Add application file

UI.py

@@ -0,0 +1,178 @@
+import streamlit as st
+import requests
+from PIL import Image
+import io
+import base64
+import json
+
+# Set the backend URL
+BACKEND_URL = "https://abao77-cv-parsing.hf.space"
+
+# Initialize session state
+def init_session_state():
+    if "uploaded_image" not in st.session_state:
+        st.session_state["uploaded_image"] = None
+    if "processed_image" not in st.session_state:
+        st.session_state["processed_image"] = None
+    if "extracted_text" not in st.session_state:
+        st.session_state["extracted_text"] = None
+    if "reformatted_cv" not in st.session_state:
+        st.session_state["reformatted_cv"] = None
+    if "bounding_boxes" not in st.session_state:
+        st.session_state["bounding_boxes"] = None
+
+def main():
+    st.set_page_config(page_title="Resume Analyzer", layout="wide")
+    init_session_state()
+    
+    # Title and description
+    st.title("📄 Resume Analyzer & Job Matcher")
+    st.markdown("""
+    Upload your resume image, extract text, and match it with job descriptions.
+    """)
+
+    # Create tabs
+    tab1, tab2, tab3 = st.tabs(["📸 Extract Text", "📝 Format Resume", "🎯 Job Matching"])
+
+    # Tab 1: Extract Text
+    with tab1:
+        col1, col2 = st.columns([1, 1])
+        
+        with col1:
+            st.subheader("Upload Resume")
+            uploaded_file = st.file_uploader("Choose an image file", type=["jpg", "jpeg", "png"])
+            
+            if uploaded_file:
+                st.session_state["uploaded_image"] = uploaded_file
+                st.image(uploaded_file, caption="Uploaded Image", use_column_width=True)
+
+            if st.button("📷 Extract Text", type="primary"):
+                if st.session_state["uploaded_image"]:
+                    with st.spinner("Processing image..."):
+                        image_bytes = st.session_state["uploaded_image"].read()
+                        files = {
+                            "image": (
+                                st.session_state["uploaded_image"].name,
+                                image_bytes,
+                                st.session_state["uploaded_image"].type,
+                            )
+                        }
+                        data = {"threshold_confidence": 0.7, "threshold_iou": 0.7}
+                        
+                        try:
+                            response = requests.post(f"{BACKEND_URL}/inference", data=data, files=files)
+                            if response.status_code == 200:
+                                result = response.json()
+                                # Store processed image
+                                image_base64 = result.get("image_base64")
+                                if image_base64:
+                                    image_data = base64.b64decode(image_base64)
+                                    image = Image.open(io.BytesIO(image_data))
+                                    st.session_state["processed_image"] = image
+                                st.session_state["extracted_text"] = result.get("outputs")
+                                st.success("Text extracted successfully!")
+                            else:
+                                st.error(f"Error: {response.text}")
+                        except Exception as e:
+                            st.error(f"Error: {str(e)}")
+                else:
+                    st.warning("Please upload an image first.")
+
+        with col2:
+            if st.session_state["processed_image"]:
+                st.subheader("Processed Image")
+                st.image(st.session_state["processed_image"], caption="With Detected Text")
+
+            if st.session_state["extracted_text"]:
+                with st.expander("View Extracted Text", expanded=True):
+                    st.json(st.session_state["extracted_text"])
+
+    # Tab 2: Format Resume
+    with tab2:
+        st.subheader("Format Resume")
+        if st.session_state["extracted_text"] is None:
+            st.warning("Please extract text from an image first.")
+        else:
+            if st.button("🔄 Format Resume", type="primary"):
+                with st.spinner("Formatting resume..."):
+                    try:
+                        data = {"text": str(st.session_state["extracted_text"])}
+                        response = requests.post(f"{BACKEND_URL}/reformat_output", json=data)
+                        if response.status_code == 200:
+                            st.session_state["reformatted_cv"] = response.json()
+                            st.success("Resume formatted successfully!")
+                            
+                            # Display formatted sections
+                            col1, col2 = st.columns(2)
+                            with col1:
+                                with st.expander("📋 Personal Information", expanded=True):
+                                    if "contact" in st.session_state["reformatted_cv"]:
+                                        st.write(st.session_state["reformatted_cv"]["contact"])
+                                
+                                with st.expander("💼 Experience", expanded=True):
+                                    if "experience" in st.session_state["reformatted_cv"]:
+                                        st.write(st.session_state["reformatted_cv"]["experience"])
+                            
+                            with col2:
+                                with st.expander("🎯 Skills", expanded=True):
+                                    if "skills" in st.session_state["reformatted_cv"]:
+                                        st.write(st.session_state["reformatted_cv"]["skills"])
+                                
+                                with st.expander("🌟 Other Information", expanded=True):
+                                    if "interests" in st.session_state["reformatted_cv"]:
+                                        st.write("Interests:", st.session_state["reformatted_cv"]["interests"])
+                                    if "community" in st.session_state["reformatted_cv"]:
+                                        st.write("Community:", st.session_state["reformatted_cv"]["community"])
+                        else:
+                            st.error(f"Error: {response.text}")
+                    except Exception as e:
+                        st.error(f"Error: {str(e)}")
+
+    # Tab 3: Job Matching
+    with tab3:
+        st.subheader("Match with Job Description")
+        
+        job_description = st.text_area("📝 Paste Job Description", height=200)
+        
+        if st.button("🎯 Match Resume", type="primary"):
+            if job_description:
+                if st.session_state["reformatted_cv"] is None:
+                    st.warning("Formatting resume first...")
+                    # Auto-format if not already formatted
+                    data = {"text": str(st.session_state["extracted_text"])}
+                    response = requests.post(f"{BACKEND_URL}/reformat_output", json=data)
+                    if response.status_code == 200:
+                        st.session_state["reformatted_cv"] = response.json()
+                
+                # Proceed with job matching
+                with st.spinner("Analyzing match..."):
+                    try:
+                        data = {
+                            "text": json.dumps(st.session_state["reformatted_cv"]),
+                            "job_desciption": job_description
+                        }
+                        response = requests.post(f"{BACKEND_URL}/matching_job_desciption", json=data)
+                        
+                        if response.status_code == 200:
+                            result = response.json()
+                            
+                            # Display match results
+                            col1, col2 = st.columns([1, 2])
+                            with col1:
+                                st.metric("Match Score", f"{result['score']}%")
+                            
+                            with col2:
+                                st.markdown("### Match Analysis")
+                                st.write(result["reasoning"])
+                            
+                            with st.expander("View Detailed Analysis", expanded=False):
+                                st.json(result)
+                        else:
+                            st.error(f"Error: {response.text}")
+                    except Exception as e:
+                        st.error(f"Error: {str(e)}")
+            else:
+                st.warning("Please enter a job description.")
+
+if __name__ == "__main__":
+    main()

app.py

@@ -0,0 +1,178 @@
+import streamlit as st
+import requests
+from PIL import Image
+import io
+import base64
+import json
+
+# Set the backend URL
+BACKEND_URL = "https://abao77-cv-parsing.hf.space"
+
+# Initialize session state
+def init_session_state():
+    if "uploaded_image" not in st.session_state:
+        st.session_state["uploaded_image"] = None
+    if "processed_image" not in st.session_state:
+        st.session_state["processed_image"] = None
+    if "extracted_text" not in st.session_state:
+        st.session_state["extracted_text"] = None
+    if "reformatted_cv" not in st.session_state:
+        st.session_state["reformatted_cv"] = None
+    if "bounding_boxes" not in st.session_state:
+        st.session_state["bounding_boxes"] = None
+
+def main():
+    st.set_page_config(page_title="Resume Analyzer", layout="wide")
+    init_session_state()
+    
+    # Title and description
+    st.title("📄 Resume Analyzer & Job Matcher")
+    st.markdown("""
+    Upload your resume image, extract text, and match it with job descriptions.
+    """)
+
+    # Create tabs
+    tab1, tab2, tab3 = st.tabs(["📸 Extract Text", "📝 Format Resume", "🎯 Job Matching"])
+
+    # Tab 1: Extract Text
+    with tab1:
+        col1, col2 = st.columns([1, 1])
+        
+        with col1:
+            st.subheader("Upload Resume")
+            uploaded_file = st.file_uploader("Choose an image file", type=["jpg", "jpeg", "png"])
+            
+            if uploaded_file:
+                st.session_state["uploaded_image"] = uploaded_file
+                st.image(uploaded_file, caption="Uploaded Image", use_column_width=True)
+
+            if st.button("📷 Extract Text", type="primary"):
+                if st.session_state["uploaded_image"]:
+                    with st.spinner("Processing image..."):
+                        image_bytes = st.session_state["uploaded_image"].read()
+                        files = {
+                            "image": (
+                                st.session_state["uploaded_image"].name,
+                                image_bytes,
+                                st.session_state["uploaded_image"].type,
+                            )
+                        }
+                        data = {"threshold_confidence": 0.7, "threshold_iou": 0.7}
+                        
+                        try:
+                            response = requests.post(f"{BACKEND_URL}/inference", data=data, files=files)
+                            if response.status_code == 200:
+                                result = response.json()
+                                # Store processed image
+                                image_base64 = result.get("image_base64")
+                                if image_base64:
+                                    image_data = base64.b64decode(image_base64)
+                                    image = Image.open(io.BytesIO(image_data))
+                                    st.session_state["processed_image"] = image
+                                st.session_state["extracted_text"] = result.get("outputs")
+                                st.success("Text extracted successfully!")
+                            else:
+                                st.error(f"Error: {response.text}")
+                        except Exception as e:
+                            st.error(f"Error: {str(e)}")
+                else:
+                    st.warning("Please upload an image first.")
+
+        with col2:
+            if st.session_state["processed_image"]:
+                st.subheader("Processed Image")
+                st.image(st.session_state["processed_image"], caption="With Detected Text")
+
+            if st.session_state["extracted_text"]:
+                with st.expander("View Extracted Text", expanded=True):
+                    st.json(st.session_state["extracted_text"])
+
+    # Tab 2: Format Resume
+    with tab2:
+        st.subheader("Format Resume")
+        if st.session_state["extracted_text"] is None:
+            st.warning("Please extract text from an image first.")
+        else:
+            if st.button("🔄 Format Resume", type="primary"):
+                with st.spinner("Formatting resume..."):
+                    try:
+                        data = {"text": str(st.session_state["extracted_text"])}
+                        response = requests.post(f"{BACKEND_URL}/reformat_output", json=data)
+                        if response.status_code == 200:
+                            st.session_state["reformatted_cv"] = response.json()
+                            st.success("Resume formatted successfully!")
+                            
+                            # Display formatted sections
+                            col1, col2 = st.columns(2)
+                            with col1:
+                                with st.expander("📋 Personal Information", expanded=True):
+                                    if "contact" in st.session_state["reformatted_cv"]:
+                                        st.write(st.session_state["reformatted_cv"]["contact"])
+                                
+                                with st.expander("💼 Experience", expanded=True):
+                                    if "experience" in st.session_state["reformatted_cv"]:
+                                        st.write(st.session_state["reformatted_cv"]["experience"])
+                            
+                            with col2:
+                                with st.expander("🎯 Skills", expanded=True):
+                                    if "skills" in st.session_state["reformatted_cv"]:
+                                        st.write(st.session_state["reformatted_cv"]["skills"])
+                                
+                                with st.expander("🌟 Other Information", expanded=True):
+                                    if "interests" in st.session_state["reformatted_cv"]:
+                                        st.write("Interests:", st.session_state["reformatted_cv"]["interests"])
+                                    if "community" in st.session_state["reformatted_cv"]:
+                                        st.write("Community:", st.session_state["reformatted_cv"]["community"])
+                        else:
+                            st.error(f"Error: {response.text}")
+                    except Exception as e:
+                        st.error(f"Error: {str(e)}")
+
+    # Tab 3: Job Matching
+    with tab3:
+        st.subheader("Match with Job Description")
+        
+        job_description = st.text_area("📝 Paste Job Description", height=200)
+        
+        if st.button("🎯 Match Resume", type="primary"):
+            if job_description:
+                if st.session_state["reformatted_cv"] is None:
+                    st.warning("Formatting resume first...")
+                    # Auto-format if not already formatted
+                    data = {"text": str(st.session_state["extracted_text"])}
+                    response = requests.post(f"{BACKEND_URL}/reformat_output", json=data)
+                    if response.status_code == 200:
+                        st.session_state["reformatted_cv"] = response.json()
+                
+                # Proceed with job matching
+                with st.spinner("Analyzing match..."):
+                    try:
+                        data = {
+                            "text": json.dumps(st.session_state["reformatted_cv"]),
+                            "job_desciption": job_description
+                        }
+                        response = requests.post(f"{BACKEND_URL}/matching_job_desciption", json=data)
+                        
+                        if response.status_code == 200:
+                            result = response.json()
+                            
+                            # Display match results
+                            col1, col2 = st.columns([1, 2])
+                            with col1:
+                                st.metric("Match Score", f"{result['score']}%")
+                            
+                            with col2:
+                                st.markdown("### Match Analysis")
+                                st.write(result["reasoning"])
+                            
+                            with st.expander("View Detailed Analysis", expanded=False):
+                                st.json(result)
+                        else:
+                            st.error(f"Error: {response.text}")
+                    except Exception as e:
+                        st.error(f"Error: {str(e)}")
+            else:
+                st.warning("Please enter a job description.")
+
+if __name__ == "__main__":
+    main()