Delete app.py

app.py

@@ -1,124 +0,0 @@
-import gradio as gr
-import requests
-import pandas as pd
-import json
-
-# ----------------------------
-# CONFIG
-# ----------------------------
-JSON_URL = "https://file.notion.so/f/f/f86ed84d-b33c-4dfb-b0e0-97c5661516a3/3ed586a1-78e7-46af-9cf1-0961f95b5109/form-submissions-1.json?table=block&id=18a5392c-c93e-8054-b617-eb2a1a213d6c&spaceId=f86ed84d-b33c-4dfb-b0e0-97c5661516a3&expirationTimestamp=1758932214635&signature=sq1Jw2w3WoKIVMc8X078LO4SbfViD9ppdO0VXZ72Nro&downloadName=form-submissions.json"
-
-MODEL_ID = "HuggingFaceH4/zephyr-7b-beta"
-
-# Access Hugging Face secret in Spaces
-import os
-HF_API_TOKEN = os.environ.get("HF_API_TOKEN")
-if not HF_API_TOKEN:
-    raise ValueError("HF_API_TOKEN not found in environment. Add it in Space Secrets.")
-
-# ----------------------------
-# CATEGORIES DEFINED BY JOB TITLES
-# ----------------------------
-CATEGORIES = {
-    "AI": [
-        "AI/ML Ops Engineer","Senior Machine Learning Engineer","Principal Data Scientist",
-        "Senior Data Scientist","Machine Learning Research Scientist","Senior AI/ML Engineer",
-        "AI/ML Engineer","Big Data Engineer","AI Research Scientist","AI Research Analyst Consultant",
-        "AI Analyst","Senior Data Analyst","Automation Engineer","Senior Data Engineer",
-        "Machine Learning Engineer","Data Engineer","Data Scientist","Data Analyst"
-    ],
-    "Marketing": [
-        "Marketing Specialist","Sales Agent","Salesman","Sales Associate"
-    ],
-    "CTO": [
-        "Chief Technology Officer","CTO"
-    ],
-    "Legal": [
-        "Legal Specialist","Attorney","Legal Intern","Lawyer"
-    ],
-    "Finance": [
-        "Financial Analyst","Financial Advisor"
-    ]
-}
-
-# ----------------------------
-# HELPER FUNCTIONS
-# ----------------------------
-def fetch_json(url):
-    resp = requests.get(url)
-    resp.raise_for_status()
-    return resp.json()
-
-def call_zephyr(prompt):
-    headers = {
-        "Authorization": f"Bearer {HF_API_TOKEN}",
-        "Content-Type": "application/json"
-    }
-    payload = {"inputs": prompt}
-    response = requests.post(
-        f"https://api-inference.huggingface.co/models/{MODEL_ID}",
-        headers=headers,
-        data=json.dumps(payload),
-        timeout=60
-    )
-    if response.status_code != 200:
-        return f"Zephyr API error: {response.text}"
-    result = response.json()
-    if isinstance(result, dict) and "error" in result:
-        return f"Zephyr API error: {result['error']}"
-    return result[0].get("generated_text", "")
-
-def get_candidates_by_category(category_name, job_titles):
-    data = fetch_json(JSON_URL)
-    candidates = []
-    for person in data:
-        work_exps = person.get("work_experiences", [])
-        if len(work_exps) == 0:
-            continue
-        if any("full stack developer" in exp.get("roleName","").lower() for exp in work_exps):
-            continue
-
-        prompt = f"""
-You are an HR assistant. Determine if the following candidate is suitable for the category '{category_name}'.
-The category is defined by the job titles: {job_titles}
-
-Candidate JSON: {json.dumps(person)}
-
-Respond only 'Yes' or 'No'.
-"""
-        llm_response = call_zephyr(prompt)
-        if llm_response and "Yes" in llm_response:
-            candidates.append({
-                "Name": person.get("name"),
-                "Email": person.get("email"),
-                "Phone": person.get("phone"),
-                "Location": person.get("location"),
-                "Roles": ", ".join([exp.get("roleName") for exp in work_exps]),
-                "Skills": ", ".join(person.get("skills", [])),
-                "Salary": person.get("annual_salary_expectation", {}).get("full-time", "N/A")
-            })
-    if len(candidates) == 0:
-        return f"No suitable candidates found for {category_name}."
-    return pd.DataFrame(candidates)
-
-# ----------------------------
-# GRADIO INTERFACE
-# ----------------------------
-def run_dashboard(category):
-    if category not in CATEGORIES:
-        return f"Category {category} not found."
-    df = get_candidates_by_category(category, CATEGORIES[category])
-    return df
-
-category_options = list(CATEGORIES.keys())
-
-demo = gr.Interface(
-    fn=run_dashboard,
-    inputs=gr.Dropdown(category_options, label="Select Category"),
-    outputs=gr.Dataframe(label="Suitable Candidates"),
-    live=False,
-    title="Startup Candidate Dashboard - Zephyr-7B-Beta"
-)
-
-if __name__ == "__main__":
-    demo.launch()