Duplicate from hudsonhayes/Vodafone_Resume_Analyzer

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
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+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
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+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+title: Vodafone Resume Analyzer
+emoji: 📈
+colorFrom: purple
+colorTo: pink
+sdk: gradio
+sdk_version: 3.35.2
+app_file: app.py
+pinned: false
+duplicated_from: hudsonhayes/Vodafone_Resume_Analyzer
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+import PyPDF2
+import os
+import openai
+
+def extract_text_from_file(file_path):
+    # Get the file extension
+    file_extension = os.path.splitext(file_path)[1]
+
+    if file_extension == '.pdf':
+        with open(file_path, 'rb') as file:
+            # Create a PDF file reader object
+            reader = PyPDF2.PdfFileReader(file)
+
+            # Create an empty string to hold the extracted text
+            extracted_text = ""
+
+            # Loop through each page in the PDF and extract the text
+            for page_number in range(reader.getNumPages()):
+                page = reader.getPage(page_number)
+                extracted_text += page.extractText()
+        return extracted_text
+
+    elif file_extension == '.txt':
+        with open(file_path, 'r') as file:
+            # Just read the entire contents of the text file
+            return file.read()
+
+    else:
+        return "Unsupported file type"
+
+def responce_from_ai(textjd, textcv):
+    resume = extract_text_from_file(textjd)
+    job_description = extract_text_from_file(textcv)
+    response = openai.Completion.create(
+        engine="text-davinci-003",
+        prompt=f"""
+                    Given the job description and the resume, assess the matching percentage and approximate percentage of the resume for the job.**Job Description:**{job_description}**Resume:**{resume}**Matching Assessment:**Based on an analysis of the resume and the job description,
+the overall matching percentage is estimated to be approximately [insert approximate percentage here].
+**Detailed Analysis:**
+                the result should be in this format:
+                matched percentage: [matching percentage]
+                reason            : [reason for this result]
+                keywords          : [matched key words from job_description and resume]
+                """,
+        temperature=0,
+        max_tokens=100,
+        n=1,
+        stop=None,
+    )
+    generated_text = response.choices[0].text.strip()
+    return generated_text
+
+def matching_percentage(job_description_path, resume_path):
+
+    job_description_path = job_description_path.name
+    resume_path = resume_path.name
+
+    generated_text = responce_from_ai(job_description_path, resume_path)
+    return generated_text
+
+
+with gr.Blocks(css="style.css",theme=gr.themes.Soft()) as app:
+      gr.HTML("""<img class="leftimage" align="left" src="https://templates.images.credential.net/1612472097627370951721412474196.png" alt="Image" width="210" height="210">
+                 <img class="rightimage" align="right" src="https://upload.wikimedia.org/wikipedia/en/thumb/c/cc/Vodafone_2017_logo.svg/2560px-Vodafone_2017_logo.svg.png" alt="Image" width="200" height="200">""")
+
+      with gr.Row():
+        with gr.Column(elem_id="col-container"):
+          gr.HTML(
+              """<br style="color:white;">"""
+              )
+          gr.HTML(
+              """<h2 style="text-align:center; color:"white">Vodafone Resume Matcher</h2> """
+          )
+          gr.HTML("<br>")
+      with gr.Row():
+        with gr.Column(scale=0.45, min_width=150, ):
+          jobDescription = gr.inputs.File(label="Job Description")
+        with gr.Column(scale=0.45, min_width=150):
+          resume = gr.inputs.File(label="Resume")
+        with gr.Column(scale=0.10, min_width=150):
+          find = gr.Button("Find")
+      with gr.Row():
+        with gr.Column(scale=1.0, min_width=150):
+          output = gr.outputs.Textbox(label="Matching Percentage")
+
+      find.click(matching_percentage, [jobDescription, resume], [output])
+app.launch()      

requirements.txt

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+openai
+gradio
+PyPDF2==2.12.1

style.css

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+#col-container {
+  max-width: 600px;
+  margin-left: auto;
+  margin-right: auto;
+}
+
+#row-flex {
+  display: flex;
+  align-items: center;
+  justify-content: center;
+}
+.leftimage .rightimage{
+  float:left;
+  filter: drop-shadow(20px 20px 10px white);
+}
+.leftimage{
+  padding-top:40px;
+  margin-left:310px;
+}
+.rightimage{
+  padding-top:30px;
+  margin-right:320px;
+}
+a,
+a:hover,
+a:visited {
+  text-decoration-line: underline;
+  font-weight: 600;
+  color: #1f2937 !important;
+}
+
+.dark a,
+.dark a:hover,
+.dark a:visited {
+  color: #f3f4f6 !important;
+}