Create app.py

app.py

@@ -0,0 +1,131 @@
+import json
+import os
+import asyncio
+from moviepy.editor import AudioFileClip, concatenate_audioclips
+from huggingface_hub import InferenceClient
+import torch
+import edge_tts
+import tempfile
+import gradio as gr
+
+# Initialize Hugging Face Inference Client
+Client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.3")
+generator = torch.Generator().manual_seed(42)
+
+async def text_to_speech(text, voice, filename):
+    communicate = edge_tts.Communicate(text, voice)
+    await communicate.save(filename)
+
+async def generate_conversation(script):
+    title = script['title']
+    content = script['content']
+    
+    temp_files = []
+
+    tasks = []
+    for key, text in content.items():
+        speaker = key.split('_')[0]  # Extract the speaker name
+        index = key.split('_')[1]    # Extract the dialogue index
+        voice = "en-US-JennyNeural" if speaker == "Alice" else "en-US-GuyNeural"
+        
+        # Create temporary file for each speaker's dialogue
+        temp_file = tempfile.NamedTemporaryFile(suffix='.mp3', delete=False)
+        temp_files.append(temp_file.name)
+        
+        filename = temp_file.name
+        tasks.append(text_to_speech(text, voice, filename))
+        print(f"Generated audio for {speaker}_{index}: {filename}")
+
+    await asyncio.gather(*tasks)
+
+    # Combine the audio files using moviepy
+    audio_clips = [AudioFileClip(temp_file) for temp_file in temp_files]
+    combined = concatenate_audioclips(audio_clips)
+
+    # Create temporary file for the combined output
+    temp_output_file = tempfile.NamedTemporaryFile(suffix='.mp3', delete=False)
+    output_filename = temp_output_file.name
+
+    # Save the combined file
+    combined.write_audiofile(output_filename)
+    print(f"Combined audio saved as: {output_filename}")
+
+    # Clean up temporary files
+    for temp_file in temp_files:
+        os.remove(temp_file)
+        print(f"Deleted temporary file: {temp_file}")
+
+    return output_filename
+
+# Function to generate podcast based on user input
+def generate_podcast(topic, seed):
+    system_instructions = '''[SYSTEM] You are an educational podcast generator. You have to create a podcast between Alice and Bob that gives an overview of the topic given by the user.
+  Please provide the script in the following JSON format:
+    {
+      "title": "[string]",
+      "content": {
+        "Alice_0": "[string]",
+        "BOB_0": "[string]",
+        ...
+      }
+    }
+    Be concise.
+    '''
+
+    text = f" Topic: {topic}"
+    formatted_prompt = system_instructions + text
+    stream = Client.text_generation(formatted_prompt, max_new_tokens=1024, seed=seed, stream=True, details=True, return_full_text=False)
+    
+    generated_script = ""
+    for response in stream:
+        if not response.token.text == "</s>":
+            generated_script += response.token.text
+
+    # Generate the podcast
+    script_json = json.loads(generated_script)  # Use the generated script as input
+    output_filename = asyncio.run(generate_conversation(script_json))
+    print("Output File:"+output_filename)
+
+    # Read the generated audio file
+    with open(output_filename, "rb") as f:
+        audio_bytes = f.read()
+
+    # Clean up the final output temporary file
+    os.remove(output_filename)
+    print(f"Deleted temporary file: {output_filename}")
+
+    return audio_bytes
+
+DESCRIPTION = """ # <center><b>PODGEN 📻</b></center>
+        ### <center>Generate a podcast on any topic</center>
+        ### <center>Use the Power of llms to understand any topic better</center>
+        """
+
+with gr.Blocks(css="style.css") as demo:    
+    gr.Markdown(DESCRIPTION)
+    with gr.Row():
+        
+        seed = gr.Slider(
+        label="Seed",
+        minimum=0,
+        maximum=999999,
+        step=1,
+        value=0,
+        visible=False
+        )
+        input = gr.Textbox(label="Topic", placeholder="Enter a topic")
+        output = gr.Audio(label="Podgen", type="filepath",
+                        interactive=False,
+                        autoplay=True,
+                        elem_classes="audio")
+        gr.Interface(
+            batch=True,
+            max_batch_size=10, 
+            fn=generate_podcast, 
+            inputs=[input, seed],
+            outputs=[output], live=True)  
+        
+    
+
+if __name__ == "__main__":
+    demo.queue(max_size=200).launch()