Update app.py

app.py

@@ -5,20 +5,22 @@ import gradio as gr
 import faiss
 from datasets import load_dataset
 from sentence_transformers import SentenceTransformer
+from gtts import gTTS  # Google Text-to-Speech
+import tempfile
 
-# Optional: Use token if dataset is private
+# Optional: Use token for private datasets
 auth_token = os.environ.get("HF_TOKEN")
 
 # Load dataset
 ds = load_dataset("RomainPct/steve-jobs-question-and-answers", split="train", use_auth_token=auth_token)
 
-# Create DataFrame from correct fields
+# Convert to DataFrame
 data = pd.DataFrame({
     'question': ds['instruction'],
     'answer': ds['output']
 })
 
-# Load SentenceTransformer model
+# Load embedding model
 model = SentenceTransformer("all-MiniLM-L6-v2")
 
 # Encode all questions
@@ -26,23 +28,29 @@ faq_embeddings = model.encode(data['question'].tolist())
 faq_index = faiss.IndexFlatL2(faq_embeddings.shape[1])
 faq_index.add(faq_embeddings)
 
-# Define search + TTS function
+# Answer + Audio generation
 def get_answer_with_audio(query):
     query_embedding = model.encode([query])
     D, I = faq_index.search(np.array(query_embedding), k=1)
     answer = data.iloc[I[0][0]]['answer']
-    return answer, answer  # returns both text and audio
 
-# Gradio UI
+    # Generate speech with gTTS
+    tts = gTTS(text=answer)
+    temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".mp3")
+    tts.save(temp_file.name)
+
+    return answer, temp_file.name
+
+# Gradio interface
 iface = gr.Interface(
     fn=get_answer_with_audio,
     inputs=gr.Textbox(placeholder="Ask a question about Steve Jobs...", label="Your Question"),
     outputs=[
         gr.Text(label="Answer"),
-        gr.Audio(label="Spoken Answer", type="auto")
+        gr.Audio(label="Spoken Answer", type="filepath")
     ],
     title="🧠 Steve Jobs FAQ Chatbot",
-    description="Ask anything about Steve Jobs. This chatbot answers with voice and text based on curated Q&A."
+    description="Ask anything about Steve Jobs. This chatbot answers in text and speech."
 )
 
 iface.launch()