Added summary and ATS

.env

@@ -0,0 +1 @@
+GOOGLE_API_KEY=AIzaSyAA59xkxMEUqffuaNMzrTxK6m5AJpY3tCw

app.py

@@ -1,114 +1,50 @@
-import os
-import pyperclip
+# app.py
 import streamlit as st
-from dotenv import load_dotenv
-import speech_recognition as sr
-import google.generativeai as genai
-from langchain.prompts import PromptTemplate
-from langchain_google_genai import ChatGoogleGenerativeAI
+import blogger
+import gist
+import pdfplumber
+import ats1
+import docx
 
-load_dotenv()
+st.set_page_config(page_title='AI MultiTask', page_icon='🤖', layout='centered')
 
-def model():
-    genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
-    return ChatGoogleGenerativeAI(model="gemini-pro")
+st.title("What You Want To Do")
 
-def getLLamaresponse(input_text,no_words,blog_style):
-    ## Prompt Template
-    template="""
-        Write a {tone} blog in {lang} as {blog_style} for a topic {input_text} within {no_words} words with references and video links also if possible and ends with a conclusion. {info}
-            """
-    prompt=PromptTemplate(input_variables=['tone','lang',"blog_style","input_text",'no_words', "info"],
-                          template=template)
-    formated_prompt = prompt.format(tone=tone,lang=lang,blog_style=blog_style,input_text=input_text,no_words=no_words,info=info)
-    ## Generate the ressponse from the LLama 2 model
-    response=llm.invoke(formated_prompt)
-    return response.content
+uploaded_file = st.file_uploader("Choose a document file", type=["pdf", "txt", "csv", "docx"])
+text = ''
+if uploaded_file is not None:
+    st.write("File uploaded successfully!")
 
-def copy_text(answer, copy_button=False):
-    pyperclip.copy(answer)
-    if copy_button:
-        st.toast("Text copied to clipboard!", icon="📋")
+    file_extension = uploaded_file.name.split(".")[-1]
 
-def record_audio():
-    r = sr.Recognizer()
-    with st.spinner("Recording..."):
-        with sr.Microphone() as source:
-            r.adjust_for_ambient_noise(source)  # Adjust for ambient noise
-            with st.spinner("Say Something..."):
-                audio = r.listen(source, timeout=5)  # Set a timeout for listening
-        with st.spinner("Processing..."):
-            # Attempt speech recognition
-            try:
-                text = r.recognize_google(audio)
-                st.session_state['input_text'] = text
-                return text
-            except sr.UnknownValueError:
-                st.write("Sorry, I could not understand what you said. Please try again or write in text box.")
-                return ""
-            except sr.RequestError as e:
-                st.write(f"Could not request results; {e}")
-                return ""
+    if file_extension == "pdf":
+        with pdfplumber.open(uploaded_file) as pdf:
+            pages = pdf.pages
+            for page in pages:
+                text = page.extract_text()
 
-def input_state(input_text):
-    if isinstance(input_text, str):
-        st.session_state['input_text'] = input_text  # Update st.session_state with new text input
-
-
-
-st.set_page_config(page_title="AI-Blogger",
-                    page_icon='🤖',
-                    layout='centered' )
-
-st.header("AI Blogger 🤖", divider='rainbow')
-hide_streamlit_style = """
-<style>
-#MainMenu {visibility: hidden;}
-footer {visibility: hidden;}
-</style>
-
-"""
-st.markdown(hide_streamlit_style, unsafe_allow_html=True)
-#? Make it False in DEV Mode
-button_disabled = True
-# Button with info hover
-if button_disabled:
-    st.toast("Mic Button is disabled in production. HF Don't have Audio Device")
-
-col1,col2=st.columns([20,3])
-with col1:
-    st.write('')
-    text = st.empty()
-    input_text = text.text_input("Enter the Blog Topic")
+    elif file_extension == "txt":
+        text = uploaded_file.getvalue().decode("utf-8")
     
-with col2:
-    st.write(':grey[.]')
-    recorder = st.button("🎙️", help="Blog Topic by Voice", key="mic", disabled=button_disabled)
+    elif file_extension == "docx":
+        docx_text = docx.Document(uploaded_file)
+        full_text = []
+        for para in docx_text.paragraphs:
+            full_text.append(para.text)
+        text = "\n".join(full_text)
 
-if recorder:
-    recorded_text = record_audio()
-    if recorded_text:
-        input_text = text.text_input("Enter the Blog Topic",value=recorded_text)
+st.text_area("Extracted From Document",value=text)
+st.session_state['doc_text'] = text
 
-with st.sidebar:
-    st.title(' :blue[_AI Generated Blog_] 🤖')
-    # Refactored from <https://github.com/a16z-infra/llama2-chatbot>
-    st.subheader('Parameters')
-    no_words = st.sidebar.slider('Maximum Characters', min_value=10, max_value=5000, value=1000, step=100)
-    blog_style=st.selectbox('Writing the blog for', ('Researchers','Data Scientist','Common People'),index=0)
-    tone=st.selectbox('Desired Tone', ('Informative','Casual','Persuasive', 'Formal', 'Humorous'),index=0)
-    lang = st.text_input('Language', 'English')
-    info = st.text_input('Specific Instruction')
-    with st.spinner("Loading Model..."):
-        llm= model()
+col1, col2, col3 = st.columns([3, 3,3])
+option = st.radio("I want to use: ", ("Blog", "Summerize", "ATS"), horizontal=True)
 
-submit=st.button("Generate Blog", on_click =lambda: input_state(input_text))
-if submit:
-    with st.spinner("Generating Blog..."):
-        answer = getLLamaresponse(st.session_state['input_text'],no_words,blog_style)
-        st.success('Blog Generated!', icon="✅")
-        st.write(answer)
-        st.markdown(''':orange[Run in your system to access Copy to Clipboard Feature]''')
+if option == "Blog":
+    blogger.run_blogger(st.session_state['doc_text'])
+    
+elif option == "Summerize":
+    gist.run_gist(st.session_state['doc_text'])
 
-        ## Uncomment if using in system HF doesn't Support Copy to clipboard
-        # st.button("📋", on_click=copy_text(answer))
+elif option == "ATS":
+    ats1.run_ats(st.session_state['doc_text'])
+    

ats.py

@@ -0,0 +1,96 @@
+from dotenv import load_dotenv
+
+load_dotenv()
+import base64
+import streamlit as st
+import os
+import io
+from PIL import Image 
+import pdf2image
+import google.generativeai as genai
+
+genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
+
+def get_gemini_response(input,pdf_cotent,prompt):
+    model=genai.GenerativeModel('gemini-pro-vision')
+    response=model.generate_content([input,pdf_content[0],prompt])
+    return response.text
+
+def input_pdf_setup(uploaded_file):
+    if uploaded_file is not None:
+        ## Convert the PDF to image
+        images=pdf2image.convert_from_bytes(uploaded_file.read(), poppler_path=r'C:\Program Files (x86)\poppler\Library\bin')
+
+        first_page=images[0]
+
+        # Convert to bytes
+        img_byte_arr = io.BytesIO()
+        first_page.save(img_byte_arr, format='JPEG')
+        img_byte_arr = img_byte_arr.getvalue()
+
+        pdf_parts = [
+            {
+                "mime_type": "image/jpeg",
+                "data": base64.b64encode(img_byte_arr).decode()  # encode to base64
+            }
+        ]
+        return pdf_parts
+    else:
+        raise FileNotFoundError("No file uploaded")
+
+## Streamlit App
+
+st.set_page_config(page_title="ATS Resume EXpert")
+st.header("ATS Tracking System")
+input_text=st.text_area("Job Description: ",key="input")
+uploaded_file=st.file_uploader("Upload your resume(PDF)...",type=["pdf"])
+
+
+if uploaded_file is not None:
+    st.write("PDF Uploaded Successfully")
+    
+
+
+submit1 = st.button("Tell Me About the Resume")
+
+#submit2 = st.button("How Can I Improvise my Skills")
+
+submit3 = st.button("Percentage match")
+
+input_prompt1 = """
+ You are an experienced Technical Human Resource Manager,your task is to review the provided resume against the job description. 
+  Please share your professional evaluation on whether the candidate's profile aligns with the role. 
+ Highlight the strengths and weaknesses of the applicant in relation to the specified job requirements.
+"""
+
+input_prompt3 = """
+You are an skilled ATS (Applicant Tracking System) scanner with a deep understanding of data science and ATS functionality, 
+your task is to evaluate the resume against the provided job description. give me the percentage of match if the resume matches
+the job description. First the output should come as percentage and then keywords missing and last final thoughts.
+"""
+
+if submit1:
+    if uploaded_file is not None:
+        pdf_content=input_pdf_setup(uploaded_file)
+        response=get_gemini_response(input_prompt1,pdf_content,input_text)
+        st.subheader("The Repsonse is")
+        st.write(response)
+    else:
+        st.write("Please uplaod the resume")
+
+elif submit3:
+    if uploaded_file is not None:
+        pdf_content=input_pdf_setup(uploaded_file)
+        response=get_gemini_response(input_prompt3,pdf_content,input_text)
+        st.subheader("The Repsonse is")
+        st.write(response)
+    else:
+        st.write("Please uplaod the resume")
+
+
+
+   
+
+
+
+

ats1.py

@@ -0,0 +1,118 @@
+import os
+import pyperclip
+import streamlit as st
+from dotenv import load_dotenv
+import speech_recognition as sr
+import google.generativeai as genai
+from langchain.prompts import PromptTemplate
+from langchain_google_genai import ChatGoogleGenerativeAI
+
+
+class ATS(object):
+    def __init__(self, title="ATS Tracking System"):
+        self.title = title
+
+    @staticmethod
+    def model():
+        genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
+        return ChatGoogleGenerativeAI(model="gemini-pro")
+
+    @staticmethod
+    def get_gemini_response(llm, input_text, doc, template, info=''):
+        formated_prompt = template.format(doc=doc, input_text=input_text)
+        response = llm.invoke(formated_prompt)
+        return response.content
+        # return formated_prompt
+
+    @staticmethod
+    def copy_text(answer, copy_button=False):
+        pyperclip.copy(answer)
+        if copy_button:
+            st.toast("Text copied to clipboard!", icon="📋")
+
+    @staticmethod
+    def record_audio():
+        r = sr.Recognizer()
+        with st.spinner("Recording..."):
+            with sr.Microphone() as source:
+                r.adjust_for_ambient_noise(source)
+                with st.spinner("Say Something..."):
+                    audio = r.listen(source, timeout=5)
+            with st.spinner("Processing..."):
+                try:
+                    text = r.recognize_google(audio)
+                    st.session_state['input_text'] = text
+                    return text
+                except sr.UnknownValueError:
+                    st.write("Sorry, I could not understand what you said. Please try again or write in text box.")
+                    return ""
+                except sr.RequestError as e:
+                    st.write(f"Could not request results; {e}")
+                    return ""
+
+    @staticmethod
+    def input_state(input_text):
+        if isinstance(input_text, str):
+            st.session_state['input_text'] = input_text
+
+
+def run_ats(doc=''):
+    load_dotenv()
+    ats = ATS()
+    st.header(ats.title + " 🤖", divider='rainbow')
+    input_text=st.text_area("Job Description: ",key="input")
+    # uploaded_file=st.file_uploader("Upload your resume(PDF)...",type=["pdf"])
+
+
+    # if uploaded_file is not None:
+        # st.write("PDF Uploaded Successfully")
+        
+
+    with st.sidebar:
+        st.title(' :blue[_AI Generated ATS] 🤖')
+        st.subheader('Parameters')
+        
+        with st.spinner("Loading Model..."):
+            llm = ats.model()
+    submit1 = st.button("Tell Me About the Resume")
+
+    # submit2 = st.button("How Can I Improvise my Skills")
+
+    submit3 = st.button("Percentage match")
+
+    input_prompt1 = """
+    You are an experienced Technical Human Resource Manager,your task is to review the provided resume against the job description. 
+    Please share your professional evaluation on whether the candidate's profile aligns with the role. 
+    Highlight the strengths and weaknesses of the applicant in relation to the specified job requirements.
+
+    Job Description: {input_text}
+    Resume: {doc}
+    """
+
+    input_prompt3 = """
+    You are an skilled ATS (Applicant Tracking System) scanner with a deep understanding of data science and ATS functionality, 
+    your task is to evaluate the resume against the provided job description. give me the percentage of match if the resume matches
+    the job description. First the output should come as percentage and then keywords missing and last final thoughts.
+
+    Job Description: {input_text}
+    Resume: {doc}
+    """
+
+    if submit1:
+        if doc is not None:
+            response=ats.get_gemini_response(llm=llm,template=input_prompt1,doc=doc,input_text=input_text)
+            st.subheader("The Repsonse is")
+            st.write(response)
+        else:
+            st.write("Please uplaod the resume")
+
+    elif submit3:
+        if doc is not None:
+            response=ats.get_gemini_response(llm=llm,template=input_prompt3,doc=doc,input_text=input_text)
+            st.subheader("The Repsonse is")
+            st.write(response)
+        else:
+            st.write("Please uplaod the resume")
+
+if __name__ == "__main__":
+    run_ats()

blogger.py

@@ -0,0 +1,118 @@
+# blogger.py
+import os
+import pyperclip
+import streamlit as st
+from dotenv import load_dotenv
+import speech_recognition as sr
+import google.generativeai as genai
+from langchain.prompts import PromptTemplate
+from langchain_google_genai import ChatGoogleGenerativeAI
+
+class Blog(object):
+    def __init__(self, title="AI Blogger"):
+        self.title = title
+
+    @staticmethod
+    def model():
+        genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
+        return ChatGoogleGenerativeAI(model="gemini-pro")
+
+    @staticmethod
+    def getLLamaresponse(llm, input_text, tone, lang, blog_style, no_words, doc='', info=''):
+        template = """
+            Write a {tone} blog in {lang} as {blog_style} for a topic {input_text} within {no_words} words with references and video links also if possible and ends with a conclusion. {info} {doc}
+                """.format(tone=tone, lang=lang, blog_style=blog_style, input_text=input_text,
+                                        no_words=no_words,doc=doc, info=info)
+        response = llm.invoke(template)
+        return response.content
+
+    @staticmethod
+    def copy_text(answer, copy_button=False):
+        pyperclip.copy(answer)
+        if copy_button:
+            st.toast("Text copied to clipboard!", icon="📋")
+
+    @staticmethod
+    def record_audio():
+        r = sr.Recognizer()
+        with st.spinner("Recording..."):
+            with sr.Microphone() as source:
+                r.adjust_for_ambient_noise(source)
+                with st.spinner("Say Something..."):
+                    audio = r.listen(source, timeout=5)
+            with st.spinner("Processing..."):
+                try:
+                    text = r.recognize_google(audio)
+                    st.session_state['input_text'] = text
+                    return text
+                except sr.UnknownValueError:
+                    st.write("Sorry, I could not understand what you said. Please try again or write in text box.")
+                    return ""
+                except sr.RequestError as e:
+                    st.write(f"Could not request results; {e}")
+                    return ""
+
+    @staticmethod
+    def input_state(input_text):
+        if isinstance(input_text, str):
+            st.session_state['input_text'] = input_text
+
+def run_blogger(doc=''):
+    load_dotenv()
+    blog = Blog()
+
+    st.header(blog.title + " 🤖", divider='rainbow')
+
+    hide_streamlit_style = """
+    <style>
+    #MainMenu {visibility: hidden;}
+    footer {visibility: hidden;}
+    </style>
+    """
+    st.markdown(hide_streamlit_style, unsafe_allow_html=True)
+
+    button_disabled = True
+
+    if button_disabled:
+        st.toast("Mic Button is disabled in production. HF Don't have Audio Device")
+
+    col1, col2 = st.columns([20, 3])
+
+    with col1:
+        text = st.empty()
+        input_text = text.text_input("Enter the Blog Topic")
+
+    with col2:
+        recorder = st.button("🎙️", help="Blog Topic by Voice", key="mic", disabled=button_disabled)
+
+    if recorder:
+        recorded_text = blog.record_audio()
+        if recorded_text:
+            input_text = text.text_input("Enter the Blog Topic", value=recorded_text)
+
+    if doc:
+        input_text = text.text_area("Enter the Blog Topic", value=doc)
+        input_text = "Reference: "+ input_text
+
+    with st.sidebar:
+        st.title(' :blue[_AI Generated Blog_] 🤖')
+        st.subheader('Parameters')
+        no_words = st.sidebar.slider('Maximum Characters', min_value=100, max_value=5000, value=1000, step=100)
+        blog_style = st.selectbox('Writing the blog for', ('Researchers', 'Data Scientist', 'Common People'), index=0)
+        tone = st.selectbox('Desired Tone', ('Informative', 'Casual', 'Persuasive', 'Formal', 'Humorous'), index=0)
+        lang = st.text_input('Language', 'English')
+        info = st.text_area('Specific Instruction')
+        with st.spinner("Loading Model..."):
+            llm = blog.model()
+
+    submit = st.button("Generate Blog", on_click=lambda: blog.input_state(input_text))
+
+    if submit:
+        with st.spinner("Generating Blog..."):
+            answer = blog.getLLamaresponse(llm, st.session_state['input_text'], tone, lang, blog_style, no_words, doc, info)
+            st.success('Blog Generated!', icon="✅")
+            st.write(answer)
+            st.markdown(''':orange[Run in your system to access Copy to Clipboard Feature]''')
+
+if __name__ == "__main__":
+    run_blogger()

gist.py

@@ -0,0 +1,121 @@
+import os
+import pyperclip
+import streamlit as st
+from dotenv import load_dotenv
+import speech_recognition as sr
+import google.generativeai as genai
+from langchain.prompts import PromptTemplate
+from langchain_google_genai import ChatGoogleGenerativeAI
+
+class Summary(object):
+    def __init__(self, title="AI Summarizer"):
+        self.title = title
+
+    @staticmethod
+    def model():
+        genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
+        return ChatGoogleGenerativeAI(model="gemini-pro")
+
+    @staticmethod
+    def getLLamaresponse(llm, input_text, tone, lang, gist_style, no_words, info=''):
+        template = """
+            Write a {tone} summary in {lang} as {gist_style} within {no_words} words.
+            Text: {input_text} 
+            {info}
+                """.format(tone=tone, lang=lang, gist_style=gist_style, input_text=input_text,
+                                        no_words=no_words, info=info)
+        response = llm.invoke(template)
+        return response.content
+        # return formated_prompt
+
+    @staticmethod
+    def copy_text(answer, copy_button=False):
+        pyperclip.copy(answer)
+        if copy_button:
+            st.toast("Text copied to clipboard!", icon="📋")
+
+    @staticmethod
+    def record_audio():
+        r = sr.Recognizer()
+        with st.spinner("Recording..."):
+            with sr.Microphone() as source:
+                r.adjust_for_ambient_noise(source)
+                with st.spinner("Say Something..."):
+                    audio = r.listen(source, timeout=5)
+            with st.spinner("Processing..."):
+                try:
+                    text = r.recognize_google(audio)
+                    st.session_state['input_text'] = text
+                    return text
+                except sr.UnknownValueError:
+                    st.write("Sorry, I could not understand what you said. Please try again or write in text box.")
+                    return ""
+                except sr.RequestError as e:
+                    st.write(f"Could not request results; {e}")
+                    return ""
+
+    @staticmethod
+    def input_state(input_text):
+        if isinstance(input_text, str):
+            st.session_state['input_text'] = input_text
+
+    
+
+def run_gist(doc=''):
+    load_dotenv()
+    gist = Summary()
+
+    st.header(gist.title + " 🤖", divider='rainbow')
+
+    hide_streamlit_style = """
+    <style>
+    #MainMenu {visibility: hidden;}
+    footer {visibility: hidden;}
+    </style>
+    """
+    st.markdown(hide_streamlit_style, unsafe_allow_html=True)
+
+    button_disabled = False
+
+    if button_disabled:
+        st.toast("Mic Button is disabled in production. HF Don't have Audio Device")
+
+    col1, col2 = st.columns([20, 3])
+
+    with col1:
+        text = st.empty()
+        input_text = text.text_area("Enter Text")
+
+    with col2:
+        recorder = st.button("🎙️", help="Text by Voice", key="mic", disabled=button_disabled)
+
+    if recorder:
+        recorded_text = gist.record_audio()
+        if recorded_text:
+            input_text = text.text_area("Enter Text", value=recorded_text)
+    
+    if doc:
+        input_text = text.text_area("Enter Text", value=doc)
+
+    with st.sidebar:
+        st.title(' :blue[_AI Generated Summary_] 🤖')
+        st.subheader('Parameters')
+        no_words = st.sidebar.slider('Maximum Characters', min_value=100, max_value=5000, value=1000, step=100)
+        gist_style = st.selectbox('Writing the Summary for', ('Researchers', 'Data Scientist', 'Common People'), index=0)
+        tone = st.selectbox('Desired Tone', ('Informative', 'Casual', 'Persuasive', 'Formal', 'Humorous'), index=0)
+        lang = st.text_input('Language', 'English')
+        info = st.text_area('Specific Instruction')
+        with st.spinner("Loading Model..."):
+            llm = gist.model()
+
+    submit = st.button("Generate Summary", on_click=lambda: gist.input_state(input_text))
+    print("Session: ", st.session_state)
+    if submit:
+        with st.spinner("Generating Summary..."):
+            answer = gist.getLLamaresponse(llm, st.session_state['input_text'], tone, lang, gist_style, no_words, info)
+            st.success('Summary Generated!', icon="✅")
+            st.write(answer)
+            st.markdown(''':orange[Run in your system to access Copy to Clipboard Feature]''')
+
+if __name__ == "__main__":
+    run_gist()

requirements.txt

@@ -2,8 +2,14 @@ google-generativeai
 langchain-google-genai
 langchain
 streamlit
+pdf2image
 pyperclip
 huggingface_hub
 python-dotenv
 SpeechRecognition
-PyAudio
+PyAudio
+pdfplumber
+python-docx
+
+
+https://github.com/oschwartz10612/poppler-windows/releases/download/v24.02.0-0/Release-24.02.0-0.zip