Create app.py

app.py

@@ -0,0 +1,143 @@
+import streamlit as st
+from langchain.chains import LLMChain
+from langchain.prompts import PromptTemplate
+from langchain.llms import HuggingFaceHub
+import fitz  # PyMuPDF for PDF text extraction
+import pytesseract
+from PIL import Image
+import os
+
+# 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", model_kwargs={"temperature": 0.5})
+
+# Define Streamlit App
+st.set_page_config(page_title="DocuMentorAI", layout="wide")
+st.title("📄 DocuMentorAI")
+st.write("Upload your CV/Resume and generate professional application documents.")
+
+# File Upload (PDF/Image)
+uploaded_file = st.file_uploader("Upload your CV/Resume (PDF or Image)", type=["pdf", "png", "jpg", "jpeg"])
+
+def extract_text_from_pdf(pdf_file):
+    """Extract text from a PDF file."""
+    text = ""
+    with fitz.open(pdf_file) as doc:
+        for page in doc:
+            text += page.get_text()
+    return text
+
+def extract_text_from_image(image_file):
+    """Extract text from an image using OCR."""
+    image = Image.open(image_file)
+    return pytesseract.image_to_string(image)
+
+if uploaded_file:
+    file_type = uploaded_file.type
+    extracted_text = ""
+    
+    if file_type == "application/pdf":
+        extracted_text = extract_text_from_pdf(uploaded_file)
+    else:
+        extracted_text = extract_text_from_image(uploaded_file)
+    
+    st.subheader("Extracted Text from CV/Resume")
+    st.text_area("Preview:", extracted_text, height=150)
+
+# Define LLM Prompt Templates
+email_template = PromptTemplate.from_template("""
+You are an AI assistant helping users craft a professional cold email for a research position.
+
+### Input:
+- Recipient: {recipient_name}
+- Position: {position_name}
+- Research Interests: {research_interests}
+- Why this professor/lab: {reason}
+- Resume Details: {resume_text}
+
+### Output:
+A well-structured, concise cold email with a polite and engaging tone.
+""")
+
+cover_letter_template = PromptTemplate.from_template("""
+You are an AI assistant generating a professional cover letter.
+
+### Input:
+- Job Title: {job_title}
+- Company/University: {company}
+- Key Skills: {key_skills}
+- Resume Details: {resume_text}
+
+### Output:
+A polished and formal cover letter.
+""")
+
+research_statement_template = PromptTemplate.from_template("""
+You are an AI assistant generating a research statement for a Ph.D. application.
+
+### Input:
+- Research Interests: {research_interests}
+- Academic Background: {resume_text}
+- Future Research Goals: {goals}
+
+### Output:
+A compelling research statement with a strong academic tone.
+""")
+
+sop_template = PromptTemplate.from_template("""
+You are an AI assistant writing a Statement of Purpose (SOP) for a master's or Ph.D. program.
+
+### Input:
+- Program Name: {program_name}
+- University: {university}
+- Research Interests: {research_interests}
+- Career Goals: {career_goals}
+- Resume Details: {resume_text}
+
+### Output:
+A structured and professional SOP.
+""")
+
+# Create 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 for Document Generation
+st.subheader("📩 Generate Application Documents")
+
+tab1, tab2, tab3, tab4 = st.tabs(["Cold Email", "Cover Letter", "Research Statement", "SOP"])
+
+with tab1:
+    recipient = st.text_input("Recipient Name")
+    position = st.text_input("Position Name")
+    research_interests = st.text_area("Research Interests")
+    reason = st.text_area("Why this professor/lab?")
+    if st.button("Generate Cold Email"):
+        email = email_chain.run({"recipient_name": recipient, "position_name": position, "research_interests": research_interests, "reason": reason, "resume_text": extracted_text})
+        st.text_area("Generated Cold Email", email, height=250)
+
+with tab2:
+    job_title = st.text_input("Job Title")
+    company = st.text_input("Company/University")
+    key_skills = st.text_area("Key Skills")
+    if st.button("Generate Cover Letter"):
+        cover_letter = cover_letter_chain.run({"job_title": job_title, "company": company, "key_skills": key_skills, "resume_text": extracted_text})
+        st.text_area("Generated Cover Letter", cover_letter, height=250)
+
+with tab3:
+    research_goals = st.text_area("Future Research Goals")
+    if st.button("Generate Research Statement"):
+        research_statement = research_statement_chain.run({"research_interests": research_interests, "goals": research_goals, "resume_text": extracted_text})
+        st.text_area("Generated Research Statement", research_statement, height=250)
+
+with tab4:
+    program_name = st.text_input("Program Name")
+    university = st.text_input("University")
+    career_goals = st.text_area("Career Goals")
+    if st.button("Generate SOP"):
+        sop = sop_chain.run({"program_name": program_name, "university": university, "research_interests": research_interests, "career_goals": career_goals, "resume_text": extracted_text})
+        st.text_area("Generated SOP", sop, height=250)