Update app.py

app.py

@@ -1,109 +1,147 @@
 import streamlit as st
 from docx import Document
+import requests
 import io
-from transformers import pipeline
 
-# Load a pre-trained model for text generation
-@st.cache_resource
-def load_model():
-    # Using Flan-T5-large for generating job descriptions
-    generator = pipeline("text-generation", model="google/flan-t5-large")
-    return generator
+# Hugging Face API details
+API_URL = "https://api-inference.huggingface.co/models/google/flan-t5-large"
+HEADERS = {"Authorization": "Bearer TNovMWSMvIBjCPmyJSkBBXsYQAwWutoXbN"}
 
-# Function to create a Word document with the job description
-def create_word_document(designation, job_description):
+# Department and Role mapping
+DEPARTMENT_ROLES = {
+    "Construction": [
+        "Site Engineer",
+        "Project Manager",
+        "Construction Worker",
+        "Safety Officer",
+        "Quantity Surveyor"
+    ],
+    "Design": [
+        "Architect",
+        "Interior Designer",
+        "CAD Technician",
+        "Structural Engineer"
+    ],
+    "Management": [
+        "Operations Manager",
+        "Site Supervisor",
+        "Contracts Manager",
+        "Planning Manager"
+    ]
+}
+
+# Function to generate JD using Hugging Face model
+def generate_jd_with_hf(department, role):
+    prompt = f"""
+    Create a detailed professional job description for a {role} in the {department} department of a construction company.
+    Include the following sections:
+    1. Job Summary
+    2. Key Responsibilities
+    3. Qualifications and Skills
+    4. Experience Requirements
+    5. Benefits and Perks
+    
+    Make it professional and detailed, suitable for a construction company.
+    """
+    
+    payload = {
+        "inputs": prompt,
+        "parameters": {
+            "max_length": 1000,
+            "temperature": 0.7,
+            "do_sample": True
+        }
+    }
+    
+    response = requests.post(API_URL, headers=HEADERS, json=payload)
+    if response.status_code == 200:
+        return response.json()[0]['generated_text']
+    else:
+        raise Exception(f"API Error: {response.status_code} - {response.text}")
+
+# Function to create Word document
+def create_word_document(role, content):
     doc = Document()
-    doc.add_heading(f'Job Description for {designation}', level=1)
-    doc.add_paragraph(job_description)
     
-    # Save the document to a BytesIO object
-    doc_io = io.BytesIO()
-    doc.save(doc_io)
-    doc_io.seek(0)
+    # Add title
+    title = doc.add_heading(f"Job Description: {role}", 0)
+    title.alignment = 1  # Center alignment
     
-    return doc_io
+    # Add content
+    for line in content.split('\n'):
+        if line.strip().startswith(('1.', '2.', '3.', '4.', '5.')):
+            heading = doc.add_heading(line.strip(), level=1)
+        else:
+            para = doc.add_paragraph(line)
+    
+    # Save to bytes buffer
+    buffer = io.BytesIO()
+    doc.save(buffer)
+    buffer.seek(0)
+    return buffer
 
-# Streamlit app layout
-st.set_page_config(
-    page_title="Construction Job Description Generator",
-    page_icon="👷‍♂️",
-    layout="wide"
-)
+# Streamlit UI
+st.set_page_config(page_title="Construction JD Generator", layout="wide")
 
-# Custom CSS for better UI/UX
-st.markdown("""
-    <style>
-        .main {
-            background-color: #f4f4f9;
-        }
-        .stButton>button {
-            background-color: #4CAF50;
-            color: white;
-            border-radius: 5px;
-            padding: 10px 20px;
-            font-size: 16px;
-        }
-        .stSelectbox>div>div {
-            background-color: #ffffff;
-            border-radius: 5px;
-        }
-        .header {
-            font-size: 36px;
-            color: #333333;
-            text-align: center;
-            margin-bottom: 20px;
-        }
-        .subheader {
-            font-size: 24px;
-            color: #555555;
-            margin-bottom: 10px;
-        }
-    </style>
-""", unsafe_allow_html=True)
+st.title("🏗️ AI-Powered Job Description Generator")
+st.subheader("Create professional job descriptions for your construction company")
 
-# Header
-st.markdown('<div class="header">Construction Job Description Generator</div>', unsafe_allow_html=True)
+# Create two columns for department and role selection
+col1, col2 = st.columns(2)
 
-# Load the pre-trained model
-generator = load_model()
+with col1:
+    department = st.selectbox(
+        "Select Department",
+        list(DEPARTMENT_ROLES.keys()),
+        index=0,
+        help="Choose the department for the position"
+    )
 
-# Departments and Roles
-departments = {
-    "Construction": ["Construction Manager", "Civil Engineer", "Project Manager", "Site Supervisor"],
-    "Engineering": ["Structural Engineer", "Electrical Engineer", "Mechanical Engineer"],
-    "Safety": ["Safety Officer", "Environmental Specialist"]
-}
+with col2:
+    role = st.selectbox(
+        "Select Role",
+        DEPARTMENT_ROLES[department],
+        index=0,
+        help="Choose the specific role"
+    )
 
-# Step 1: Select Department
-st.markdown('<div class="subheader">Step 1: Select Department</div>', unsafe_allow_html=True)
-department = st.selectbox("Select the department:", list(departments.keys()))
+# Generate JD button
+if st.button("✨ Generate Job Description"):
+    with st.spinner("Generating professional job description using AI..."):
+        try:
+            # Generate JD content
+            jd_content = generate_jd_with_hf(department, role)
+            
+            # Create Word document
+            doc_buffer = create_word_document(role, jd_content)
+            
+            # Show preview
+            st.success("Job Description Generated Successfully!")
+            st.markdown("### Preview:")
+            st.markdown(jd_content)
+            
+            # Download button
+            st.download_button(
+                label="📥 Download Job Description",
+                data=doc_buffer,
+                file_name=f"{role.replace(' ', '_')}_JD.docx",
+                mime="application/vnd.openxmlformats-officedocument.wordprocessingml.document",
+                help="Download the job description in Word format"
+            )
+            
+        except Exception as e:
+            st.error(f"Error generating job description: {str(e)}")
 
-# Step 2: Select Role
-if department:
-    st.markdown('<div class="subheader">Step 2: Select Role</div>', unsafe_allow_html=True)
-    role = st.selectbox("Select the role:", departments[department])
-    
-    if role:
-        # Generate a prompt for the model
-        prompt = f"Generate a detailed and professional job description for a {role} in the {department} department."
-        
-        # Generate job description using the pre-trained model
-        with st.spinner("Generating job description..."):
-            generated_text = generator(prompt, max_length=300, num_return_sequences=1)[0]['generated_text']
-        
-        # Display job description
-        st.markdown('<div class="subheader">Generated Job Description</div>', unsafe_allow_html=True)
-        st.write(generated_text)
-        
-        # Create Word document
-        doc_io = create_word_document(role, generated_text)
-        
-        # Download button for Word document
-        st.download_button(
-            label="Download Job Description as Word",
-            data=doc_io,
-            file_name=f"{role.replace(' ', '_')}_job_description.docx",
-            mime="application/vnd.openxmlformats-officedocument.wordprocessingml.document"
-        )
-else:
-    st.warning("Please select a department and role to generate the job description.")
+# Instructions
+st.markdown("""
+### Instructions:
+1. Select the department from the first dropdown
+2. Choose the specific role from the second dropdown
+3. Click 'Generate Job Description'
+4. Review the generated description
+5. Download the Word document
+""")
+
+# Footer
+st.markdown("---")
+st.markdown("Built with ❤️ using Streamlit and Hugging Face")