Update app.py

app.py

@@ -1,79 +1,75 @@
-from fastapi import FastAPI, HTTPException, UploadFile, File
-import pandas as pd
-from resume_extractor import ResumeExtractor
-from job_description_extractor import JobDescriptionExtractor
-from model_trainer import ModelTrainer
-from comparison_utils import (
-    compare_with_chatgpt_job_title,
-    compare_with_chatgpt_education,
-    compare_with_chatgpt_location,
-    compare_age_range_with_description
-)
-from synthetic_data import create_synthetic_data
-
-app = FastAPI()
-
-def main(resume_text, job_description):
-    openai_api_key = 'sk-proj-bC6H6QrP6DUqHkn5vOkYT3BlbkFJsSyvL4Bc9c3UEbHrsPMj'
-    ner_model_name_or_path = "NLPclass/Named-entity-recognition"
-    skill_model_name_or_path = "GalalEwida/lm-ner-skills-recognition"
-
-    resume_extractor = ResumeExtractor(ner_model_name_or_path, openai_api_key)
-    job_description_extractor = JobDescriptionExtractor(openai_api_key)
-
-    full_name, loc, age, skills, education_resume, title_job_resume = resume_extractor.extract_resume_info(resume_text, skill_model_name_or_path)
-    job_skills, education_job, title_job, location, age_DS = job_description_extractor.extract_job_info(job_description, skill_model_name_or_path)
-
-    education_match = compare_with_chatgpt_education(education_resume, education_job, openai_api_key)
-    title_job_match = compare_with_chatgpt_job_title(title_job_resume, title_job, openai_api_key)
-    title_loc_match = compare_with_chatgpt_location(loc, location, openai_api_key)
-    title_age_match = compare_age_range_with_description(age, age_DS, openai_api_key)
-
-    synthetic_data = create_synthetic_data(job_skills, education_job, title_job, location, age_DS)
-    synthetic_data.to_csv('synthetic_data.csv')
-    model_trainer = ModelTrainer(synthetic_data)
-    best_model = model_trainer.train_models()
-
-    input_data = {skill: 1 if skill in skills else 0 for skill in job_skills}
-    input_data[education_job] = education_match
-    input_data[title_job] = title_job_match
-    input_data[location] = title_loc_match
-    input_data[age_DS] = title_age_match
-
-    input_df = pd.DataFrame([input_data])
-    input_df.to_csv('input_df.csv')
-    predicted_target = best_model.predict(input_df)
-
-    return {
-        "full_name": full_name,
-        "location": loc,
-        "age": age,
-        "age_DS": age_DS,
-        "skills": skills,
-        "education_resume": education_resume,
-        "title_job_resume": title_job_resume,
-        "job_skills": job_skills,
-        "education_job": education_job,
-        "title_job": title_job,
-        "location_job": location,
-        "predicted_target": predicted_target[0]
-    }
-
-@app.post("/extract")
-async def extract(resume_file: UploadFile = File(...), job_description_file: UploadFile = File(...)):
-    try:
-        resume_text = await resume_file.read()
-        job_description = await job_description_file.read()
-        
-        # Convert bytes to string
-        resume_text = resume_text.decode('utf-8')
-        job_description = job_description.decode('utf-8')
-        
-        output = main(resume_text, job_description)
-        return output
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
-
-if __name__ == "__main__":
-    import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=8000)
+from fastapi import FastAPI, HTTPException, UploadFile, File
+import pandas as pd
+from resume_extractor import ResumeExtractor
+from job_description_extractor import JobDescriptionExtractor
+from model_trainer import ModelTrainer
+from comparison_utils import (
+    compare_with_chatgpt_job_title,
+    compare_with_chatgpt_education,
+    compare_with_chatgpt_location,
+    compare_age_range_with_description
+)
+from synthetic_data import create_synthetic_data
+
+app = FastAPI()
+
+def main(resume_text, job_description):
+    openai_api_key = 'sk-proj-bC6H6QrP6DUqHkn5vOkYT3BlbkFJsSyvL4Bc9c3UEbHrsPMj'
+    ner_model_name_or_path = "NLPclass/Named-entity-recognition"
+    skill_model_name_or_path = "GalalEwida/lm-ner-skills-recognition"
+
+    resume_extractor = ResumeExtractor(ner_model_name_or_path, openai_api_key)
+    job_description_extractor = JobDescriptionExtractor(openai_api_key)
+
+    full_name, loc, age, skills, education_resume, title_job_resume = resume_extractor.extract_resume_info(resume_text, skill_model_name_or_path)
+    job_skills, education_job, title_job, location, age_DS = job_description_extractor.extract_job_info(job_description, skill_model_name_or_path)
+
+    education_match = compare_with_chatgpt_education(education_resume, education_job, openai_api_key)
+    title_job_match = compare_with_chatgpt_job_title(title_job_resume, title_job, openai_api_key)
+    title_loc_match = compare_with_chatgpt_location(loc, location, openai_api_key)
+    title_age_match = compare_age_range_with_description(age, age_DS, openai_api_key)
+
+    synthetic_data = create_synthetic_data(job_skills, education_job, title_job, location, age_DS)
+    synthetic_data.to_csv('synthetic_data.csv')
+    model_trainer = ModelTrainer(synthetic_data)
+    best_model = model_trainer.train_models()
+
+    input_data = {skill: 1 if skill in skills else 0 for skill in job_skills}
+    input_data[education_job] = education_match
+    input_data[title_job] = title_job_match
+    input_data[location] = title_loc_match
+    input_data[age_DS] = title_age_match
+
+    input_df = pd.DataFrame([input_data])
+    input_df.to_csv('input_df.csv')
+    predicted_target = best_model.predict(input_df)
+
+    return {
+        "full_name": full_name,
+        "location": loc,
+        "age": age,
+        "age_DS": age_DS,
+        "skills": skills,
+        "education_resume": education_resume,
+        "title_job_resume": title_job_resume,
+        "job_skills": job_skills,
+        "education_job": education_job,
+        "title_job": title_job,
+        "location_job": location,
+        "predicted_target": predicted_target[0]
+    }
+
+@app.post("/extract")
+async def extract(resume_file: UploadFile = File(...), job_description_file: UploadFile = File(...)):
+    try:
+        resume_text = await resume_file.read()
+        job_description = await job_description_file.read()
+        
+        # Convert bytes to string
+        resume_text = resume_text.decode('utf-8')
+        job_description = job_description.decode('utf-8')
+        
+        output = main(resume_text, job_description)
+        return output
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))