Upload 5 files

app.py

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+REPLACE_WITH_FINAL_APP_CODE

utils/parser.py

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+
+import fitz  # PyMuPDF
+import spacy
+
+nlp = spacy.load("en_core_web_sm")
+
+def parse_cv(file_path):
+    text = ""
+    with fitz.open(file_path) as doc:
+        for page in doc:
+            text += page.get_text()
+
+    doc_nlp = nlp(text)
+    education_level = "Unknown"
+    if "bachelor" in text.lower():
+        education_level = "Bachelor's Degree"
+    elif "master" in text.lower():
+        education_level = "Master's Degree"
+    elif "phd" in text.lower() or "doctor" in text.lower():
+        education_level = "PhD or Doctorate"
+    elif "high school" in text.lower():
+        education_level = "High School"
+
+    cv_type = "Technical" if any(tok.text.lower() in ["engineer", "developer", "python", "data"] for tok in doc_nlp) else "Non-Technical"
+
+    return text, education_level, cv_type

utils/scorer.py

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+
+def get_cv_score(text):
+    score = 50
+    if "python" in text.lower():
+        score += 10
+    if "project" in text.lower():
+        score += 10
+    if "lead" in text.lower() or "manager" in text.lower():
+        score += 10
+    if "certified" in text.lower() or "certification" in text.lower():
+        score += 10
+    return min(score, 100)

utils/suggestions.py

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+
+import requests
+import os
+
+HF_API_TOKEN = os.getenv("HF_API_TOKEN")
+ADZUNA_APP_ID = os.getenv("ADZUNA_APP_ID")
+ADZUNA_API_KEY = os.getenv("ADZUNA_API_KEY")
+
+def call_llm(prompt):
+    headers = {"Authorization": f"Bearer {HF_API_TOKEN}"}
+    json_data = {
+        "inputs": prompt,
+        "parameters": {"max_new_tokens": 300},
+    }
+    response = requests.post("https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.1", headers=headers, json=json_data)
+    if response.status_code == 200:
+        return response.json()[0]["generated_text"]
+    raise Exception(f"Hugging Face API Error {response.status_code}: {response.text}")
+
+def get_certification_suggestions(text):
+    return call_llm(f"Suggest professional certifications based on this CV: {text}")
+
+def get_higher_education_suggestions(text):
+    return call_llm(f"Suggest higher education programs based on this CV: {text}")
+
+def get_visa_recommendations(text):
+    return call_llm(f"Suggest visa opportunities in top countries based on this CV: {text}")
+
+def get_career_advice(text):
+    return call_llm(f"Act like a career counselor. Give personalized advice for this CV: {text}")
+
+def get_job_listings(text):
+    url = "https://api.adzuna.com/v1/api/jobs/us/search/1"
+    params = {"app_id": ADZUNA_APP_ID, "app_key": ADZUNA_API_KEY, "what": "engineer", "results_per_page": 5}
+    response = requests.get(url, params=params)
+    if response.status_code == 200:
+        return [f"{job['title']} - {job['location']['display_name']}" for job in response.json().get("results", [])]
+    return ["No jobs found"]

utils/visualizer.py

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+
+import matplotlib.pyplot as plt
+
+def generate_timeline(text):
+    stages = ["Skill Development", "Certifications", "Higher Education", "Job Applications", "Visa Process"]
+    durations = [2, 2, 3, 1, 1]
+    fig, ax = plt.subplots(figsize=(10, 2))
+    ax.barh(["Roadmap"], [sum(durations)], color="lightgray")
+    left = 0
+    for stage, duration in zip(stages, durations):
+        ax.barh(["Roadmap"], [duration], left=left, label=stage)
+        left += duration
+    ax.legend()
+    return fig