Create app.py

app.py

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+import os
+import gradio as gr
+import whisper
+import re
+import datetime
+import pandas as pd
+import google.generativeai as genai
+
+# ✅ Configure Gemini API
+genai.configure(api_key="AIzaSyAeNCjKJVCT0gmQRAPq4NltXkc-1zELH28")
+model = genai.GenerativeModel("gemini-1.5-flash-latest")
+asr_model = whisper.load_model("base")
+
+# ✅ Global session state
+session = {
+    "username": "",
+    "company": "",
+    "role": "",
+    "questions": [],
+    "index": 0,
+    "feedback": [],
+    "interview_done": False
+}
+
+# ✅ Dummy public data with full analysis
+public_data = pd.DataFrame([
+    {"Username": "Flipkart employee", "Company": "amazon", "Role": "services", "Date": "2025-05-27", "Tone (%)": 60, "Vocabulary (%)": 50, "Grammar (%)": 40, "Technical (%)": 30},
+    {"Username": "NIELT", "Company": "Dmart", "Role": "Marketing and Sales Funnel", "Date": "2025-05-22", "Tone (%)": 74, "Vocabulary (%)": 70, "Grammar (%)": 76, "Technical (%)": 69},
+    {"Username": "AIML Saddi", "Company": "Nykaa", "Role": "Product Manager", "Date": "2025-05-27", "Tone (%)": 86, "Vocabulary (%)": 79, "Grammar (%)": 81, "Technical (%)": 88},
+    {"Username": "AIML Deepanshu", "Company": "Unilever", "Role": "Brand Manager", "Date": "2025-05-27", "Tone (%)": 80, "Vocabulary (%)": 78, "Grammar (%)": 83, "Technical (%)": 82},
+    {"Username": "Rahul Ropar", "Company": "Godrej", "Role": "B2B Sales", "Date": "2025-05-23", "Tone (%)": 72, "Vocabulary (%)": 75, "Grammar (%)": 69, "Technical (%)": 65},
+    {"Username": "Vidit789", "Company": "Amazon", "Role": "Cloud Security Analyst", "Date": "2025-05-23", "Tone (%)": 90, "Vocabulary (%)": 82, "Grammar (%)": 87, "Technical (%)": 91},
+    {"Username": "DSP Dev", "Company": "Tesla", "Role": "Autopilot QA", "Date": "2025-05-22", "Tone (%)": 84, "Vocabulary (%)": 77, "Grammar (%)": 81, "Technical (%)": 79}
+])
+
+# ✅ Start interview
+def start_interview(username, company, role):
+    if not username or not company or not role:
+        return "❌ Please fill all fields first."
+    session.update({
+        "username": username,
+        "company": company,
+        "role": role,
+        "questions": [],
+        "index": 0,
+        "feedback": [],
+        "interview_done": False
+    })
+    greeting = (
+        "Hello, I am Mrinankush Dutta.\n\n"
+        "Welcome to your AI Mock interview session.\n\n"
+        "This Mock interview provides tailored support for interviewees in real time.\n\n"
+        "Just focus on being yourself. We will handle the rest."
+    )
+    return greeting
+
+# ✅ Generate interview questions
+def generate_questions():
+    if not session["company"] or not session["role"]:
+        return "❌ Please complete setup first."
+    prompt = f"Generate 13 internship interview questions (8 technical and 5 behavioral) for the role of {session['role']} at {session['company']}. Number them 1 to 13."
+    response = model.generate_content(prompt)
+    session["questions"] = re.findall(r"^\d+\.\s+.*", response.text.strip(), re.MULTILINE)
+    return next_question()
+
+# ✅ Show next question
+def next_question():
+    if session["index"] >= len(session["questions"]):
+        session["interview_done"] = True
+        return "Interview complete. Click 'Finish' to view feedback."
+    return session["questions"][session["index"]]
+
+# ✅ Record and process answer
+def process_answer(audio):
+    if audio is None:
+        return "❌ Please record your answer."
+    result = asr_model.transcribe(audio)
+    transcript = result["text"]
+    q = session["questions"][session["index"]]
+    session["index"] += 1
+    prompt = (
+        f"Interview Question: {q}\n"
+        f"Answer: {transcript}\n\n"
+        "Evaluate the following in percentage:\n"
+        "Tone, Grammar, Vocabulary, and Technical correctness.\n"
+        "Respond in this format:\n"
+        "Tone: %\nGrammar: %\nVocabulary: %\nTechnical: %"
+    )
+    feedback = model.generate_content(prompt).text.strip()
+    session["feedback"].append({"question": q, "answer": transcript, "feedback": feedback})
+    return "✅ Answer recorded."
+
+# ✅ Compile full feedback summary
+def feedback_summary():
+    session["interview_done"] = True
+    summary = ""
+    for i, entry in enumerate(session["feedback"], 1):
+        summary += f"Q{i}: {entry['question']}\nAnswer: {entry['answer']}\n{entry['feedback']}\n{'-'*40}\n"
+    return summary
+
+# ✅ Public sharing after confirmation
+def make_public(share_decision):
+    if not session["interview_done"]:
+        return "❌ Complete interview first."
+    if share_decision.lower() != "yes":
+        return "✅ Interview ended. Your data was not shared publicly."
+    last_feedback = session["feedback"][-1]["feedback"]
+    tone = re.search(r"Tone:\s*(\d+)%", last_feedback)
+    grammar = re.search(r"Grammar:\s*(\d+)%", last_feedback)
+    vocab = re.search(r"Vocabulary:\s*(\d+)%", last_feedback)
+    tech = re.search(r"Technical:\s*(\d+)%", last_feedback)
+    today = datetime.date.today().strftime("%Y-%m-%d")
+    new_row = {
+        "Username": session["username"],
+        "Company": session["company"],
+        "Role": session["role"],
+        "Date": today,
+        "Tone (%)": int(tone.group(1)) if tone else 70,
+        "Vocabulary (%)": int(vocab.group(1)) if vocab else 70,
+        "Grammar (%)": int(grammar.group(1)) if grammar else 70,
+        "Technical (%)": int(tech.group(1)) if tech else 70
+    }
+    global public_data
+    public_data.loc[len(public_data)] = new_row
+    return "✅ Your analysis has been shared publicly."
+
+# ✅ View public profiles as DataFrame
+def show_public():
+    if not session["interview_done"]:
+        return public_data.iloc[0:0]  # Empty table if not finished
+    return public_data
+
+# ✅ Gradio UI
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# 🧠 AI MOCK INTERVIEW BUDDY")
+
+    uname = gr.Textbox(label="👤 Unique Username")
+    comp = gr.Textbox(label="🏢 Company")
+    role = gr.Textbox(label="🎯 Role")
+    start_btn = gr.Button("🚀 Start Interview")
+    greet = gr.Textbox(label="Greeting")
+    start_btn.click(start_interview, inputs=[uname, comp, role], outputs=greet)
+
+    gen_btn = gr.Button("📋 Generate Questions")
+    qbox = gr.Textbox(label="Current Question")
+    gen_btn.click(generate_questions, outputs=qbox)
+
+    record = gr.Audio(sources=["microphone"], type="filepath", label="🎤 Record Answer")
+    submit = gr.Button("✅ Submit")
+    result = gr.Textbox(label="Status")
+    submit.click(process_answer, inputs=record, outputs=result)
+
+    next_btn = gr.Button("➡ Next Question")
+    next_btn.click(next_question, outputs=qbox)
+
+    final = gr.Button("📊 End Interview")
+    fb = gr.Textbox(label="📄 Final Feedback", lines=15)
+    final.click(feedback_summary, outputs=fb)
+
+    confirm = gr.Textbox(label="Do you want to share publicly? (Yes/No)")
+    share_btn = gr.Button("☑ Share Final Results")
+    status = gr.Textbox(label="Sharing Status")
+    share_btn.click(make_public, inputs=confirm, outputs=status)
+
+    view = gr.Button("📣 View Public Profiles")
+    public_table = gr.Dataframe(label="🧠 Public Summary Table", interactive=False)
+    view.click(show_public, outputs=public_table)
+
+# ✅ Fixed launch block
+if __name__ == "__main__":
+    demo.launch()