# Job Tracker Agent - First Version with Gradio + JSON Memory + CSV Export (No OpenAI Key)
# - Uses Hugging Face Inference API (Mistral-7B)
# - Extracts job info from text
# - Saves to JSON + allows CSV export

import gradio as gr
import os
import json
import csv
import requests
from datetime import datetime

JOB_LOG_FILE = "job_memory.json"
CSV_EXPORT_FILE = "job_memory.csv"
HF_API_URL = "https://api-inference.huggingface.co/models/HuggingFaceH4/zephyr-7b-beta"


# Optional: use your Hugging Face token for more reliability
HF_API_TOKEN = os.getenv("HUG_API_TOKEN")  # or leave None for free/public use

headers = {"Authorization": f"Bearer {HF_API_TOKEN}"} if HF_API_TOKEN else {}

# Initialize memory file
if not os.path.exists(JOB_LOG_FILE):
    with open(JOB_LOG_FILE, "w") as f:
        json.dump([], f)

# Extract job info using Hugging Face Inference API

def extract_job_details(text):
    prompt = f"""
Extract structured JSON with the following fields from the input text:
- company
- role
- location
- source
- status
- link (if present)
- follow_up_date (in YYYY-MM-DD if mentioned, else null)

Input:
{text}

Output:
"""
    payload = {
        "inputs": prompt,
        "parameters": {"max_new_tokens": 500},
        "options": {"wait_for_model": True}
    }
    try:
        response = requests.post(HF_API_URL, headers=headers, json=payload)
        response.raise_for_status()
        result = response.json()
        if isinstance(result, dict) and "error" in result:
            return json.dumps(result)
        if isinstance(result, list) and "generated_text" in result[0]:
            return result[0]["generated_text"].split("Output:")[-1].strip()
        return json.dumps({"error": "Unexpected response format"})
    except Exception as e:
        return json.dumps({"error": str(e)})

# Store to JSON file
def store_job_data(parsed_json):
    try:
        job_data = json.loads(parsed_json)
        job_data["logged_at"] = datetime.now().isoformat()
        with open(JOB_LOG_FILE, "r+") as f:
            existing = json.load(f)
            existing.append(job_data)
            f.seek(0)
            json.dump(existing, f, indent=2)
        return "✅ Job logged successfully."
    except Exception as e:
        return f"❌ Error logging job: {e}"

# Read memory safely with missing key handling
def get_job_history():
    with open(JOB_LOG_FILE, "r") as f:
        entries = json.load(f)
    formatted = "\n\n".join([
        f"{e.get('role', '[Missing Role]')} at {e.get('company', '[Missing Company]')} ({e.get('status', 'Unknown')}) - {e.get('follow_up_date', 'N/A')}"
        for e in entries
    ])
    return formatted or "No jobs tracked yet."

# Export to CSV
def export_to_csv():
    try:
        with open(JOB_LOG_FILE, "r") as f:
            entries = json.load(f)
        if entries:
            keys = set()
            for entry in entries:
                keys.update(entry.keys())
            keys = sorted(keys)
            with open(CSV_EXPORT_FILE, "w", newline='') as f:
                writer = csv.DictWriter(f, fieldnames=keys)
                writer.writeheader()
                writer.writerows(entries)
            return f"✅ Exported to {CSV_EXPORT_FILE}"
        return "❌ No job data to export."
    except Exception as e:
        return f"❌ Export failed: {e}"

# Gradio logic
def job_tracker_interface(user_input):
    parsed_json = extract_job_details(user_input)
    if "error" in parsed_json:
        return parsed_json, "❌ Failed to log job.", get_job_history()
    store_status = store_job_data(parsed_json)
    memory_state = get_job_history()
    return parsed_json, store_status, memory_state

def export_interface():
    return export_to_csv()

# Gradio app
demo = gr.Interface(
    fn=job_tracker_interface,
    inputs=gr.Textbox(lines=10, label="Paste a job description or job-related email"),
    outputs=[
        gr.Textbox(label="🧠 Extracted Job Info (JSON)"),
        gr.Textbox(label="📥 Save Status"),
        gr.Textbox(label="📂 Job Tracker Memory (Current Session)")
    ],
    title="Job Tracker AI (v1) - No OpenAI Key",
    description="Paste job descriptions or emails. This AI agent uses Mistral-7B to extract job data, save it in memory, and export to CSV."
)

export_demo = gr.Interface(
    fn=export_interface,
    inputs=[],
    outputs=gr.Textbox(label="📤 Export Result"),
    title="Export to CSV",
    description="Click the button below to export your job data to a CSV file."
)

demo.launch()
# Optionally launch CSV export in a separate tab
# export_demo.launch()