Update app.py

app.py

@@ -1,43 +1,21 @@
 import gradio as gr
+from huggingface_hub import InferenceClient
 
-# In-memory user profile
+# Initialize LLM client
+client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+
+# User profile state
 user_profile = {
     "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?"
-        ]
-    }
+    "mode": "text"
 }
 
-# Phase 1: Choose interview type
+# Step 1: Set 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?"
+
 def set_type_behavioral():
     return set_type("Behavioral")
 
@@ -47,72 +25,58 @@ def set_type_technical():
 def set_type_college():
     return set_type("College")
 
-# Phase 2: Save background
+# Step 2: Save background info
 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)
+    return "Awesome! Type 'start' below to begin your interview."
 
-    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
+# Step 3: Launch chat interface with interview context
+def chat_bot():
+    def respond(message, history):
+        messages = [
+            {"role": "system", "content": f"You are a professional interviewer for a {user_profile['interview_type']} interview in the {user_profile['field']} field."}
+        ]
+        if history:
+            messages.extend(history)
+        messages.append({"role": "user", "content": message})
+
+        response = ""
+        for msg in client.chat_completion(
+            messages,
+            max_tokens=100,
+            stream=True
+        ):
+            token = msg.choices[0].delta.content
+            response += token
+            yield response
+
+    gr.ChatInterface(respond, title="Intervu - AI Interview Practice").launch()
+
+# Build UI
 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")
-
+    gr.Markdown("### Step 1: Choose Interview Type")
     with gr.Row():
         btn1 = gr.Button("Behavioral")
         btn2 = gr.Button("Technical")
         btn3 = gr.Button("College / Scholarship")
+        type_out = gr.Textbox(label="Bot", interactive=False)
 
     btn1.click(set_type_behavioral, outputs=type_out)
     btn2.click(set_type_technical, outputs=type_out)
     btn3.click(set_type_college, outputs=type_out)
 
+    gr.Markdown("### Step 2: Enter Your Background")
+    background = gr.Textbox(label="Your background and field/goal")
+    background_btn = gr.Button("Submit")
+    background_out = gr.Textbox(label="Bot", interactive=False)
 
-    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")
+    background_btn.click(save_background, inputs=background, outputs=background_out)
 
-    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)
+    gr.Markdown("### Step 3: Start Your Interview")
+    start_btn = gr.Button("Launch Chat Interview")
+    start_btn.click(fn=chat_bot, inputs=[], outputs=[])
 
 demo.launch()
+