Create app.py

app.py

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+import os
+import gradio as gr
+import requests
+from huggingface_hub import InferenceClient
+
+# Load Gemini API key from environment variable
+GEMINI_API_KEY = os.getenv("GOOGLE_AI_API_KEY")
+HF_API_KEY = os.getenv("HUGGINGFACE_API_KEY")  # optional if private model
+
+# Hugging Face Client (free model, no API key needed for public models)
+hf_client = InferenceClient(model="HuggingFaceH4/zephyr-7b-beta")
+
+def generate_with_huggingface(resume_text, job_desc):
+    prompt = f"""
+    You are an expert career assistant. 
+    Resume:
+    {resume_text}
+
+    Job Description:
+    {job_desc}
+
+    Task:
+    1. Create a customized resume version highlighting relevant skills and achievements.
+    2. Write a professional cover letter tailored for this role.
+    """
+
+    response = hf_client.text_generation(
+        prompt,
+        max_new_tokens=800,
+        temperature=0.7,
+    )
+    return response
+
+
+def call_gemini_api(resume_text, job_desc):
+    if not GEMINI_API_KEY:
+        return None  # force fallback
+
+    prompt = f"""
+    You are an expert career assistant. 
+    Resume:
+    {resume_text}
+
+    Job Description:
+    {job_desc}
+
+    Task:
+    1. Create a customized resume version highlighting relevant skills and achievements.
+    2. Write a professional cover letter tailored for this role.
+    """
+
+    url = "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent"
+    headers = {
+        "Content-Type": "application/json",
+        "X-goog-api-key": GEMINI_API_KEY,
+    }
+    data = {
+        "contents": [
+            {"parts": [{"text": prompt}]}
+        ]
+    }
+
+    try:
+        response = requests.post(url, headers=headers, json=data, timeout=30)
+        result = response.json()
+
+        if "candidates" in result:
+            output_text = result["candidates"][0]["content"]["parts"][0]["text"]
+            return output_text
+        else:
+            return None
+    except Exception:
+        return None
+
+
+def generate_documents(resume_text, job_desc):
+    # Try Gemini first
+    gemini_output = call_gemini_api(resume_text, job_desc)
+
+    if gemini_output:
+        output_text = gemini_output
+        source = "✅ Google Gemini"
+    else:
+        output_text = generate_with_huggingface(resume_text, job_desc)
+        source = "⚠️ Gemini failed → Using Hugging Face LLM"
+
+    # Split Resume + Cover Letter
+    if "Cover Letter" in output_text:
+        parts = output_text.split("Cover Letter")
+        resume_out = parts[0].strip()
+        cover_letter_out = "Cover Letter" + parts[1].strip()
+    else:
+        resume_out = output_text
+        cover_letter_out = ""
+
+    return resume_out + f"\n\n(Source: {source})", cover_letter_out
+
+
+# Gradio UI
+with gr.Blocks(title="AI Resume & Cover Letter Generator") as demo:
+    gr.Markdown("# 📄 AI Resume & Cover Letter Generator")
+    gr.Markdown("Upload your resume or paste LinkedIn profile + Job description, and get customized documents.\
+                 \n(Default: Gemini, fallback: Hugging Face LLM)")
+
+    with gr.Row():
+        resume_input = gr.Textbox(label="Paste Resume / LinkedIn profile", lines=10, placeholder="Paste your resume text here...")
+        job_input = gr.Textbox(label="Paste Job Description", lines=8, placeholder="Paste job description here...")
+
+    generate_btn = gr.Button("✨ Generate Resume & Cover Letter")
+
+    with gr.Row():
+        resume_output = gr.Textbox(label="Customized Resume", lines=15)
+        cover_output = gr.Textbox(label="Cover Letter", lines=15)
+
+    generate_btn.click(generate_documents, inputs=[resume_input, job_input], outputs=[resume_output, cover_output])
+
+demo.launch()