Update app.py

app.py

@@ -4,146 +4,137 @@ import os
 import PyPDF2
 from io import BytesIO
 
-# API config
+# Hugging Face API and model
 HF_API_TOKEN = os.getenv("HF_API_TOKEN")
 HF_API_URL = "https://api-inference.huggingface.co/models/HuggingFaceH4/zephyr-7b-beta"
 MODEL_CHECK_PROMPT = "Say hello!"
 
-# --- API/Model Health Check ---
+# ✅ Check whether the API key and model are working correctly
 def check_api_ready():
     if not HF_API_TOKEN:
-        return False, "❌ HF_API_TOKEN not set. Please add your Hugging Face token as a repo secret."
+        return False, "❌ HF_API_TOKEN not set. Please add in Space secrets."
     headers = {"Authorization": f"Bearer {HF_API_TOKEN}"}
     try:
-        resp = requests.post(HF_API_URL, headers=headers, json={"inputs": MODEL_CHECK_PROMPT}, timeout=15)
+        resp = requests.post(HF_API_URL, headers=headers, json={"inputs": MODEL_CHECK_PROMPT}, timeout=20)
         if resp.status_code == 200:
             data = resp.json()
             if isinstance(data, dict) and "error" in data:
-                return False, f"❌ Model error: {data['error']}"
+                return False, f"❌ Model Error: {data['error']}"
             if isinstance(data, list) and data[0].get("generated_text"):
-                return True, "✅ Model is up and responding!"
+                return True, "✅ Model is ready!"
         elif resp.status_code == 401:
-            return False, "❌ Unauthorized. API token missing or invalid."
+            return False, "❌ Unauthorized. Check your API token."
         else:
-            return False, f"❌ Unexpected API error: {resp.text}"
+            return False, f"❌ Unexpected API response: {resp.text}"
     except Exception as e:
-        return False, f"❌ API connection error: {str(e)}"
+        return False, f"❌ API connection failed: {str(e)}"
 
-# --- Lottie Animation Sidebar ---
+# ✅ Tooltip animation HTML from Lottie JSON (place Robotics-Students.json in Space root)
 def lottie_html():
-    # Robotics-Students.json must exist in app root folder
     return """
     <script src="https://unpkg.com/@lottiefiles/lottie-player@latest/dist/lottie-player.js"></script>
     <lottie-player src='Robotics-Students.json' background='transparent' speed='1' style='width:340px; height:340px; margin-bottom:12px;' loop autoplay></lottie-player>
     """
 
-# --- PDF Text Extraction ---
+# ✅ PDF text extractor
 def extract_text_from_pdf(pdf_file):
-    pdf_reader = PyPDF2.PdfReader(BytesIO(pdf_file.read()))
-    text = ""
-    for page in pdf_reader.pages:
-        content = page.extract_text()
-        if content:
-            text += content + "\n"
-    return text.strip()
-
-# --- AI-Powered Interview Questions ---
+    reader = PyPDF2.PdfReader(BytesIO(pdf_file.read()))
+    return "\n".join([page.extract_text() or "" for page in reader.pages]).strip()
+
+# ✅ Generate questions and answers using language model
 def ai_generate_questions(resume_text, job_title):
     prompt = (
-        f"You are an AI interview prep coach.\n"
+        f"You are an AI interview coach.\n"
         f"Candidate Resume:\n{resume_text}\n"
         f"Target Role: {job_title}\n"
-        f"Generate 10 realistic interview questions based on the resume, "
-        f"and add a 1–2 sentence coaching tip for each."
+        f"Generate 10 realistic interview questions based on this person’s resume, "
+        f"and for each question provide a coaching tip to help them answer effectively."
     )
     headers = {"Authorization": f"Bearer {HF_API_TOKEN}"}
-    payload = {
-        "inputs": prompt,
-        "parameters": {"max_new_tokens": 512, "temperature": 0.7, "return_full_text": False},
-    }
+    payload = {"inputs": prompt, "parameters": {"max_new_tokens": 512, "temperature": 0.7}}
+
     try:
         response = requests.post(HF_API_URL, headers=headers, json=payload, timeout=60)
         data = response.json()
-        if isinstance(data, dict) and "error" in data:
+        if "error" in data:
             return f"<div style='color:red;'>❌ API Error: {data['error']}</div>"
         return data[0]["generated_text"].strip()
     except Exception as e:
         return f"<div style='color:red;'>❌ Exception: {str(e)}</div>"
 
-# --- HTML Output Rendering ---
-def render_output_html(is_ready, msg, name, role, status, queue, inner_html):
-    sidebar = lottie_html() if is_ready else ""
-    status_panel = (
-        f"<div style='background:#f4f6fa;padding:18px;border-radius:16px;max-width:340px;box-shadow:0 2px 14px #ccd4e6b3;font-family:sans-serif;'>"
-        f"<div style='font-size:1.25rem;font-weight:700;'>ROBOT RESUME ANALYZER</div>"
-        f"<div style='margin-top:10px;'><b>Status:</b> <span style='color:#058c42'>{status}</span></div>"
-        f"<div style='margin-top:10px;'><b>Current:</b> {name}</div>"
-        f"<div><b>Position:</b> {role}</div>"
-        f"<div style='margin-top:10px;'><b>Analysis Progress:</b></div>"
-        f"<div style='font-size:2rem;'>👥</div>"
-        f"<div><b>QUEUE</b></div>"
-        f"<div style='font-size:1.5rem;font-weight:700;'>{queue}</div>"
-        f"<div><i>Analyzing Next...</i></div>"
-        f"{('<div style=\"color:#46af41; font-weight:600; margin-top:10px;\">'+msg+'</div>') if msg else ''}"
-        f"</div>"
-    )
-    return (
-        sidebar +
-        status_panel +
-        "<div style='max-width:620px;margin-left:30px;display:inline-block;vertical-align:top;'>" +
-        inner_html +
-        "</div>"
-    )
+# ✅ Format final output HTML
+def render_output(name, job, status, queue, messages, model_message, show_lottie=True):
+    lottie_side = lottie_html() if show_lottie else ""
+    model_msg = f"<div style='margin-top:12px;font-size:0.9rem;color:#46a546;'>{model_message}</div>" if model_message else ""
+    left_panel = f"""
+        {lottie_side}
+        <div style='background:#f4f6fa;padding:18px;border-radius:16px;
+        max-width:340px;box-shadow:0 2px 14px #ccd4e6b3;font-family:sans-serif;'>
+            <div style='font-size:1.25rem;font-weight:700;'>🤖 ROBOT RESUME ANALYZER</div>
+            <div style='margin-top:10px;'><b>Status:</b> <span style='color:#058c42'>{status}</span></div>
+            <div style='margin-top:10px;'><b>Current:</b> {name}</div>
+            <div><b>Position:</b> {job}</div>
+            <div style='margin-top:10px;'><b>Analysis Progress:</b></div>
+            <div style='font-size:2rem;'>👥</div>
+            <div><b>QUEUE</b></div>
+            <div style='font-size:1.5rem;font-weight:700;'>{queue}</div>
+            <div><i>Analyzing Next...</i></div>
+            {model_msg}
+        </div>
+    """
+
+    return f"""
+    <div style='display:flex;gap:30px;'>
+        {left_panel}
+        <div style='max-width:620px;margin-top:15px;'>{messages}</div>
+    </div>
+    """
 
-# --- Main App Logic ---
+# ✅ Main app logic with staged UI updates
 def interface(pdf_file, job_title):
     is_ready, msg = check_api_ready()
     if not is_ready:
-        out = f"<div style='color:red; font-size:1.1em;'><b>API/model check failed:</b> <br>{msg}</div>"
-        return render_output_html(False, msg, "---", "---", "API/Model Error", 1, out)
+        err_html = f"<div style='color:red;'><b>ERROR:</b> {msg}</div>"
+        return render_output("---", "---", "Unavailable", 0, err_html, msg, show_lottie=False)
 
     if not pdf_file:
-        out = '<span style="color:red;">Please upload a PDF resume.</span>'
-        return render_output_html(True, msg, "---", "---", "No resume uploaded", 1, out)
+        return render_output("---", "---", "Waiting for PDF", 1, "<b>Please upload a PDF resume.</b>", msg)
+
     if not job_title.strip():
-        name = pdf_file.name.replace('.pdf', '')
-        out = '<span style="color:red;">Please enter your target job title.</span>'
-        return render_output_html(True, msg, name, "---", "Waiting for job input", 1, out)
+        name = pdf_file.name.replace('.pdf','')
+        return render_output(name, "---", "Waiting for Job Title", 1, "<b>Please enter a target job title.</b>", msg)
 
-    name = pdf_file.name.replace('.pdf', '')
+    name = pdf_file.name.replace(".pdf", "")
     try:
         pdf_file.seek(0)
         resume_text = extract_text_from_pdf(pdf_file)
         if not resume_text:
-            raise Exception("No text found in PDF.")
+            raise Exception("PDF contains no readable text")
     except Exception as e:
-        out = f"<div style='color:red;'>Failed to read PDF: {e}</div>"
-        return render_output_html(True, msg, name, job_title, "Error", 0, out)
+        return render_output(name, job_title, "PDF Error", 1, f"<div style='color:red;'>Failed to extract PDF: {e}</div>", msg)
 
-    yield render_output_html(True, msg, name, job_title, "Analyzing...", 1,
-        "<i>⏳ Sending resume to AI coach, please wait...</i>"
-    )
+    yield render_output(name, job_title, "Analyzing...", 1, "<i>Generating questions with AI...</i>", msg)
+
+    ai_output = ai_generate_questions(resume_text, job_title)
+    questions = [q.strip() for q in ai_output.split("\n") if q.strip()]
 
-    ai_response = ai_generate_questions(resume_text, job_title)
-    questions = [q.strip() for q in ai_response.split("\n") if q.strip()]
-    output = "<div style='margin-top:20px;'><b>✅ Interview Questions + Tips:</b></div>"
+    html = "<h3>✅ Interview Questions & Coaching Tips</h3>"
     for i, q in enumerate(questions[:10], 1):
-        output += f"<div style='background:#fff;border-radius:10px;padding:14px;box-shadow:0 1px 5px #dde5f4ba;margin-top:10px;'><b>Q{i}:</b> {q}</div>"
+        html += f"<div style='background:#fff;padding:16px;border-radius:10px;margin-bottom:12px;box-shadow:0 2px 6px #ddd;'>"
+        html += f"<b>Q{i}:</b> {q}</div>"
 
-    yield render_output_html(True, msg, name, job_title, "Done ✅", 0, output)
+    yield render_output(name, job_title, "Done ✅", 0, html, msg)
 
+# ✅ Launch Gradio app
 with gr.Interface(
     fn=interface,
     inputs=[
-        gr.File(label="Upload Resume (PDF)", file_types=[".pdf"], type="binary"),
-        gr.Textbox(label="Target Job Title", placeholder="e.g. Machine Learning Engineer"),
+        gr.File(label="Upload PDF Resume", type="binary", file_types=[".pdf"]),
+        gr.Textbox(label="Target Job Title", placeholder="e.g. Data Analyst"),
     ],
     outputs=gr.HTML(),
-    title="Robot Resume Analyzer + AI Interview Coach (PDF Powered)",
-    live=False,
     allow_flagging="never",
+    title="🤖 AI Resume Analyzer + Interview Coach (PDF + Lottie)",
+    live=False,
 ) as demo:
-    pass
-
-if __name__ == "__main__":
     demo.launch()