Create app.py

app.py

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+from langchain.llms.huggingface_pipeline import HuggingFacePipeline
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+from transformers import BitsAndBytesConfig
+import pdfplumber
+from langchain.prompts import PromptTemplate
+
+nf4_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_compute_dtype=torch.bfloat16
+)
+
+model_id = "huggingFaceH4/zephyr-7b-alpha"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    quantization_config=nf4_config,
+    device_map="auto"
+)
+
+model.tie_weights()
+
+pipe = pipeline("text-generation",
+                model=model,
+                tokenizer=tokenizer,
+                max_new_tokens= 512
+                )
+
+llm = HuggingFacePipeline(pipeline=pipe)
+
+## LLM Response
+def get_llm_response(input):
+  res = llm.predict(input)
+  return res
+
+def input_pdf_text(uploaded_file):
+  with open(uploaded_file, 'rb') as f:
+    pdf = pdfplumber.open(f)
+    text = ""
+    for page in pdf.pages:
+        text += page.extract_text()
+    return text
+
+
+def Get_Response(upload_pdf,jd):
+  text = input_pdf_text(upload_pdf)
+  prompt_template = PromptTemplate.from_template(
+    """
+    Hey Act Like a skilled or very experience ATS(Application Tracking System)
+    with a deep understanding of tech field, software engineering, data science,
+    data analyst and big data engineer. Your Task is to evaluate the resume based on the
+    given job description.
+    You must consider the job market is very competitive and you should provide
+    best assistance for the improving the resume. Assisn the percentage Matching 
+    based on JD(Job Description) and the missing keywords with high accuracy
+    resume:{text}
+    description:{jd}
+
+    I want the response in one single tring having the structure
+    {{"JD Match":"%","MissingKeywords:[]","Profile Summary":""}}
+        """)
+  prompt = prompt_template.format(text=text,jd=jd)
+  response = llm.predict(prompt)
+  return response
+
+# Define Gradio interface
+interface = gr.Interface(
+    fn=Get_Response,
+    inputs=["file","text"],
+    # inputs=[
+    #     gr.File("upload_pdf", label="Upload PDF"),
+    #     gr.Textbox("jd", label="Job Description"),
+    # ],
+    outputs="text",
+    title="Get ATS-Style Resume Evaluation",
+    description="Upload a resume PDF and provide a job description to get an evaluation with JD match percentage, missing keywords, and profile summary.",
+)
+
+# Launch the Gradio application
+interface.launch(debug=True, share=True)