Update app.py

app.py

@@ -1,71 +1,42 @@
 import gradio as gr
-import requests
+import openai
 import os
-import docx2txt
-import PyPDF2 as pdf
 
+# Set OpenAI API key
+openai.api_key = os.getenv("OPENAI_API_KEY")
 
-
-
-def generate_response_from_jabir(resume_text, job_description):
-    base_url = "https://api.jabirproject.org/generate"
-    headers = {"apikey": os.getenv("7471142a-deb4-4a70-8ee3-6603e21bcc1d")}
-    input_prompt_template = """
+def evaluate_resume(resume, job_description):
+    prompt = f"""
     As an experienced Applicant Tracking System (ATS) analyst,
-    with profound knowledge in technology, software engineering, data science, 
+    with profound knowledge in technology, software engineering, data science,
     and big data engineering, your role involves evaluating resumes against job descriptions.
     Recognizing the competitive job market, provide top-notch assistance for resume improvement.
-    Your goal is to analyze the resume against the given job description, 
+    Your goal is to analyze the resume against the given job description,
     assign a percentage match based on key criteria, and pinpoint missing keywords accurately.
-    resume:{text}
+    resume:{resume}
     description:{job_description}
     I want the response in one single string having the structure
     {{"Job Description Match":"%","Missing Keywords":"","Candidate Summary":"","Experience":""}}
     """
-    prompt = input_prompt_template.format(text=resume_text, job_description=job_description)
-    data = {
-        "messages": [{"role": "user", "content": prompt}]
-    }
-    response = requests.post(base_url, headers=headers, json=data)
     
-    if response.ok:
-        response_text = response.json()["choices"][0]["message"]["content"]
-        match_percentage_str = response_text.split('"Job Description Match":"')[1].split('"')[0]
-        match_percentage = float(match_percentage_str.rstrip('%'))
-        
-        if match_percentage >= 80:
-            recommendation = "Move forward with hiring"
-        else:
-            recommendation = "Not a Match"
-        
-        return response_text, recommendation
-    else:
-        return f"Error: {response.status_code}, {response.text}", None
-
-def extract_text_from_file(uploaded_file):
-    if uploaded_file.name.endswith('.pdf'):
-        pdf_reader = pdf.PdfReader(uploaded_file)
-        text_content = ""
-        for page in pdf_reader.pages:
-            text_content += str(page.extract_text())
-        return text_content
-    elif uploaded_file.name.endswith('.docx'):
-        return docx2txt.process(uploaded_file)
-    else:
-        return "Unsupported file format"
-
-def process_file(uploaded_file, job_description):
-    if uploaded_file is not None:
-        resume_text = extract_text_from_file(uploaded_file)
-        return generate_response_from_jabir(resume_text, job_description)
-    else:
-        return "No file uploaded", None
+    response = openai.ChatCompletion.create(
+        model="gpt-3.5-turbo",
+        messages=[
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": prompt}
+        ]
+    )
+    
+    return response.choices[0].message['content']
 
 iface = gr.Interface(
-    fn=process_file,
-    inputs=[gr.File(type="filepath", label="resume") ,gr.Textbox(lines=10, label="Job Description")],
-    outputs=[gr.Textbox(label="ATS Evaluation Result"), gr.Textbox(label="Recommendation")],
-    title="Intelligent ATS-Enhance Your Resume ATS"
+    fn=evaluate_resume,
+    inputs=[
+        gr.inputs.Textbox(lines=10, label="Resume"),
+        gr.inputs.Textbox(lines=10, label="Job Description")
+    ],
+    outputs="text",
+    title="Resume Evaluator"
 )
 
 iface.launch()