Update app.py

app.py

@@ -1,79 +1,62 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
 
-# Initialize LLM client
-client = InferenceClient("")
+client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")  # Use actual model
 
-# User profile state
-user_profile = {
-    "interview_type": "",
-    "field": "",
-    "mode": "text"
-}
-
-# Step 1: Set interview type
-def set_type(choice):
+def set_type(choice, user_profile):
     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")
-
-def set_type_technical():
-    return set_type("Technical")
-
-def set_type_college():
-    return set_type("College")
+    return "Great! What’s your background and what field/role are you aiming for?", user_profile
 
-# Step 2: Save background info
-def save_background(info):
+def save_background(info, user_profile):
     user_profile["field"] = info
-    return "Awesome! Type 'start' below to begin your interview."
+    return "Awesome! Type 'start' below to begin your interview.", user_profile
+
+def respond(message, history, user_profile):
+    if not user_profile["interview_type"] or not user_profile["field"]:
+        yield "Please finish steps 1 and 2 before starting the interview."
+        return
 
-# Step 3: Response logic for embedded Chatbot
-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."}
+        {"role": "system", "content": f"You are a professional interviewer conducting a {user_profile['interview_type']} interview for a candidate 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
+    for msg in client.chat_completion(messages, max_tokens=100, stream=True):
+        token = msg.choices[0].delta.content or ""
         response += token
         yield response
 
-# Build UI
 with gr.Blocks() as demo:
-    gr.Markdown("# 🎤 Welcome to Intervu")
+    user_profile = gr.State({"interview_type": "", "field": ""})
 
+    gr.Markdown("# 🎤 Welcome to Intervu")
+    
+    # Step 1
     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)
+    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)
+    btn1.click(set_type, inputs=[gr.Textbox(value="Behavioral", visible=False), user_profile], outputs=[type_out, user_profile])
+    btn2.click(set_type, inputs=[gr.Textbox(value="Technical", visible=False), user_profile], outputs=[type_out, user_profile])
+    btn3.click(set_type, inputs=[gr.Textbox(value="College", visible=False), user_profile], outputs=[type_out, user_profile])
 
+    # Step 2
     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)
 
-    background_btn.click(save_background, inputs=background, outputs=background_out)
+    background_btn.click(save_background, inputs=[background, user_profile], outputs=[background_out, user_profile])
 
-    gr.Markdown("### Step 3: Start Your Interview")
-    chatbot = gr.ChatInterface(respond, title="Intervu - AI Interview Practice")
+    # Step 3: Interview
+    gr.Markdown("### Step 3: Start Interview")
+    chatbot = gr.ChatInterface(lambda msg, hist: respond(msg, hist, user_profile.value), title="Intervu - AI Interview Practice")
 
 demo.launch()
 
-