Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,246 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import google.generativeai as genai
+from PIL import Image
+import fitz  # PyMuPDF
+from docx import Document
+import json
+from pathlib import Path
+from datetime import datetime
+import re
+import pytesseract
+import io
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+def extract_text_from_pdf(pdf_file):
+    """Extract text from uploaded PDF file."""
+    text_content = []
+    try:
+        pdf_bytes = pdf_file.read()
+        doc = fitz.open(stream=pdf_bytes, filetype="pdf")
+        for page_num in range(len(doc)):
+            page = doc[page_num]
+            text_content.append(page.get_text())
+        return "\n".join(text_content)
+    except Exception as e:
+        st.error(f"Error in PDF extraction: {str(e)}")
+        return ""
+
+def extract_text_from_docx(docx_file):
+    """Extract text from uploaded DOCX file."""
+    try:
+        doc = Document(docx_file)
+        text_content = []
+        for paragraph in doc.paragraphs:
+            text_content.append(paragraph.text)
+        return "\n".join(text_content)
+    except Exception as e:
+        st.error(f"Error in DOCX extraction: {str(e)}")
+        return ""
+
+def parse_date(date_str):
+    """Parse date from various formats."""
+    try:
+        # Handle 'Present' or 'Current'
+        if date_str.lower() in ['present', 'current', 'now']:
+            return datetime.now()
+
+        date_str = date_str.strip()
+
+        formats = [
+            '%Y', '%b %Y', '%B %Y', '%m/%Y', '%m-%Y',
+            '%Y/%m', '%Y-%m'
+        ]
+
+        for fmt in formats:
+            try:
+                return datetime.strptime(date_str, fmt)
+            except ValueError:
+                continue
+
+        year_match = re.search(r'\b20\d{2}\b', date_str)
+        if year_match:
+            return datetime.strptime(year_match.group(), '%Y')
+
+        return None
+    except Exception:
+        return None
+
+def calculate_experience(work_history):
+    """Calculate total years of experience from work history."""
+    total_experience = 0
+    current_year = datetime.now().year
+
+    for job in work_history:
+        duration = job.get('duration', '')
+        if not duration:
+            continue
+
+        parts = re.split(r'\s*-\s*|\s+to\s+', duration)
+        if len(parts) != 2:
+            continue
+
+        start_date = parse_date(parts[0])
+        end_date = parse_date(parts[1])
+
+        if start_date and end_date:
+            years = (end_date.year - start_date.year) + \
+                   (end_date.month - start_date.month) / 12
+            total_experience += max(0, years)
+
+    return round(total_experience, 1)
+
+def parse_resume(file_uploaded, api_key):
+    """Parse resume and extract information."""
+    genai.configure(api_key=api_key)
+    model = genai.GenerativeModel('gemini-1.5-flash')
+
+    prompt = """Extract the following information from this resume:
+    1. Summarize the following resume in 100 words, focusing on key skills, experience, and qualifications
+    2. Full Name
+    3. Email Address
+    4. Phone Number
+    5. Education History (including degree, institution, graduation year, and field of study)
+    6. Companies worked at with positions and EXACT duration (e.g., "Jan 2020 - Present" or "2018-2020")
+    7. Skills
+    8. LinkedIn Profile URL
+    Return the information in this JSON format:
+    {
+        "summary": "",
+        "name": "",
+        "email": "",
+        "phone": "",
+        "education": [
+            {
+                "degree": "",
+                "institution": "",
+                "year": "",
+                "field": "",
+                "gpa": ""
+            }
+        ],
+        "work_experience": [
+            {
+                "company": "",
+                "position": "",
+                "duration": ""
+            }
+        ],
+        "skills": [],
+        "linkedin": ""
+    }
+    For skills include tools and technologies in output if present any in resume.
+    For work experience durations, please specify exact dates in format: "MMM YYYY - MMM YYYY" or "YYYY - Present" , please return in one order either in ascending or descending.
+    Only return the JSON object, nothing else. If any field is not found, leave it empty."""
+
+    try:
+        file_extension = Path(file_uploaded.name).suffix.lower()
+        
+        if file_extension == '.pdf':
+            text_content = extract_text_from_pdf(file_uploaded)
+        elif file_extension in ['.docx', '.doc']:
+            text_content = extract_text_from_docx(file_uploaded)
+        elif file_extension in ['.jpg', '.jpeg', '.png']:
+            image = Image.open(file_uploaded)
+            text_content = pytesseract.image_to_string(image)
+        else:
+            st.error(f"Unsupported file format: {file_extension}")
+            return None
+
+        response = model.generate_content(f"{prompt}\n\nResume Text:\n{text_content}")
+        
+        try:
+            response_text = response.text
+            json_start = response_text.find('{')
+            json_end = response_text.rfind('}') + 1
+            json_str = response_text[json_start:json_end]
+            
+            result = json.loads(json_str)
+            total_exp = calculate_experience(result.get('work_experience', []))
+            result['total_years_experience'] = total_exp
+            
+            return result
+        except json.JSONDecodeError as e:
+            st.error(f"Error parsing response: {str(e)}")
+            return None
+
+    except Exception as e:
+        st.error(f"Error processing resume: {str(e)}")
+        return None
+
+def format_education(edu):
+    """Format education details for display."""
+    parts = []
+    if edu.get('degree'):
+        parts.append(edu['degree'])
+    if edu.get('field'):
+        parts.append(f"in {edu['field']}")
+    if edu.get('institution'):
+        parts.append(f"from {edu['institution']}")
+    if edu.get('year'):
+        parts.append(f"({edu['year']})")
+    if edu.get('gpa') and edu['gpa'].strip():
+        parts.append(f"- GPA: {edu['gpa']}")
+    return " ".join(parts)
+
+def main():
+    st.title("Resume Parser")
+    st.write("Upload a resume (PDF, DOCX, or Image) to extract information")
+
+    # Get API key from secrets or user input
+    api_key = st.secrets["GEMINI_API_KEY"] if "GEMINI_API_KEY" in st.secrets else st.text_input("Enter Gemini API Key", type="password")
+
+    uploaded_file = st.file_uploader("Choose a resume file", type=["pdf", "docx", "doc", "jpg", "jpeg", "png"])
+
+    if uploaded_file and api_key:
+        with st.spinner('Analyzing resume...'):
+            result = parse_resume(uploaded_file, api_key)
+
+            if result:
+                st.subheader("Extracted Information")
+                
+                # Display summary in a text area
+                st.text_area("Summary", result.get('summary', 'Not found'), height=100)
+                
+                # Display personal information
+                col1, col2, col3 = st.columns(3)
+                with col1:
+                    st.write("*Name:*", result.get('name', 'Not found'))
+                with col2:
+                    st.write("*Email:*", result.get('email', 'Not found'))
+                with col3:
+                    st.write("*Phone:*", result.get('phone', 'Not found'))
+
+                # Display total experience
+                total_exp = result.get('total_years_experience', 0)
+                exp_text = f"{total_exp:.1f} years" if total_exp >= 1 else f"{total_exp * 12:.0f} months"
+                st.write("*Total Experience:*", exp_text)
+
+                # Display education
+                st.subheader("Education")
+                if result.get('education'):
+                    for edu in result['education']:
+                        st.write(f"- {format_education(edu)}")
+                else:
+                    st.write("No education information found")
+
+                # Display work experience
+                st.subheader("Work Experience")
+                if result.get('work_experience'):
+                    for exp in result['work_experience']:
+                        duration = f" ({exp.get('duration', 'Duration not specified')})" if exp.get('duration') else ""
+                        st.write(f"- {exp.get('position', 'Role not found')} at {exp.get('company', 'Company not found')}{duration}")
+                else:
+                    st.write("No work experience found")
+
+                # Display Skills
+                st.subheader("Skills:")
+                if result.get('skills'):
+                    for skill in result['skills']:
+                        st.write(f"- {skill}")
+                else:
+                    st.write("- No skills found")
+
+                # Display LinkedIn profile
+                st.write("*LinkedIn Profile:*", result.get('linkedin', 'Not found'))
+
+if __name__ == "__main__":
+    main()