Create app.py

app.py

@@ -0,0 +1,81 @@
+# app.py
+import os
+import json
+import pandas as pd
+import google.generativeai as genai
+import gradio as gr
+
+# Configure Gemini
+genai.configure(api_key=os.environ["GEMINI_API_KEY"])
+model = genai.GenerativeModel("gemini-pro")
+
+# Load and clean CSV
+df_raw = pd.read_csv("data/assessments.csv")
+
+def preprocess_data(df):
+    def clean_duration(text):
+        try:
+            return int(text.split('=')[-1].strip())
+        except:
+            return None
+
+    def clean_support(val):
+        return "Yes" if val == 'T' else "No"
+
+    def clean_test_type(val):
+        return [x.strip() for x in str(val).split('\n') if x.strip()]
+
+    df_cleaned = pd.DataFrame({
+        "url": "https://www.shl.com" + df.iloc[:, 2].astype(str),
+        "remote_support": df.iloc[:, 3].apply(clean_support),
+        "adaptive_support": df.iloc[:, 4].apply(clean_support),
+        "test_type": df.iloc[:, 5].apply(clean_test_type),
+        "description": df.iloc[:, 6],
+        "duration": df.iloc[:, 9].apply(clean_duration),
+    })
+    return df_cleaned
+
+assessments = preprocess_data(df_raw)
+
+def recommend_assessments(query, top_k=10):
+    prompt = f"""
+    Given this job description: "{query}", recommend the top {top_k} relevant SHL assessments from the following list.
+    Return the result as JSON with this format:
+    {{
+        "recommended_assessments": [
+            {{
+                "url": ..., 
+                "adaptive_support": ..., 
+                "remote_support": ..., 
+                "description": ..., 
+                "duration": ..., 
+                "test_type": [...]
+            }},
+            ...
+        ]
+    }}
+
+    Data:
+    {assessments.to_dict(orient='records')}
+    """
+
+    response = model.generate_content(prompt)
+    try:
+        result = json.loads(response.text)
+        return result
+    except Exception as e:
+        return {"error": f"Failed to parse response: {str(e)}\n{response.text}"}
+
+def predict(query):
+    return recommend_assessments(query)
+
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Textbox(label="Enter Job Description", lines=4),
+    outputs="json",
+    title="SHL Assessment Recommender (Gemini-powered)",
+    description="Paste a job description and get the most relevant SHL assessments."
+)
+
+if __name__ == "__main__":
+    iface.launch()