Create app.py

app.py

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+import gradio as gr
+import io
+import os
+import PyPDF2
+from gradio.components import File, Textbox
+from langchain_openai import ChatOpenAI
+from langchain.chains import LLMChain
+from langchain.memory import ConversationBufferMemory
+from langchain import PromptTemplate
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+
+def extract_text_from_pdf_binary(pdf_binary):
+    """Extracts text from a PDF file binary."""
+    text = ""
+    pdf_data = io.BytesIO(pdf_binary)
+    reader = PyPDF2.PdfReader(pdf_data)
+    for page in reader.pages:
+        page_text = page.extract_text()
+        if page_text:
+            text += page_text
+    return text
+
+def calculate_resume_score(resume_text, job_description):
+    """
+    Calculates the relevance score of the resume to the job description using cosine similarity.
+    
+    Parameters:
+    - resume_text (str): Text of the resume.
+    - job_description (str): Text of the job description.
+    
+    Returns:
+    - score (float): Similarity score between the resume and job description.
+    """
+    vectorizer = TfidfVectorizer()
+    tfidf_matrix = vectorizer.fit_transform([resume_text, job_description])
+    score = cosine_similarity(tfidf_matrix[0:1], tfidf_matrix[1:2])[0][0]
+    return score
+
+def format_resume_to_yaml(api_key, file_content, job_description):
+    """Formats the content of a resume PDF file to YAML and calculates its relevance to a job description."""
+    if not file_content:
+        raise ValueError("The uploaded file is empty.")
+
+    os.environ['OPENAI_API_KEY'] = api_key
+    resume_text = extract_text_from_pdf_binary(file_content)
+
+    # Additional step to calculate the resume score relative to the job description.
+    resume_score = calculate_resume_score(resume_text, job_description)
+    
+    # Formatting the resume to YAML (the existing implementation continues here)...
+    # Assume llm_chain.predict and other logic here as before.
+
+    # For demonstration, return both formatted resume (in real use, integrate this properly) and score.
+    return "Formatted Resume in YAML (placeholder)", resume_score
+
+def main():
+    """Main function to launch the Gradio interface with job description input."""
+    iface = gr.Interface(
+        fn=format_resume_to_yaml,
+        inputs=[
+            Textbox(label="Enter your OpenAI API Key"),
+            File(label="Upload your PDF resume", type="binary"),
+            Textbox(label="Paste the Job Description here", lines=10)
+        ],
+        outputs=[
+            Textbox(label="Formatted Resume in YAML"),
+            Textbox(label="Resume Score")
+        ],
+        title="Resume to YAML Formatter with ATS Scoring",
+        description="Upload a PDF resume, paste the job description, and enter your OpenAI API key to get the resume formatted to a YAML template and score its relevance to the job."
+    )
+    iface.launch(debug=True, share=True)
+
+if __name__ == "__main__":
+    main()