Create app.py

app.py

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+import gradio as gr
+import openai
+import fitz  # PyMuPDF
+import torch
+from transformers import pipeline
+
+# Set OpenAI and Hugging Face API keys
+openai.api_key = "your_openai_api_key"
+
+# Initialize the Gemma model
+gemma_pipe = pipeline(
+    "text-generation",
+    model="google/gemma-2-27b-it",
+    model_kwargs={"torch_dtype": torch.bfloat16},
+    device="cuda"
+)
+
+def extract_text_from_pdf(pdf_file):
+    document = fitz.open(pdf_file)
+    text = ""
+    for page_num in range(len(document)):
+        page = document.load_page(page_num)
+        text += page.get_text()
+    return text
+
+def evaluate_with_gpt(pdf_file, job_description):
+    resume_text = extract_text_from_pdf(pdf_file)
+
+    prompt = f"""به عنوان یک تحلیلگر با تجربه سیستم ردیابی متقاضی (ATS)، نقش شما شامل ارزیابی رزومه در برابر شرح شغل است.
+    رزومه:{resume_text}
+    شرح شغل:{job_description}
+    """
+    
+    response = openai.ChatCompletion.create(
+        model="gpt-4o",
+        messages=[
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": prompt}
+        ]
+    )
+    
+    return response.choices[0].message['content']
+
+def evaluate_with_gemma(pdf_file, job_description):
+    resume_text = extract_text_from_pdf(pdf_file)
+
+    prompt = f"Evaluate the following resume against the job description. Resume: {resume_text} Job Description: {job_description}"
+    
+    outputs = gemma_pipe(prompt, max_new_tokens=256)
+    return outputs[0]["generated_text"].strip()
+
+def evaluate_both_models(pdf_file, job_description):
+    gpt_result = evaluate_with_gpt(pdf_file, job_description)
+    gemma_result = evaluate_with_gemma(pdf_file, job_description)
+    return f"GPT-4o Result:\n{gpt_result}\n\nGemma Result:\n{gemma_result}"
+
+iface = gr.Interface(
+    fn=lambda pdf, jd, model: evaluate_with_gpt(pdf, jd) if model == "GPT-4o" else evaluate_with_gemma(pdf, jd) if model == "Gemma" else evaluate_both_models(pdf, jd),
+    inputs=[
+        gr.File(label="Upload Resume PDF"),
+        gr.Textbox(lines=10, label="Job Description"),
+        gr.Radio(choices=["GPT-4o", "Gemma", "Both"], label="Choose Model")
+    ],
+    outputs="text",
+    title="Resume Evaluator"
+)
+
+iface.launch()