Update Resume_Analyser.py (#135)

### Description

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also include relevant motivation and context. List any dependencies that
are required for this change.

Fixes # (issue)

### Type of change

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- [ ] Bug fix (non-breaking change which fixes an issue)
- [ ] New feature (non-breaking change which adds functionality)
- [ ] Breaking change (fix or feature that would cause existing
functionality to not work as expected)
- [ ] This change requires a documentation update


### Checklist:

- [ ] My code follows the style guidelines of this project
- [ ] I have performed a self-review of my own code
- [ ] I have commented my code, particularly in hard-to-understand areas
- [ ] I have made corresponding changes to the documentation
- [ ] My changes generate no new warnings
- [ ] I have added tests that prove my fix is effective or that my
feature works
- [ ] New and existing unit tests pass locally with my changes
- [ ] Any dependent changes have been merged and published in downstream
modules

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### Additional Notes

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menu/Resume_Analyser.py

@@ -4,49 +4,28 @@
 import os
 from langchain_google_genai import GoogleGenerativeAIEmbeddings
 import google.generativeai as genai
-from langchain_community.vectorstores import FAISS
+from langchain.vectorstores import FAISS
 from langchain_google_genai import ChatGoogleGenerativeAI
 from langchain.chains.question_answering import load_qa_chain
 from langchain.prompts import PromptTemplate
 from dotenv import load_dotenv
-from streamlit_lottie import st_lottie
+from streamlit_lottie import st_lottie 
 import json
-import asyncio
-import requests
-import hashlib
+import pickle
 
-# Load environment variables
 load_dotenv()
 
-# Configure the API key for Google Generative AI
+os.getenv("GOOGLE_API_KEY")
 genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
 
-# Centralized configuration
-CONFIG = {
-    "headline": "Resume Analyzer",
-    "animation_path": 'src/Resume.json',
-    "example_skills": ["Python", "Data Analysis"],  # Example skills
-    "prompt_template": """
-        You are an Advanced resume Analyzer.
-        1. Analyze the resume and give the best 3 job domains relevant to the skills in the given context.
-        2. Based on those job domains, separately suggest more skills and best courses from YouTube.
-        3. Suggest improvements in the resume.
-        4. Use bullet points, tables, and keep the text more interactive.
-        5. Ensure the provided YouTube links are working and are the latest.
-
-        Context:\n {context}?\n
-
-        Answer:
-    """
-}
+
 
 def get_pdf_text(pdf_docs):
     text = ""
     for pdf in pdf_docs:
         pdf_reader = PdfReader(pdf)
         for page in pdf_reader.pages:
-            if page.extract_text():
-                text += page.extract_text()
+            text += page.extract_text()
     return text
 
 def get_text_chunks(text):
@@ -57,134 +36,95 @@ def get_text_chunks(text):
 def get_vector_store(text_chunks):
     embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
     vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)
-    vector_store.save_local("faiss_index")
+    with open("ResumeAnz.pkl", "wb") as f:
+        pickle.dump(vector_store, f)
+
+def get_conversational_chain():
+    prompt_template = """
+    You are an Advance resume Analyzer.
+    1. analyse the resume and give the best 3 Job Domain relavent to skills in given context.
+    2. based on that job domains seprately suggest more skill and best cources from youtube.
+    3. Suggest the improvememnt in resume.
+    4. Use bullet points, tables and keep text more interactive
+    5. make sure that the provide youtube links are working and are latest.
+    
+    Context:\n {context}?\n
 
-def load_vector_store():
-    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
-    vector_store = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True)
-    return vector_store
 
-async def get_conversational_chain():
-    prompt_template = CONFIG["prompt_template"]
+    Answer:
+    """
+
     model = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.3)
+
     prompt = PromptTemplate(template=prompt_template, input_variables=["context"])
     chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
 
     return chain
 
 def user_input(user_question):
-    try:
-        vector_store = load_vector_store()
-        docs = vector_store.similarity_search(user_question)
-
-        # Create an event loop and run the asynchronous function
-        loop = asyncio.new_event_loop()
-        asyncio.set_event_loop(loop)
-        chain = loop.run_until_complete(get_conversational_chain())
-
-        response = chain(
-            {"input_documents": docs},
-            return_only_outputs=True
-        )
-
-        st.session_state.output_text = response["output_text"]
-        st.write("Reply: ", st.session_state.output_text)
-    except Exception as e:
-        st.error(f"An error occurred: {e}")
-
-def get_youtube_links(skills):
-    api_key = os.getenv("YOUTUBE_API_KEY")
-    search_url = "https://www.googleapis.com/youtube/v3/search"
-    video_urls = []
-
-    for skill in skills:
-        params = {
-            "part": "snippet",
-            "q": f"{skill} course",
-            "key": api_key,
-            "maxResults": 3,
-            "type": "video"
-        }
-        response = requests.get(search_url, params=params)
-        if response.status_code == 200:
-            videos = response.json().get("items", [])
-            for video in videos:
-                video_urls.append(f"https://www.youtube.com/watch?v={video['id']['videoId']}")
-        else:
-            st.error(f"Failed to fetch YouTube videos for {skill}")
-
-    return video_urls
-
-def cache_data(func):
-    cache_dir = "cache"
-    os.makedirs(cache_dir, exist_ok=True)
-
-    def wrapper(*args, **kwargs):
-        cache_key = hashlib.md5(str(args).encode()).hexdigest()
-        cache_file = os.path.join(cache_dir, cache_key)
-
-        if os.path.exists(cache_file):
-            with open(cache_file, "r") as f:
-                return json.load(f)
-        else:
-            result = func(*args, **kwargs)
-            with open(cache_file, "w") as f:
-                json.dump(result, f)
-            return result
-
-    return wrapper
-
-@cache_data
-def get_resume_analysis(text):
-    text_chunks = get_text_chunks(text)
-    get_vector_store(text_chunks)
-    vector_store = load_vector_store()
-    docs = vector_store.similarity_search(text)
+    with open("ResumeAnz.pkl", "rb") as f:
+        new_db = pickle.load(f)
 
-    loop = asyncio.new_event_loop()
-    asyncio.set_event_loop(loop)
-    chain = loop.run_until_complete(get_conversational_chain())
+    docs = new_db.similarity_search(user_question)
+
+    chain = get_conversational_chain()
 
     response = chain(
         {"input_documents": docs},
         return_only_outputs=True
     )
-
-    return response["output_text"]
+
+    st.session_state.output_text = response["output_text"]
+    st.write("Reply: ", st.session_state.output_text)
 
 def main():
-    #st.set_page_config(page_title=CONFIG["headline"], page_icon=":memo:")
-    st.title(CONFIG["headline"])
+
+    with open('src/Resume.json', encoding='utf-8') as anim_source:
+        animation = json.load(anim_source)
+    st_lottie(animation, 1, True, True, "high", 200, -200)
+
+
+
+    st.header("Resume Analyser")
+
+    if 'pdf_docs' not in st.session_state:
+        st.session_state.pdf_docs = None
 
-    try:
-        with open(CONFIG["animation_path"], encoding='utf-8') as anim_source:
-            animation = json.load(anim_source)
-        st_lottie(animation, height=200, width=400)
-    except FileNotFoundError:
-        st.warning("Animation file not found.")
+    if 'output_text' not in st.session_state:
+        st.session_state.output_text = ""
 
-    pdf_docs = st.file_uploader("Upload your PDF Files", accept_multiple_files=True)
+    if 'prompt_selected' not in st.session_state:
+        st.session_state.prompt_selected = ""
+
+    pdf_docs = st.file_uploader("Upload your PDF Files and Click on the Submit & Process Button", accept_multiple_files=True)
 
     if st.button("Process"):
         if pdf_docs:
-            with st.spinner("Analyzing..."):
-                try:
-                    raw_text = get_pdf_text(pdf_docs)
-                    if raw_text:
-                        analysis_result = get_resume_analysis(raw_text)
-                        st.session_state.output_text = analysis_result
-                        st.write("Reply: ", st.session_state.output_text)
-
-                        skills = CONFIG["example_skills"]  # Example skills; extract from analysis result
-                        youtube_links = get_youtube_links(skills)
-                        st.divider()
-                        st.text("Additional Courses:")
-                        for link in youtube_links:
-                            st.video(link)
-                    else:
-                        st.warning("No text found in the uploaded PDFs.")
-                except Exception as e:
-                    st.error(f"An error occurred during processing: {e}")
+            with st.spinner("Analysing..."):
+                raw_text = get_pdf_text(pdf_docs)
+                text_chunks = get_text_chunks(raw_text)
+                get_vector_store(text_chunks)
+                user_input(raw_text)  # Call user_input function with raw_text
+
+
+        #Additional Courses
+        st.divider()
+        st.text("Additional Courses: ")
+        st.video('https://www.youtube.com/watch?v=JxgmHe2NyeY&t')
+        st.divider()
+        st.video('https://www.youtube.com/watch?v=5NQjLBuNL0I')
+        st.divider()
+
+
+
+        st.link_button('LinkedIn', "https://www.linkedin.com/in/surajsanap01")
+    if pdf_docs:
+        st.session_state.pdf_docs = pdf_docs
 
 if __name__ == "__main__":
     main()
+
+
+
+
+