Init app

app.py

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+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel, PeftConfig
+
+# Initialize the FastAPI app
+app = FastAPI()
+
+# Load model and tokenizer once at startup
+base_model_name = "akjindal53244/Llama-3.1-Storm-8B"
+peft_model_id = "LlamaFactoryAI/cv-job-description-matching"
+
+base_model = AutoModelForCausalLM.from_pretrained(base_model_name)
+tokenizer = AutoTokenizer.from_pretrained(base_model_name)
+config = PeftConfig.from_pretrained(peft_model_id)
+model = PeftModel.from_pretrained(base_model, peft_model_id)
+
+# Define request model
+class AnalysisRequest(BaseModel):
+    cv: str
+    job_description: str
+
+@app.post("/analyze")
+async def analyze(request: AnalysisRequest):
+    try:
+        # Prepare input text with formatted message
+        system_prompt = """
+        You are an advanced AI model designed to analyze the compatibility between a CV and a job description. You will receive a CV and a job description. Your task is to output a structured JSON format that includes the following:
+
+        1. matching_analysis: Analyze the CV against the job description to identify key strengths and gaps.
+        2. description: Summarize the relevance of the CV to the job description in a few concise sentences.
+        3. score: Provide a numerical compatibility score (0-100) based on qualifications, skills, and experience.
+        4. recommendation: Suggest actions for the candidate to improve their match or readiness for the role.
+
+        Your output must be in JSON format as follows:
+        {
+          "matching_analysis": "Your detailed analysis here.",
+          "description": "A brief summary here.",
+          "score": 85,
+          "recommendation": "Your suggestions here."
+        }
+        """
+        user_input = f"<CV> {request.cv} </CV>\n<job_description> {request.job_description} </job_description>"
+        input_text = system_prompt + user_input
+
+        # Tokenize and generate response
+        inputs = tokenizer(input_text, return_tensors="pt")
+        outputs = model.generate(**inputs, max_new_tokens=128)
+        generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+        return {"analysis": generated_text}
+
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+

requirements.txt

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+fastapi
+uvicorn[standard]
+transformers
+peft
+torch 
+pydantic
+torchvision
+torchaudio