Create app.py

app.py

@@ -1,64 +1,71 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-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("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    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,
-    ):
-        token = message.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
-"""
-demo = gr.ChatInterface(
-    respond,
-    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)",
-        ),
-    ],
-)
+import random, json
+from googletrans import Translator
+from transformers import pipeline
 
+translator = Translator()
+sentiment_model = pipeline("sentiment-analysis", model="nlptown/bert-base-multilingual-uncased-sentiment")
+
+with open("questions.json", "r") as f:
+    questions = json.load(f)
+
+with open("chatbot_lines.json", "r") as f:
+    chatbot_lines = json.load(f)
+
+# Helper functies
+def get_question(mode):
+    return random.choice(questions[mode])
+
+def process_input(audio, lang_from, lang_to):
+    text = "(Gesproken tekst hier invoegen)"
+    translation = translator.translate(text, src=lang_from, dest=lang_to).text
+    sentiment = sentiment_model(text)[0]
+    emotion = f"{sentiment['label']} ({sentiment['score']:.2f})"
+    # Chatbotreactie afhankelijk van type vraag
+    if sentiment['label'].lower() in ["1 star", "2 stars"]:
+        botline = random.choice(chatbot_lines["pittig"])
+    else:
+        botline = random.choice(chatbot_lines["grappig"])
+    return text, translation, emotion, botline
+
+with gr.Blocks() as demo:
+    gr.Markdown("## 🎭 Relatiegame voor 2 spelers op dezelfde computer")
+
+    with gr.Row():
+        speler1_taal = gr.Dropdown(["en", "pl"], label="Speler A spreekt", value="en")
+        speler2_taal = gr.Dropdown(["pl", "en"], label="Speler B spreekt", value="pl")
+
+    mode = gr.Radio(["emotioneel", "speels"], label="Soort vraag")
+    vraagveld = gr.Textbox(label="Vraag", interactive=False)
+    nieuwevraag = gr.Button("Nieuwe vraag")
+
+    with gr.Row():
+        audio1 = gr.Audio(source="microphone", type="filepath", label="🎙️ Speler A opname")
+        audio2 = gr.Audio(source="microphone", type="filepath", label="🎙️ Speler B opname")
+
+    out1 = gr.Textbox(label="Transcriptie Speler A")
+    trans1 = gr.Textbox(label="Vertaling voor Speler B")
+    emo1 = gr.Textbox(label="Emotie Speler A")
+    bot1 = gr.Textbox(label="🤖 Opmerking Chatbot A")
+
+    out2 = gr.Textbox(label="Transcriptie Speler B")
+    trans2 = gr.Textbox(label="Vertaling voor Speler A")
+    emo2 = gr.Textbox(label="Emotie Speler B")
+    bot2 = gr.Textbox(label="🤖 Opmerking Chatbot B")
+
+    nieuwevraag.click(fn=get_question, inputs=mode, outputs=vraagveld)
+
+    def run_a(a, l1, l2): return process_input(a, l1, l2)
+    def run_b(b, l2, l1): return process_input(b, l2, l1)
+
+    knop = gr.Button("🎮 Beiden ingesproken, verwerk antwoord")
+    knop.click(run_a, inputs=[audio1, speler1_taal, speler2_taal], outputs=[out1, trans1, emo1, bot1])
+    knop.click(run_b, inputs=[audio2, speler2_taal, speler1_taal], outputs=[out2, trans2, emo2, bot2])
+
+    gr.Markdown("""
+    🔁 Blijf spelen. Lach samen. Voel samen. Begrijp elkaar beter.
+    🤖 Onze chatbot maakt het luchtig, maar de diepgang zit in jullie antwoorden.
+    """)
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()