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app.py

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+import gradio as gr
+import torch
+from transformers import BertTokenizer, BertForSequenceClassification
+
+# Load model and tokenizer
+model_name = "AventIQ-AI/bert-talentmatchai"
+tokenizer = BertTokenizer.from_pretrained(model_name)
+model = BertForSequenceClassification.from_pretrained(model_name, torch_dtype=torch.float16)
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+model.eval()
+
+# Label mapping
+label_mapping = {0: "No Fit", 1: "Low Fit", 2: "Potential Fit", 3: "Good Fit"}
+
+def preprocess_text(text, max_length=256):
+    """Truncate input text to avoid exceeding model limits."""
+    return " ".join(text.split()[:max_length])
+
+def talent_match(resume, job_description):
+    resume = preprocess_text(resume)
+    job_description = preprocess_text(job_description)
+    
+    input_text = f"Resume: {resume} Job Description: {job_description}"
+    inputs = tokenizer([input_text], padding="max_length", truncation=True, return_tensors="pt").to(device)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    prediction = outputs.logits.argmax(dim=1).item()
+    
+    return label_mapping[prediction]
+
+iface = gr.Interface(
+    fn=talent_match,
+    inputs=[
+        gr.Textbox(label="📄 Resume", placeholder="Paste the candidate's resume here...", lines=5),
+        gr.Textbox(label="📌 Job Description", placeholder="Paste the job description here...", lines=5)
+    ],
+    outputs=gr.Textbox(label="✅ Match Result"),
+    title="🤖 AI-Powered Talent Matching System",
+    description="🔍 Enter a candidate's resume and a job description to check if they are a good match using AI.",
+    theme="compact",
+    allow_flagging="never",
+    examples=[
+        ["Experienced Python developer skilled in machine learning and data science.", "Looking for a Python developer with ML experience."],
+        ["Project manager with 5 years in Agile methodologies.", "Seeking a Scrum Master with Agile experience."]
+    ],
+)
+
+if __name__ == "__main__":
+    iface.launch()

requirements.txt

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+torch
+transformers
+gradio
+sentencepiece