Update app.py

app.py

@@ -0,0 +1,68 @@
+import gradio as gr
+import openai
+from io import BytesIO
+from pydub import AudioSegment
+import requests
+
+# Set up OpenAI API
+openai.api_key = "YOUR_OPENAI_API_KEY"
+
+def generate_presentation(topic):
+    prompt = f"Please explain {topic} in the most easy and attractive way possible."
+
+    # Set up OpenAI API parameters
+    model_engine = "text-davinci-002"
+    max_tokens = 1048
+    temperature = 0.7
+
+    # Generate the presentation content using OpenAI's GPT-3 API
+    response = openai.Completion.create(
+        engine=model_engine,
+        prompt=prompt,
+        max_tokens=max_tokens,
+        temperature=temperature
+    )
+
+    return response.choices[0].text
+
+
+def generate_audio(text):
+    # Set up text-to-speech API parameters
+    api_key = "YOUR_TTS_API_KEY"
+    api_url = "https://api.fpt.ai/hmi/tts/v5"
+    voice = "banmai"
+    speed = "0"
+
+    # Send a request to the text-to-speech API
+    headers = {
+        "api-key": api_key,
+        "voice": voice,
+        "speed": speed
+    }
+    data = {"text": text}
+    response = requests.post(api_url, headers=headers, json=data)
+
+    # Convert the response audio to a playable format
+    audio_bytes = BytesIO(response.content)
+    audio_segment = AudioSegment.from_file(audio_bytes.getvalue(), format="mp3")
+    audio_segment.export("presentation_audio.mp3", format="mp3")
+
+    return audio_bytes
+
+
+def ai_presentation(topic):
+    presentation = generate_presentation(topic)
+    audio = generate_audio(presentation)
+
+    # Return the presentation and generated audio
+    return presentation, audio.read()
+
+# Set up Gradio interface
+inputs = gr.inputs.Textbox(label="Enter the topic for your presentation:")
+outputs = [
+    gr.outputs.Textbox(label="Presentation"),
+    gr.outputs.Audio(label="Presentation Audio", type="audio")
+]
+
+gr.Interface(fn=ai_presentation, inputs=inputs, outputs=outputs, title="AICademy", 
+             icon=":books:", server_port=8080).launch()