Update app.py

app.py

@@ -37,27 +37,64 @@ def calculate_resume_score(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):
+def format_resume_and_score(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.")
 
+    # Set the OpenAI API key
     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.
+    # Formatting the resume to YAML
+    template = """Format the provided resume to this YAML template:
+    ---
+    name: ''
+    phoneNumbers:
+      - ''
+    websites:
+      - ''
+    emails:
+      - ''
+    dateOfBirth: ''
+    addresses:
+      - street: ''
+        city: ''
+        state: ''
+        zip: ''
+        country: ''
+    summary: ''
+    education:
+      - school: ''
+        degree: ''
+        fieldOfStudy: ''
+        startDate: ''
+        endDate: ''
+    workExperience:
+      - company: ''
+        position: ''
+        startDate: ''
+        endDate: ''
+    skills:
+      - name: ''
+    certifications:
+      - name: ''
+    """
+    prompt = PromptTemplate(input_variables=["resume_text"], template=template)
+    memory = ConversationBufferMemory(memory_key="resume_text")
+
+    llm_chain = LLMChain(llm=ChatOpenAI(model="gpt-3.5-turbo"), prompt=prompt, verbose=True, memory=memory)
+    formatted_resume = llm_chain.predict(human_input=resume_text)
+
+    # 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
+    return formatted_resume, resume_score
 
 def main():
     """Main function to launch the Gradio interface with job description input."""
     iface = gr.Interface(
-        fn=format_resume_to_yaml,
+        fn=format_resume_and_score,
         inputs=[
             Textbox(label="Enter your OpenAI API Key"),
             File(label="Upload your PDF resume", type="binary"),
@@ -67,8 +104,8 @@ def main():
             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."
+        title="Resume Formatter and Relevance Scorer",
+        description="Upload a PDF resume, paste the job description, and enter your OpenAI API key to format your resume to a YAML template and score its relevance to the job."
     )
     iface.launch(debug=True, share=True)