Add HF Spaces deployment and fix broken app

- Fix LangChain imports (langchain_classic for ConversationChain/Memory)
- Fix variable scoping bugs and duplicate methods
- Make JD optional, support text input for JD
- Add app.py + requirements.txt + README metadata for HF Spaces
- Add .streamlit config at project root

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

Code+Folder/src/resume_suggestions.py

@@ -29,71 +29,77 @@ st.set_page_config(page_title="Resume Reviewer")
 llm = None
 resume_chain = None
 
-# Add API key input in the sidebar
+# Initialize variables
+resume_content = None
+job_description_content = None
+
+# Sidebar
 with st.sidebar:
     st.title('Resume Reviewer')
-    st.write("Upload your resume and JD for my recommendations.")
-    
-    # API Key handling - cleaner UI
-    st.write("---")  # Add a separator
+    st.write("Upload your resume for my recommendations. Job description is optional.")
+
+    # API Key
+    st.write("---")
     st.write("### OpenAI API Key")
     st.write("Enter your API key to use the application. Get one at [OpenAI](https://platform.openai.com/api-keys)")
     api_key = st.text_input("API Key", type="password", help="Your API key will not be stored")
-    
+
     if api_key:
         os.environ['OPENAI_API_KEY'] = api_key
         llm = ChatOpenAI(temperature=0.0, model=OPENAI_MODEL_NAME)
     else:
-        st.info("👆 Please enter your OpenAI API key to start")
+        st.info("Please enter your OpenAI API key to start")
         llm = None
-    
-    # Add upload method selection
-    upload_method = st.radio("Choose input method:", ["File Upload", "Text Input"])
-
-# Initialize variables to avoid NameError
-resume_file = None
-jd_file = None
-resume_content = None
-job_description_content = None
 
-with st.sidebar:
-    if upload_method == "File Upload":
-        st.write("Note: File size should be less than 5MB")
-        resume_file = st.file_uploader("Upload your resume (pdf file only)", type=["pdf"], accept_multiple_files=False)
-        jd_file = st.file_uploader("Upload your JD (txt file only)", type=["txt"], accept_multiple_files=False)
-    else:
-        resume_text = st.text_area("Paste your resume content here:", height=200)
-        jd_text = st.text_area("Paste your job description here:", height=200)
+    # Resume upload (file only)
+    st.write("---")
+    st.write("### Resume")
+    st.write("Note: File size should be less than 5MB")
+    resume_file = st.file_uploader("Upload your resume (PDF)", type=["pdf"], accept_multiple_files=False)
 
-        if resume_text and jd_text:
-            resume_content = resume_text
-            job_description_content = jd_text
-            st.sidebar.success("Text content received successfully!")
+    # JD input (file or text, optional)
+    st.write("---")
+    st.write("### Job Description (optional)")
+    jd_file = st.file_uploader("Upload a JD (txt file)", type=["txt"], accept_multiple_files=False)
+    jd_text = st.text_area("Or paste the job description here:", height=150)
 
-if upload_method == "File Upload" and resume_file is not None and jd_file is not None and api_key:
+# Process resume
+if resume_file is not None and api_key:
     try:
         with st.spinner("Processing resume file..."):
             directory_reader = DirectoryReader("", "")
             resume_content = directory_reader.extract_text_from_pdf(resume_file)
             st.sidebar.success("Resume processed successfully!")
-
-        with st.spinner("Processing job description file..."):
-            if jd_file.type == 'text/plain':
-                try:
-                    from io import StringIO
-                    stringio = StringIO(jd_file.getvalue().decode('utf-8'))
-                    read_data = stringio.read()
-                    job_description_content = read_data
-                    st.sidebar.success("JD processed successfully!")
-                except Exception as e:
-                    st.sidebar.error(f"Error processing JD file: {str(e)}")
-                    job_description_content = None
     except Exception as e:
         st.sidebar.error(f"Error processing resume file: {str(e)}")
         resume_content = None
-        job_description_content = None
 
-SYSTEM_PROMPT = "\n\n" + TEMPLATE_CONTENT + "<RESUME STARTS HERE> {}. <RESUME ENDS HERE> with the job description: <JOB DESCRIPTION STARTS HERE> {}.<JOB DESCRIPTION ENDS HERE>\n\nBe crisp and clear in response.DO NOT provide the resume and job description in the response\n\n".format(resume_content, job_description_content)
+# Process JD - prefer file upload, fall back to text input
+if jd_file is not None:
+    try:
+        from io import StringIO
+        stringio = StringIO(jd_file.getvalue().decode('utf-8'))
+        job_description_content = stringio.read()
+        st.sidebar.success("JD processed successfully!")
+    except Exception as e:
+        st.sidebar.error(f"Error processing JD file: {str(e)}")
+elif jd_text:
+    job_description_content = jd_text
+
+# Build system prompt based on what's provided
+if resume_content and job_description_content:
+    SYSTEM_PROMPT = "\n\n" + TEMPLATE_CONTENT + \
+        "<RESUME STARTS HERE> {}. <RESUME ENDS HERE> with the job description: " \
+        "<JOB DESCRIPTION STARTS HERE> {}.<JOB DESCRIPTION ENDS HERE>\n\n" \
+        "Be crisp and clear in response. DO NOT provide the resume and job description in the response.\n\n".format(
+            resume_content, job_description_content)
+elif resume_content:
+    SYSTEM_PROMPT = "\n\n" + TEMPLATE_CONTENT + \
+        "<RESUME STARTS HERE> {}. <RESUME ENDS HERE>\n\n" \
+        "No job description was provided. Focus on general resume feedback, strengths, and areas for improvement. " \
+        "Be crisp and clear in response. DO NOT provide the resume in the response.\n\n".format(resume_content)
+else:
+    SYSTEM_PROMPT = "\n\n" + TEMPLATE_CONTENT
 
 
 # Store LLM generated responses
@@ -133,18 +139,28 @@ def clear_chat_history():
 def generate_report():
     user_message = {"role": "user", "content": "Generate a Report!"}
     st.session_state.messages.append(user_message)
-    if resume_content is not None and job_description_content is not None:
-        with st.chat_message("assistant"):
-            with st.spinner("Just a moment..."):
+    if resume_content is None:
+        st.error("Please upload a resume first!")
+        return
+
+    with st.chat_message("assistant"):
+        with st.spinner("Just a moment..."):
+            resume_analysis = generate_response(resume_analysis_prompt.format(resume_content))
+            resume_formatting_analysis = generate_response(
+                resume_formatting_prompt.format(resume_content, "N/A"))
 
-                comparison_analysis = generate_response(comparison_prompt.format(resume_content, job_description_content))
-                resume_analysis = generate_response(resume_analysis_prompt.format(resume_content))
+            report = f"**Resume Analysis:**\n{resume_analysis}\n\n" \
+                     f"**Resume Formatting:**\n{resume_formatting_analysis}"
+
+            if job_description_content is not None:
+                comparison_analysis = generate_response(
+                    comparison_prompt.format(resume_content, job_description_content))
                 job_description_analysis = generate_response(
                     job_description_analysis_prompt.format(job_description_content))
-                gap_analysis = generate_response(gap_analysis_prompt.format(resume_content,
-                                                                            job_description_content))
-                actionable_steps_analysis = generate_response(actionable_steps_prompt.format(
-                    resume_content, job_description_content))
+                gap_analysis = generate_response(
+                    gap_analysis_prompt.format(resume_content, job_description_content))
+                actionable_steps_analysis = generate_response(
+                    actionable_steps_prompt.format(resume_content, job_description_content))
                 experience_enhancement_analysis = generate_response(
                     experience_enhancement_prompt.format(resume_content, job_description_content))
                 additional_qualifications_analysis = generate_response(
@@ -153,28 +169,21 @@ def generate_report():
                     resume_tailoring_prompt.format(resume_content, job_description_content))
                 relevant_skills_highlight_analysis = generate_response(
                     relevant_skills_highlight_prompt.format(resume_content, job_description_content))
-                resume_formatting_analysis = generate_response(
-                    resume_formatting_prompt.format(resume_content, job_description_content))
                 resume_length_analysis = generate_response(
                     resume_length_prompt.format(resume_content, job_description_content))
 
-                # Compile the report
-                report = f"Comparison Analysis:\n{comparison_analysis}\n\n" \
-                         f"Resume Analysis:\n{resume_analysis}\n\n" \
-                         f"Job Description Analysis:\n{job_description_analysis}\n" \
-                         f"\nGap Analysis:\n{gap_analysis} \n\n" \
-                         f"Actionable Steps:\n{actionable_steps_analysis}\n\n" \
-                         f"Experience Enhancement:\n{experience_enhancement_analysis}\n\n" \
-                         f"Additional Qualifications:\n{additional_qualifications_analysis}\n\n" \
-                         f"Resume Tailoring:\n{resume_tailoring_analysis}\n\n" \
-                         f"Relevant Skills Highlight:\n{relevant_skills_highlight_analysis}\n\n" \
-                         f"Resume Formatting:\n{resume_formatting_analysis}\n\n" \
-                         f"Resume Length:\n{resume_length_analysis} "
-
-        report_message = {"role": "assistant", "content": report}
-        st.session_state.messages.append(report_message)
-    else:
-        st.error("Please upload a resume and enter a job description!")
+                report += f"\n\n**Comparison Analysis:**\n{comparison_analysis}\n\n" \
+                          f"**Job Description Analysis:**\n{job_description_analysis}\n\n" \
+                          f"**Gap Analysis:**\n{gap_analysis}\n\n" \
+                          f"**Actionable Steps:**\n{actionable_steps_analysis}\n\n" \
+                          f"**Experience Enhancement:**\n{experience_enhancement_analysis}\n\n" \
+                          f"**Additional Qualifications:**\n{additional_qualifications_analysis}\n\n" \
+                          f"**Resume Tailoring:**\n{resume_tailoring_analysis}\n\n" \
+                          f"**Relevant Skills Highlight:**\n{relevant_skills_highlight_analysis}\n\n" \
+                          f"**Resume Length:**\n{resume_length_analysis}"
+
+    report_message = {"role": "assistant", "content": report}
+    st.session_state.messages.append(report_message)
 
 
 # Setup the system message and prompt template

README.md

@@ -0,0 +1,28 @@
+---
+title: Resume Reviewer
+emoji: 📄
+colorFrom: blue
+colorTo: indigo
+sdk: streamlit
+sdk_version: "1.54.0"
+app_file: app.py
+pinned: false
+---
+
+# Resume Reviewer
+
+AI-powered resume analyzer that reviews your resume and compares it against job descriptions using OpenAI.
+
+## Features
+
+- Upload a PDF resume for instant analysis
+- Optionally provide a job description (file or text) for targeted feedback
+- Get a full report: gap analysis, tailoring suggestions, formatting tips, and more
+- Chat with the AI for follow-up questions about your resume
+
+## Usage
+
+1. Enter your OpenAI API key in the sidebar
+2. Upload your resume (PDF)
+3. Optionally add a job description (upload .txt or paste text)
+4. Click **Generate Report** or ask questions in the chat

app.py

@@ -0,0 +1,254 @@
+import os
+import sys
+
+# Add source directory to path so sibling imports work
+sys.path.insert(0, os.path.join(os.path.dirname(os.path.abspath(__file__)), "Code+Folder", "src"))
+
+import streamlit as st
+
+try:
+    from streamlit_feedback import streamlit_feedback
+    FEEDBACK_AVAILABLE = True
+except ImportError:
+    FEEDBACK_AVAILABLE = False
+
+from langchain_classic.chains import ConversationChain
+from langchain_classic.memory import ConversationBufferWindowMemory
+from langchain_core.messages import SystemMessage
+from langchain_core.prompts import ChatPromptTemplate, HumanMessagePromptTemplate
+from langchain_openai import ChatOpenAI
+from constants import OPENAI_MODEL_NAME, TEMPLATE_CONTENT, comparison_prompt, resume_analysis_prompt, \
+    job_description_analysis_prompt, gap_analysis_prompt, actionable_steps_prompt, experience_enhancement_prompt, \
+    additional_qualifications_prompt, resume_tailoring_prompt, relevant_skills_highlight_prompt, \
+    resume_formatting_prompt, resume_length_prompt
+from directory_reader import DirectoryReader
+
+st.set_page_config(page_title="Resume Reviewer")
+
+# Initialize llm as None at the top level
+llm = None
+resume_chain = None
+
+# Initialize variables
+resume_content = None
+job_description_content = None
+
+# Sidebar
+with st.sidebar:
+    st.title('Resume Reviewer')
+    st.write("Upload your resume for my recommendations. Job description is optional.")
+
+    # API Key
+    st.write("---")
+    st.write("### OpenAI API Key")
+    st.write("Enter your API key to use the application. Get one at [OpenAI](https://platform.openai.com/api-keys)")
+    api_key = st.text_input("API Key", type="password", help="Your API key will not be stored")
+
+    if api_key:
+        os.environ['OPENAI_API_KEY'] = api_key
+        llm = ChatOpenAI(temperature=0.0, model=OPENAI_MODEL_NAME)
+    else:
+        st.info("Please enter your OpenAI API key to start")
+        llm = None
+
+    # Resume upload (file only)
+    st.write("---")
+    st.write("### Resume")
+    st.write("Note: File size should be less than 5MB")
+    resume_file = st.file_uploader("Upload your resume (PDF)", type=["pdf"], accept_multiple_files=False)
+
+    # JD input (file or text, optional)
+    st.write("---")
+    st.write("### Job Description (optional)")
+    jd_file = st.file_uploader("Upload a JD (txt file)", type=["txt"], accept_multiple_files=False)
+    jd_text = st.text_area("Or paste the job description here:", height=150)
+
+# Process resume
+if resume_file is not None and api_key:
+    try:
+        with st.spinner("Processing resume file..."):
+            directory_reader = DirectoryReader("", "")
+            resume_content = directory_reader.extract_text_from_pdf(resume_file)
+            st.sidebar.success("Resume processed successfully!")
+    except Exception as e:
+        st.sidebar.error(f"Error processing resume file: {str(e)}")
+        resume_content = None
+
+# Process JD - prefer file upload, fall back to text input
+if jd_file is not None:
+    try:
+        from io import StringIO
+        stringio = StringIO(jd_file.getvalue().decode('utf-8'))
+        job_description_content = stringio.read()
+        st.sidebar.success("JD processed successfully!")
+    except Exception as e:
+        st.sidebar.error(f"Error processing JD file: {str(e)}")
+elif jd_text:
+    job_description_content = jd_text
+
+# Build system prompt based on what's provided
+if resume_content and job_description_content:
+    SYSTEM_PROMPT = "\n\n" + TEMPLATE_CONTENT + \
+        "<RESUME STARTS HERE> {}. <RESUME ENDS HERE> with the job description: " \
+        "<JOB DESCRIPTION STARTS HERE> {}.<JOB DESCRIPTION ENDS HERE>\n\n" \
+        "Be crisp and clear in response. DO NOT provide the resume and job description in the response.\n\n".format(
+            resume_content, job_description_content)
+elif resume_content:
+    SYSTEM_PROMPT = "\n\n" + TEMPLATE_CONTENT + \
+        "<RESUME STARTS HERE> {}. <RESUME ENDS HERE>\n\n" \
+        "No job description was provided. Focus on general resume feedback, strengths, and areas for improvement. " \
+        "Be crisp and clear in response. DO NOT provide the resume in the response.\n\n".format(resume_content)
+else:
+    SYSTEM_PROMPT = "\n\n" + TEMPLATE_CONTENT
+
+
+# Store LLM generated responses
+if "messages" not in st.session_state.keys():
+    st.session_state.messages = [{"role": "assistant", "content": "How may I assist you today?"}]
+
+# Display or clear chat messages
+for message in st.session_state.messages:
+    if message["role"] != "feedback":
+        with st.chat_message(message["role"]):
+            st.write(message["content"])
+
+
+def clear_chat_history():
+    global resume_chain
+    st.session_state.messages = [{"role": "assistant", "content": "How may I help you today?"}]
+    if llm is not None:
+        resume_chain = ConversationChain(
+            llm=llm,
+            prompt=prompt_template,
+            memory=memory,
+            verbose=False
+        )
+
+
+def generate_report():
+    user_message = {"role": "user", "content": "Generate a Report!"}
+    st.session_state.messages.append(user_message)
+    if resume_content is None:
+        st.error("Please upload a resume first!")
+        return
+
+    with st.chat_message("assistant"):
+        with st.spinner("Just a moment..."):
+            resume_analysis = generate_response(resume_analysis_prompt.format(resume_content))
+            resume_formatting_analysis = generate_response(
+                resume_formatting_prompt.format(resume_content, "N/A"))
+
+            report = f"**Resume Analysis:**\n{resume_analysis}\n\n" \
+                     f"**Resume Formatting:**\n{resume_formatting_analysis}"
+
+            if job_description_content is not None:
+                comparison_analysis = generate_response(
+                    comparison_prompt.format(resume_content, job_description_content))
+                job_description_analysis = generate_response(
+                    job_description_analysis_prompt.format(job_description_content))
+                gap_analysis = generate_response(
+                    gap_analysis_prompt.format(resume_content, job_description_content))
+                actionable_steps_analysis = generate_response(
+                    actionable_steps_prompt.format(resume_content, job_description_content))
+                experience_enhancement_analysis = generate_response(
+                    experience_enhancement_prompt.format(resume_content, job_description_content))
+                additional_qualifications_analysis = generate_response(
+                    additional_qualifications_prompt.format(resume_content, job_description_content))
+                resume_tailoring_analysis = generate_response(
+                    resume_tailoring_prompt.format(resume_content, job_description_content))
+                relevant_skills_highlight_analysis = generate_response(
+                    relevant_skills_highlight_prompt.format(resume_content, job_description_content))
+                resume_length_analysis = generate_response(
+                    resume_length_prompt.format(resume_content, job_description_content))
+
+                report += f"\n\n**Comparison Analysis:**\n{comparison_analysis}\n\n" \
+                          f"**Job Description Analysis:**\n{job_description_analysis}\n\n" \
+                          f"**Gap Analysis:**\n{gap_analysis}\n\n" \
+                          f"**Actionable Steps:**\n{actionable_steps_analysis}\n\n" \
+                          f"**Experience Enhancement:**\n{experience_enhancement_analysis}\n\n" \
+                          f"**Additional Qualifications:**\n{additional_qualifications_analysis}\n\n" \
+                          f"**Resume Tailoring:**\n{resume_tailoring_analysis}\n\n" \
+                          f"**Relevant Skills Highlight:**\n{relevant_skills_highlight_analysis}\n\n" \
+                          f"**Resume Length:**\n{resume_length_analysis}"
+
+    report_message = {"role": "assistant", "content": report}
+    st.session_state.messages.append(report_message)
+
+
+# Setup the system message and prompt template
+system_message = SystemMessage(content=TEMPLATE_CONTENT)
+human_message = HumanMessagePromptTemplate.from_template("{history} User:{input} Assistant:")
+prompt_template = ChatPromptTemplate(messages=[system_message, human_message])
+memory = ConversationBufferWindowMemory(k=2)
+
+# Initialize the chain if llm is available
+if llm is not None:
+    resume_chain = ConversationChain(
+        llm=llm,
+        prompt=prompt_template,
+        memory=memory,
+        verbose=False
+    )
+
+def generate_response(prompt_input):
+    if resume_chain is None:
+        return "Please enter your OpenAI API key to use this application"
+    output = resume_chain.predict(input=prompt_input)
+    return output
+
+
+st.sidebar.button('Clear Chat History', on_click=clear_chat_history)
+st.sidebar.button('Generate Report', on_click=generate_report)
+
+
+def get_feedback():
+    st.session_state.messages.append({"role": "feedback", "content": st.session_state.fbk})
+
+
+# At the beginning of your script, initialize the prompt in session state
+if "current_prompt" not in st.session_state:
+    st.session_state.current_prompt = ""
+
+# When user enters a prompt
+if prompt := st.chat_input():
+    st.session_state.current_prompt = prompt
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    with st.chat_message("user"):
+        st.write(prompt)
+
+
+def get_llm_response():
+    with st.chat_message("assistant"):
+        with st.spinner("Thinking..."):
+            response = generate_response(st.session_state.current_prompt + SYSTEM_PROMPT)
+            placeholder = st.empty()
+            placeholder.markdown(response)
+            full_response = response
+    message = {"role": "assistant", "content": full_response}
+    st.session_state.messages.append(message)
+
+    # Only show feedback form if the feature is available
+    if FEEDBACK_AVAILABLE:
+        with st.form("form"):
+            streamlit_feedback(feedback_type="thumbs", optional_text_label="[Optional] Please provide an explanation", key="fbk")
+            st.form_submit_button('Save feedback', on_click=get_feedback)
+
+
+# Generate a new response if last message is not from assistant
+if st.session_state.messages[-1]["role"] not in ["assistant", "feedback"]:
+    get_llm_response()
+
+if st.session_state.messages[-1]["role"] in ["feedback"]:
+    try:
+        feedback_response = st.session_state.messages[-1]["content"]
+        score_mappings = {
+            "thumbs": {"thumbs_up": 1, "thumbs_down": 0},
+        }
+        score = score_mappings[feedback_response["type"]][feedback_response["score"]]
+        if score == 0:
+            feedback = st.session_state.messages[-1]["content"]['text']
+            prompt = "Please respond according to feedback '{0}' on the previous response on \n".format(feedback) \
+                     + st.session_state.messages[-3]["content"]
+            get_llm_response()
+    except:
+        pass

requirements.txt

@@ -0,0 +1,15 @@
+streamlit>=1.31.0
+langchain>=0.3.0
+langchain-classic>=1.0.0
+langchain-community>=0.3.0
+langchain-core>=0.3.0
+langchain-openai>=0.2.0
+openai>=1.10.0
+streamlit-feedback>=0.1.3
+PyPDF2>=3.0.1
+pypdf>=3.17.1
+numpy>=1.26.3
+pandas>=2.1.4
+scikit-learn>=1.3.2
+tqdm>=4.66.1
+python-dotenv>=1.0.0