Update app.py

app.py

@@ -1,57 +1,109 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta") #change LLM
 
+# In-memory user profile
 user_profile = {
-    "interview_type": None,
-    "field": None,
-    "mode": "text",
-    "stage": "select_type",  # stages: select_type → get_background → interview → done
+    "interview_type": "",
+    "field": "",
+    "mode": "",
+    "responses": []
+}
+
+# Question bank
+questions = {
+    "technical": {
+        "software": [
+            "Can you explain a recent project where you solved a technical challenge?",
+            "How would you optimize a slow-running algorithm?"
+        ],
+        "general": [
+            "Explain Big O Notation.",
+            "What’s a common bug and how do you fix it?"
+        ]
+    },
+    "behavioral": {
+        "general": [
+            "Tell me about a time you had to overcome a challenge.",
+            "How do you handle feedback?"
+        ]
+    },
+    "college": {
+        "general": [
+            "Why do you want to attend this school?",
+            "What extracurricular activity means the most to you and why?"
+        ]
+    }
 }
 
-def respond(message, history):
-    global user_profile
-
-    # Stage 1: Choose interview type
-    if user_profile["stage"] == "select_type":
-        user_profile["interview_type"] = message.lower()
-        user_profile["stage"] = "get_background"
-        yield f"Great! What’s your background and what field are you aiming for?"
-        return
-
-    # Stage 2: Get background info
-    elif user_profile["stage"] == "get_background":
-        user_profile["field"] = message
-        user_profile["stage"] = "interview"
-        yield f"Thanks! Let’s begin your {user_profile['interview_type']} interview. Type 'start' to begin."
-        return
-
-    # Stage 3: Start asking interview questions
-    elif user_profile["stage"] == "interview":
-        if message.lower() == "start":
-            intro = f"Let’s begin! I’ll ask you questions based on a {user_profile['interview_type']} interview in {user_profile['field']}."
-            return_stream = client.text_generation(
-                f"Ask a good first interview question for a {user_profile['interview_type']} interview in the {user_profile['field']} field.",
-                max_tokens=100,
-                stream=True
-            )
-            for token in return_stream:
-                yield intro + "\n\n" + token
-        elif message.lower() == "done":
-            user_profile["stage"] = "done"
-            yield "Thanks for completing the interview! I’ll now evaluate your performance... (WIP)"
-        else:
-            messages = [{"role": "system", "content": "You are a professional interviewer. Give feedback or ask follow-up questions."}]
-            messages.extend(history)
-            messages.append({"role": "user", "content": message})
-            response = ""
-            for message in client.chat_completion(messages, max_tokens=100, stream=True):
-                token = message.choices[0].delta.content
-                response += token
-                yield response
-
-
-chatbot = gr.ChatInterface(respond, type="messages", title="Intervu - AI Interview Practice")
-chatbot.launch()
+# Phase 1: Choose interview type
+def set_type(choice):
+    user_profile["interview_type"] = choice.lower()
+    return "Great! What’s your background and what field/role are you aiming for?"
+
+# Phase 2: Save background
+def save_background(info):
+    user_profile["field"] = info
+    return "Would you like to continue with Text, Audio, or Webcam interview?"
+
+# Phase 3: Set mode
+def set_mode(choice):
+    user_profile["mode"] = choice.lower()
+    return f"Starting {choice} interview... Type 'done' anytime to finish."
+
+# Phase 4: Ask question
+def start_interview(user_input):
+    if user_input.lower() == "done":
+        return "Interview finished! We'll provide feedback shortly."
+
+    user_profile["responses"].append(user_input)
+
+    itype = user_profile["interview_type"]
+    field = user_profile["field"].lower()
+
+    field_key = "software" if "software" in field else "general"
+    question_list = questions.get(itype, {}).get(field_key, [])
+
+    if len(user_profile["responses"]) < len(question_list):
+        return question_list[len(user_profile["responses"])]
+    else:
+        return "Thank you! You've completed all questions. Type 'done' to finish."
+
+# UI construction
+with gr.Blocks() as demo:
+    gr.Markdown("# 🎤 Welcome to Intervu")
+
+    with gr.Row():
+        gr.Markdown("## Step 1: Choose Interview Type")
+        type_out = gr.Textbox(label="Bot Response")
+
+    with gr.Row():
+        btn1 = gr.Button("Behavioral")
+        btn2 = gr.Button("Technical")
+        btn3 = gr.Button("College / Scholarship")
+
+    btn1.click(set_type, inputs=[], outputs=type_out, _js="() => 'Behavioral'")
+    btn2.click(set_type, inputs=[], outputs=type_out, _js="() => 'Technical'")
+    btn3.click(set_type, inputs=[], outputs=type_out, _js="() => 'College'")
+
+    gr.Markdown("## Step 2: Enter Background")
+    background = gr.Textbox(label="Your background + field/goal")
+    background_btn = gr.Button("Submit Background")
+    mode_out = gr.Textbox(label="Bot Response")
+
+    background_btn.click(save_background, inputs=background, outputs=mode_out)
+
+    gr.Markdown("## Step 3: Choose Mode")
+    btn_text = gr.Button("Text")
+    btn_audio = gr.Button("Audio (WIP)")
+    btn_webcam = gr.Button("Webcam (WIP)")
+    mode_feedback = gr.Textbox(label="Bot Response")
+
+    btn_text.click(set_mode, inputs=[], outputs=mode_feedback, _js="() => 'Text'")
+    btn_audio.click(set_mode, inputs=[], outputs=mode_feedback, _js="() => 'Audio'")
+    btn_webcam.click(set_mode, inputs=[], outputs=mode_feedback, _js="() => 'Webcam'")
+
+    gr.Markdown("## Step 4: Text Interview")
+    user_input = gr.Textbox(label="Your Response")
+    bot_reply = gr.Textbox(label="Bot Question")
+    user_input.submit(start_interview, inputs=user_input, outputs=bot_reply)
 
+demo.launch()