Update app.py

app.py

@@ -1,12 +1,10 @@
-import os
 import streamlit as st
 import PyPDF2
-from langchain_community.llms import HuggingFaceHub
-
-# Streamlit page configuration
-st.set_page_config(page_title="Research Position Application Generator", page_icon="🔬")
+import os
+from langchain.llms import HuggingFaceHub
+from langchain.prompts import PromptTemplate
 
-# Set Hugging Face API Key
+# Set up API token
 os.environ["HUGGINGFACEHUB_API_TOKEN"] = st.secrets["HF_TOKEN"]
 
 # Initialize LLM
@@ -15,145 +13,66 @@ llm = HuggingFaceHub(
     model_kwargs={"temperature": 0.5}
 )
 
-def extract_text_from_pdf(uploaded_file):
-    """
-    Extract text from an uploaded PDF file.
-    
-    Args:
-        uploaded_file (UploadedFile): PDF file uploaded by the user
-    
-    Returns:
-        str: Extracted text from the PDF
-    """
-    try:
-        pdf_reader = PyPDF2.PdfReader(uploaded_file)
-        text = ""
-        for page in pdf_reader.pages:
-            text += page.extract_text()
-        return text
-    except Exception as e:
-        st.error(f"Error extracting PDF text: {e}")
-        return ""
-
-def generate_cold_email(position_details, cv_text):
-    """
-    Generate a professional cold email using the LLM.
-    
-    Args:
-        position_details (dict): Details about the research position
-        cv_text (str): Text extracted from the CV/resume
-    
-    Returns:
-        str: Generated cold email
-    """
-    prompt = f"""Write a professional and concise cold email to Professor {position_details['professor_name']} 
-    at {position_details['university']} about the research position in {position_details['research_focus']}. 
-    The email should:
-    1. Demonstrate knowledge of the professor's research
-    2. Highlight relevant experience from the CV
-    3. Express genuine interest in the position
-    4. Be no more than 250 words
-
-    CV Details:
-    {cv_text}
-
-    Research Position Details:
-    Research Focus: {position_details['research_focus']}
-    Professor: {position_details['professor_name']}
-    University: {position_details['university']}
-    """
-    
-    return llm.invoke(prompt)
+# Function to extract text from PDF
+def extract_text_from_pdf(pdf_file):
+    pdf_reader = PyPDF2.PdfReader(pdf_file)
+    text = ""
+    for page in pdf_reader.pages:
+        text += page.extract_text() + "\n"
+    return text
 
-def generate_cover_letter(position_details, cv_text):
-    """
-    Generate a formal cover letter using the LLM.
-    
-    Args:
-        position_details (dict): Details about the research position
-        cv_text (str): Text extracted from the CV/resume
-    
-    Returns:
-        str: Generated cover letter
-    """
-    prompt = f"""Write a professional and formal cover letter for a research position with the following details:
-    Research Focus: {position_details['research_focus']}
-    University: {position_details['university']}
+# Streamlit UI
+st.title("Cold Email & Cover Letter Generator for Professors")
 
-    The cover letter should:
-    1. Follow a standard business letter format
-    2. Clearly state the purpose of the letter
-    3. Highlight relevant skills and experiences from the CV
-    4. Demonstrate alignment with the research position
-    5. Be 300-400 words long
-    6. Include a strong closing paragraph
+# User inputs
+position_details = st.text_area("Enter details about the research position:", 
+                                "e.g., Professor's name, research focus, university, lab details, etc.")
+resume_file = st.file_uploader("Upload your CV/Resume (PDF)", type=["pdf"])
 
-    CV Details:
-    {cv_text}
-    """
-    
-    return llm.invoke(prompt)
+if st.button("Generate Cold Email & Cover Letter"):
+    if position_details and resume_file:
+        # Extract text from the uploaded PDF
+        resume_text = extract_text_from_pdf(resume_file)
 
-def main():
-    """
-    Main Streamlit app function
-    """
-    st.title("🔬 Research Position Application Generator")
-    
-    # Sidebar for position details
-    st.sidebar.header("Research Position Details")
-    professor_name = st.sidebar.text_input("Professor's Name")
-    university = st.sidebar.text_input("University")
-    research_focus = st.sidebar.text_input("Research Focus")
-    
-    # CV Upload
-    st.sidebar.header("Upload CV/Resume")
-    uploaded_cv = st.sidebar.file_uploader("Choose a PDF file", type="pdf")
-    
-    # Generate button
-    if st.sidebar.button("Generate Documents"):
-        # Validate inputs
-        if not (professor_name and university and research_focus and uploaded_cv):
-            st.error("Please fill in all details and upload a CV")
-            return
+        # Define prompt for cold email
+        email_prompt = PromptTemplate.from_template(
+            """
+            Based on the following details of a research position and a student's CV, generate a professional cold email:
+            
+            Research Position Details:
+            {position}
+            
+            Student's CV:
+            {resume}
+            
+            The email should be formal, concise, and engaging, expressing interest in the position while highlighting relevant skills.
+            """
+        )
         
-        # Extract CV text
-        cv_text = extract_text_from_pdf(uploaded_cv)
+        email_content = llm(email_prompt.format(position=position_details, resume=resume_text))
         
-        # Prepare position details
-        position_details = {
-            'professor_name': professor_name,
-            'university': university,
-            'research_focus': research_focus
-        }
+        # Define prompt for cover letter
+        cover_prompt = PromptTemplate.from_template(
+            """
+            Generate a professional cover letter based on the following research position details and a student's CV:
+            
+            Research Position Details:
+            {position}
+            
+            Student's CV:
+            {resume}
+            
+            The cover letter should be well-structured, highlighting the student's background, relevant skills, research interests, and motivation for applying.
+            """
+        )
         
-        # Generate documents
-        with st.spinner('Generating documents...'):
-            cold_email = generate_cold_email(position_details, cv_text)
-            cover_letter = generate_cover_letter(position_details, cv_text)
+        cover_content = llm(cover_prompt.format(position=position_details, resume=resume_text))
         
         # Display results
-        st.header("Generated Documents")
-        
-        # Cold Email
-        st.subheader("Cold Email")
-        st.write(cold_email)
-        st.download_button(
-            label="Download Cold Email",
-            data=cold_email,
-            file_name="cold_email.txt",
-            mime="text/plain"
-        )
+        st.subheader("Generated Cold Email")
+        st.write(email_content)
         
-        # Cover Letter
-        st.subheader("Cover Letter")
-        st.write(cover_letter)
-        st.download_button(
-            label="Download Cover Letter",
-            data=cover_letter,
-            file_name="cover_letter.txt",
-            mime="text/plain"
-        )
-
-if __name__ == "__main__":
-    main()
+        st.subheader("Generated Cover Letter")
+        st.write(cover_content)
+    else:
+        st.warning("Please provide both the position details and upload a CV.")