Update app.py

app.py

@@ -1,70 +1,103 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-
-def respond(
-    message,
-    history: list[dict[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-    hf_token: gr.OAuthToken,
-):
-    """
-    For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-    """
-    client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b")
-
-    messages = [{"role": "system", "content": system_message}]
-
-    messages.extend(history)
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        choices = message.choices
-        token = ""
-        if len(choices) and choices[0].delta.content:
-            token = choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-chatbot = gr.ChatInterface(
-    respond,
-    type="messages",
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
+from transformers import pipeline
+import random
+
+# -------------------------------
+# Load Models
+# -------------------------------
+
+# Speech to Text Model (Whisper)
+asr = pipeline(
+    "automatic-speech-recognition",
+    model="openai/whisper-base"
 )
 
+# Text Generation Model (LLM)
+generator = pipeline(
+    "text-generation",
+    model="mistralai/Mistral-7B-Instruct-v0.1",
+    device_map="auto"
+)
+
+# -------------------------------
+# Question Bank
+# -------------------------------
+
+questions = [
+    "Explain overfitting in machine learning.",
+    "What is the difference between supervised and unsupervised learning?",
+    "Explain gradient descent in simple terms.",
+    "What is the role of activation functions in neural networks?",
+    "What is the difference between CNN and RNN?"
+]
+
+# -------------------------------
+# Interview Logic
+# -------------------------------
+
+def start_interview():
+    question = random.choice(questions)
+    return question
+
+def evaluate_answer(audio, question):
+
+    # Convert Speech to Text
+    result = asr(audio)
+    user_answer = result["text"]
+
+    # Create evaluation prompt
+    prompt = f"""
+    You are a technical interviewer.
+
+    Question: {question}
+    Candidate Answer: {user_answer}
+
+    Evaluate the answer and give:
+    1. Technical Accuracy Score (out of 10)
+    2. Clarity Score (out of 10)
+    3. Overall Score (out of 10)
+    4. Improvement Suggestions
+
+    Keep feedback concise.
+    """
+
+    # Generate Evaluation
+    output = generator(
+        prompt,
+        max_new_tokens=300,
+        do_sample=True,
+        temperature=0.7
+    )
+
+    feedback = output[0]["generated_text"]
+
+    return f"📝 Transcribed Answer:\n{user_answer}\n\n📊 Evaluation:\n{feedback}"
+
+
+# -------------------------------
+# Gradio UI
+# -------------------------------
+
 with gr.Blocks() as demo:
-    with gr.Sidebar():
-        gr.LoginButton()
-    chatbot.render()
 
+    gr.Markdown("# 🎤 Smart Interview Simulator (AI Voice Bot)")
+    gr.Markdown("Answer the question using your voice.")
+
+    question_output = gr.Textbox(label="Interview Question")
+
+    start_button = gr.Button("Start Interview")
+    start_button.click(start_interview, outputs=question_output)
+
+    audio_input = gr.Audio(source="microphone", type="filepath")
+
+    submit_button = gr.Button("Submit Answer")
+
+    result_output = gr.Textbox(label="Evaluation Feedback")
+
+    submit_button.click(
+        evaluate_answer,
+        inputs=[audio_input, question_output],
+        outputs=result_output
+    )
 
-if __name__ == "__main__":
-    demo.launch()
+demo.launch()