            "key_skills": key_skills,
+            "resume_text": cv_resume_text
+        }).strip()
+        st.markdown(cover_letter_output)
+        st.download_button("Download Cover Letter", cover_letter_output, file_name="cover_letter.txt")
+
+# Research Statement Generator
+with tabs[2]:
+    st.header("Generate a Research Statement")
+    research_background = st.text_area("Your Research Background")
+    key_projects = st.text_area("Key Projects You've Worked On")
+    future_goals = st.text_area("Future Research Goals")
 
     if st.button("Generate Research Statement"):
-        with st.spinner("Generating Research Statement..."):
-            research_statement = research_statement_chain.run({
-                "research_background": research_background,
-                "key_projects": key_projects,
-                "future_goals": future_goals
-            })
-            st.markdown("**Generated Research Statement:**")
-            st.markdown(research_statement)
-            st.download_button("Download Research Statement", research_statement, file_name="research_statement.txt")
-
-# SOP Generation
-with tab4:
-    motivation = st.text_area("Motivation for Graduate Studies", placeholder="Example: I have always been passionate about...")
-    academic_background = st.text_area("Academic Background", placeholder="Example: I completed my undergraduate degree in...")
-    research_experiences = st.text_area("Research & Projects", placeholder="Example: During my undergraduate studies, I worked on...")
-    career_goals = st.text_area("Career Goals", placeholder="Example: My long-term goal is to...")
-    why_this_program = st.text_area("Why This Program", placeholder="Example: This program aligns with my research interests because...")
+        research_statement_output = research_statement_chain.run({
+            "research_background": research_background,
+            "key_projects": key_projects,
+            "future_goals": future_goals
+        }).strip()
+        st.markdown(research_statement_output)
+        st.download_button("Download Research Statement", research_statement_output, file_name="research_statement.txt")
+
+# Statement of Purpose Generator
+with tabs[3]:
+    st.header("Generate a Statement of Purpose (SOP)")
+    motivation = st.text_area("Your Motivation for Graduate Studies")
+    academic_background = st.text_area("Your Academic Background")
+    research_experiences = st.text_area("Your Research Experiences")
+    career_goals = st.text_area("Your Career Goals")
+    why_this_program = st.text_area("Why This Program?")
 
     if st.button("Generate SOP"):
-        with st.spinner("Generating SOP..."):
-            sop = sop_chain.run({
-                "motivation": motivation,
-                "academic_background": academic_background,
-                "research_experiences": research_experiences,
-                "career_goals": career_goals,
-                "why_this_program": why_this_program
-            })
-            st.markdown("**Generated SOP:**")
-            st.markdown(sop)
-            st.download_button("Download SOP", sop, file_name="sop.txt")
-
-# Reset Button
-if st.button("🔄 Reset All Inputs and Outputs"):
-    st.experimental_rerun()
+        sop_output = sop_chain.run({
+            "motivation": motivation,
+            "academic_background": academic_background,
+            "research_experiences": research_experiences,
+            "career_goals": career_goals,
+            "why_this_program": why_this_program
+        }).strip()
+        st.markdown(sop_output)
+        st.download_button("Download SOP", sop_output, file_name="sop.txt")