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Freelancer-Recommender

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shiv-am-04 commited on 20 days ago

Commit

3a4750b

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1 Parent(s): b2a993d

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Dockerfile +20 -0
app.py +96 -0
freelancers.csv +1001 -0
freelancers.pkl +3 -0
jobs.csv +31 -0
mlb.pkl +3 -0
model_training.py +103 -0
ranker_model.pkl +3 -0
requirements.txt +6 -0
scaler.pkl +3 -0

Dockerfile ADDED Viewed

	@@ -0,0 +1,20 @@

+# Use official lightweight Python image
+FROM python:3.11.4
+# Set working directory
+WORKDIR /app
+# Copy requirements first for better caching
+COPY requirements.txt .
+# Install Python dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+# Copy all app files
+COPY . .
+# Expose the port for Hugging Face Spaces
+EXPOSE 7860
+# Use gunicorn instead of Flask dev server for production
+CMD ["gunicorn", "--bind", "0.0.0.0:7860", "app:app"]

app.py ADDED Viewed

	@@ -0,0 +1,96 @@

+from flask import Flask, request,render_template_string
+import pickle
+import warnings
+warnings.filterwarnings(action='ignore')
+app = Flask(__name__)
+# Load artifacts
+with open("freelancers.pkl", "rb") as f:
+    freelancers_df = pickle.load(f)
+with open("mlb.pkl", "rb") as f:
+    mlb = pickle.load(f)
+with open("scaler.pkl", "rb") as f:
+    scaler = pickle.load(f)
+with open("ranker_model.pkl", "rb") as f:
+    ranker = pickle.load(f)
+template = """
+<!DOCTYPE html>
+<html>
+<head>
+    <title>Freelancer Recommender</title>
+</head>
+<body style="font-family: Arial; padding: 40px;">
+    <h2>Freelancer Recommender</h2>
+    <form method="POST">
+        <label>Required Skills (comma-separated):</label><br>
+        <input type="text" name="skills" required><br><br>
+        <label>Budget:</label><br>
+        <input type="number" name="budget" required><br><br>
+        <label>Duration (in days):</label><br>
+        <input type="number" name="duration" required><br><br>
+        <input type="submit" value="Get Recommendations">
+    </form>
+    {% if top_freelancers %}
+        <h3>Top 5 Freelancers:</h3>
+        <table border="1" cellpadding="8">
+            <tr>
+                <th>ID</th>
+                <th>Score</th>
+            </tr>
+            {% for fid, score in top_freelancers %}
+            <tr>
+                <td>{{ fid }}</td>
+                <td>{{ score }}</td>
+            </tr>
+            {% endfor %}
+        </table>
+    {% endif %}
+</body>
+</html>
+"""
+@app.route("/", methods=["GET", "POST"])
+def recommend_freelancers_for_job():
+    top_freelancers = None
+    if request.method == "POST":
+        try:
+            skills = [s.strip() for s in request.form["skills"].split(",")]
+            budget = float(request.form["budget"])
+            duration = float(request.form["duration"])
+            job_skills_vec = mlb.transform(skills)[0]
+            job_vector = list(job_skills_vec) + [budget, duration]
+            scores = []
+            for _, freelancer in freelancers_df.iterrows():
+                freelancer_skills_vec = mlb.transform([freelancer["Skills"]])[0]
+                freelancer_vector = list(freelancer_skills_vec) + [
+                    freelancer["Hourly_Rate"],
+                    freelancer["Rating"],
+                    freelancer["Completed_Projects"]
+                ]
+                input_vec = job_vector + freelancer_vector
+                input_scaled = scaler.transform([input_vec])
+                score = ranker.predict(input_scaled)[0]
+                scores.append((freelancer["Freelancer_ID"], score))
+            top_freelancers = sorted(scores, key=lambda x: x[1], reverse=True)[:5]
+        except Exception as e:
+            return f"<h3>Error: {str(e)}</h3>"
+    return render_template_string(template, top_freelancers=top_freelancers)
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=7860)

freelancers.csv ADDED Viewed

	@@ -0,0 +1,1001 @@

+Freelancer_ID,Skills,Hourly_Rate,Rating,Completed_Projects
+F0000,"['Docker', 'AWS', 'SQL', 'JavaScript']",99.25,4.4,98
+F0001,"['NodeJS', 'Python', 'AWS', 'Django', 'JavaScript']",90.35,3.0,36
+F0002,"['React', 'NodeJS', 'Docker']",82.65,4.6,97
+F0003,"['Docker', 'Python']",81.66,3.8,85
+F0004,"['Docker', 'AWS', 'React']",28.79,3.6,42
+F0005,"['React', 'SQL']",47.74,3.4,91
+F0006,"['Python', 'NodeJS', 'React']",51.12,3.0,46
+F0007,"['Django', 'SQL', 'Docker']",16.75,4.6,15
+F0008,"['Python', 'NodeJS', 'JavaScript', 'Django']",61.46,3.6,18
+F0009,"['SQL', 'Docker']",25.67,4.8,8
+F0010,"['NodeJS', 'React']",63.56,3.4,9
+F0011,"['Python', 'NodeJS', 'Django', 'React', 'Docker']",93.87,3.8,93
+F0012,"['Docker', 'Python', 'SQL', 'React']",68.2,3.9,53
+F0013,"['Django', 'NodeJS']",37.5,3.5,36
+F0014,"['React', 'Python', 'SQL', 'AWS']",27.16,3.9,41
+F0015,"['AWS', 'Python', 'SQL', 'Django']",75.08,3.2,96
+F0016,"['Docker', 'JavaScript', 'React', 'AWS', 'Django']",31.32,3.6,26
+F0017,"['JavaScript', 'Python', 'Django', 'AWS', 'NodeJS']",59.65,3.1,61
+F0018,"['Python', 'Docker', 'AWS', 'React']",49.29,4.1,82
+F0019,"['SQL', 'AWS', 'React']",91.14,3.7,21
+F0020,"['Docker', 'Python', 'AWS']",46.83,4.3,97
+F0021,"['React', 'Docker', 'Python', 'NodeJS']",77.8,4.1,39
+F0022,"['Python', 'Docker', 'React']",83.54,4.9,96
+F0023,"['SQL', 'JavaScript', 'AWS', 'Docker', 'Python']",80.31,4.4,72
+F0024,"['SQL', 'React', 'Docker', 'JavaScript']",88.63,4.5,20
+F0025,"['React', 'NodeJS', 'Django', 'Python']",94.28,4.6,72
+F0026,"['Python', 'Django']",24.07,4.4,53
+F0027,"['React', 'Python', 'SQL', 'AWS']",33.22,4.9,7
+F0028,"['React', 'AWS', 'Python', 'Django']",54.74,3.9,96
+F0029,"['SQL', 'Python', 'React']",61.15,4.8,77
+F0030,"['React', 'AWS', 'Django']",35.09,4.1,48
+F0031,"['Python', 'NodeJS', 'Django', 'AWS', 'SQL']",88.83,3.7,52
+F0032,"['AWS', 'Docker']",21.04,4.2,53
+F0033,"['SQL', 'React', 'Docker']",40.45,3.0,77
+F0034,"['AWS', 'Docker', 'JavaScript', 'Django']",70.2,4.2,21
+F0035,"['Docker', 'SQL']",51.39,4.2,11
+F0036,"['AWS', 'Docker', 'SQL', 'Python', 'NodeJS']",49.91,4.4,84
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+F0058,"['Python', 'SQL']",49.02,3.1,5
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+F0060,"['SQL', 'JavaScript', 'AWS']",53.59,4.4,25
+F0061,"['Django', 'JavaScript', 'NodeJS', 'Docker', 'React']",17.82,4.2,23
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+F0082,"['Python', 'React', 'JavaScript', 'Django', 'AWS']",72.45,4.8,36
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+F0087,"['NodeJS', 'JavaScript']",82.55,4.6,34
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+F0089,"['Django', 'JavaScript']",68.08,3.4,18
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+F0091,"['NodeJS', 'Django', 'AWS', 'SQL', 'Docker']",36.68,3.9,59
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+F0094,"['JavaScript', 'Python']",42.6,4.8,28
+F0095,"['Docker', 'Django', 'AWS', 'JavaScript']",80.96,3.8,76
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+F0099,"['Docker', 'SQL', 'Python']",18.1,3.5,26
+F0100,"['JavaScript', 'React', 'Python', 'NodeJS']",79.88,4.2,63
+F0101,"['Python', 'Django', 'AWS', 'React', 'JavaScript']",54.23,4.6,9
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+F0137,"['JavaScript', 'NodeJS', 'AWS']",18.0,3.9,18
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+F0139,"['JavaScript', 'Python', 'React', 'Docker', 'AWS']",77.43,4.8,73
+F0140,"['React', 'SQL']",99.59,4.3,98
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freelancers.pkl ADDED Viewed

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+oid sha256:bef6843f1fefa1d8a7e5d473141c5101b0a2b920af970b99e59ca38517cd0303
+size 67554

jobs.csv ADDED Viewed

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+Job_ID,Required_Skills,Budget,Duration_Days
+J2000,"['JavaScript', 'NodeJS']",1107,25
+J2001,"['Python', 'SQL', 'JavaScript', 'Docker']",2179,11
+J2002,"['Python', 'React', 'Django']",4968,10
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+J2005,"['SQL', 'Python', 'Docker']",3035,12
+J2006,"['Django', 'AWS', 'Docker', 'Python']",347,21
+J2007,"['AWS', 'Django', 'SQL', 'Python']",3194,23
+J2008,"['React', 'AWS', 'Docker', 'Django']",2770,11
+J2009,"['AWS', 'Django']",806,26
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mlb.pkl ADDED Viewed

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+version https://git-lfs.github.com/spec/v1
+oid sha256:728bfb5ca20b80b9816b8da93286b38e29172e05387b39eb96b6926bdf8f33bf
+size 397

model_training.py ADDED Viewed

	@@ -0,0 +1,103 @@

+import pandas as pd
+import random
+import ast
+from sklearn.preprocessing import MultiLabelBinarizer, StandardScaler
+import lightgbm as lgb
+import pickle
+freelancers_df = pd.read_csv(r'freelancers.csv')
+jobs_df = pd.read_csv(r'jobs.csv')
+freelancers_df["Skills"] = freelancers_df["Skills"].apply(ast.literal_eval)
+jobs_df["Required_Skills"] = jobs_df["Required_Skills"].apply(ast.literal_eval)
+# converting text data into binary
+mlb = MultiLabelBinarizer()
+freelancer_skills = mlb.fit_transform(freelancers_df["Skills"])
+job_skills = mlb.transform(jobs_df["Required_Skills"])
+mlb.classes_
+"""*Combining the encoded features back to the dataframe*"""
+freelancer_features = pd.DataFrame(freelancer_skills, columns=[f"FSkill_{s}" for s in mlb.classes_])
+freelancer_features["Hourly_Rate"] = freelancers_df["Hourly_Rate"]
+freelancer_features["Rating"] = freelancers_df["Rating"]
+freelancer_features["Completed_Projects"] = freelancers_df["Completed_Projects"]
+freelancer_features["Freelancer_ID"] = freelancers_df["Freelancer_ID"]
+job_features = pd.DataFrame(job_skills, columns=[f"JSkill_{s}" for s in mlb.classes_])
+job_features["Budget"] = jobs_df["Budget"]
+job_features["Duration_Days"] = jobs_df["Duration_Days"]
+job_features["Job_ID"] = jobs_df["Job_ID"]
+# Job-Freelancer Interactions dataframe
+interactions = []
+for job in jobs_df.itertuples():
+    selected_freelancers = random.sample(list(freelancers_df.Freelancer_ID), 20)
+    hired = random.choice(selected_freelancers)
+    for f in selected_freelancers:
+        interactions.append({
+            "Job_ID": job.Job_ID,
+            "Freelancer_ID": f,
+            "Is_Hired": int(f == hired)
+        })
+interactions_df = pd.DataFrame(interactions)
+# Merge and build training set
+merged_df = interactions_df.merge(job_features, on="Job_ID").merge(freelancer_features, on="Freelancer_ID")
+X = merged_df.drop(columns=["Job_ID","Freelancer_ID","Is_Hired"])
+y = merged_df["Is_Hired"]
+# Grouping number of freelancers to job id.
+job_group = merged_df.groupby("Job_ID").size().to_list()
+# Scale numeric features
+scaler = StandardScaler()
+X_scaled = scaler.fit_transform(X)
+# Model training
+ranker = lgb.LGBMRanker(
+    objective="lambdarank",
+    metric="ndcg",
+    learning_rate=0.1,
+    n_estimators=100,
+    random_state=42
+)
+ranker.fit(X_scaled, y, group=job_group)
+# Save files
+with open("freelancers.pkl", "wb") as f:
+    pickle.dump(freelancers_df, f)
+with open("mlb.pkl", "wb") as f:
+    pickle.dump(mlb, f)
+with open("scaler.pkl", "wb") as f:
+    pickle.dump(scaler, f)
+with open("ranker_model.pkl", "wb") as f:
+    pickle.dump(ranker, f)
+print("Model training and data saving completed.")

ranker_model.pkl ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:8a718732ea0ec7828eabf996730f875a46a2ccec1179903d3e9d25e5962ea007
+size 303703

requirements.txt ADDED Viewed

	@@ -0,0 +1,6 @@

+pandas
+numpy
+lightgbm
+scikit-learn
+flask
+gunicorn

scaler.pkl ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:b72ef3d5a13303ffb00d2bb8ef82bb30e14b99c54ee44842ecf680a7a58b55ac
+size 1359