Update app.py

app.py

@@ -1,4 +1,7 @@
 import gradio as gr
+import google.generativeai as genai
+import numpy as np
+import re
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
 from huggingface_hub import snapshot_download, login
@@ -7,7 +10,9 @@ import os
 import spaces
 import warnings
 from snac import SNAC
-import numpy as np
+from dotenv import load_dotenv
+
+load_dotenv()
 
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
 logger = logging.getLogger(__name__)
@@ -22,8 +27,6 @@ model = None
 tokenizer = None
 snac_model = None
 
-EMOTIVE_TAGS = ["<laugh>", "<sigh>", "<gasp>", "<cry>", "<yawn>"]
-
 @spaces.GPU()
 def load_model():
     global model, tokenizer, snac_model
@@ -56,6 +59,40 @@ def load_model():
         logger.error(f"Error loading model: {str(e)}")
         raise
 
+@spaces.GPU()
+def generate_podcast_script(api_key, content, uploaded_file, duration, num_hosts):
+    try:
+        genai.configure(api_key=api_key)
+        model = genai.GenerativeModel('gemini-2.5-pro-preview-03-25')
+        
+        combined_content = content or ""
+        if uploaded_file:
+            file_content = uploaded_file.read().decode('utf-8')
+            combined_content += "\n" + file_content if combined_content else file_content
+        
+        prompt = f"""
+        Create a podcast script for {'one person' if num_hosts == 1 else 'two people'} discussing:
+        {combined_content}
+        
+        Duration: {duration}. Include natural speech, humor, and occasional off-topic thoughts.
+        Use speech fillers like um, ah. Vary emotional tone.
+        
+        Format: {'Monologue' if num_hosts == 1 else 'Alternating dialogue'} without speaker labels.
+        Separate {'paragraphs' if num_hosts == 1 else 'lines'} with blank lines.
+        
+        Use emotion tags in angle brackets: <laugh>, <sigh>, <chuckle>, <cough>, <sniffle>, <groan>, <yawn>, <gasp>.
+        
+        Example: "I can't believe I stayed up all night <yawn> only to find out the meeting was canceled <groan>."
+        
+        Ensure content flows naturally and stays on topic. Match the script length to {duration}.
+        """
+        
+        response = model.generate_content(prompt)
+        return re.sub(r'[^a-zA-Z0-9\s.,?!<>]', '', response.text)
+    except Exception as e:
+        logger.error(f"Error generating podcast script: {str(e)}")
+        raise
+
 def process_prompt(prompt, voice, tokenizer, device):
     prompt = f"{voice}: {prompt}"
     input_ids = tokenizer(prompt, return_tensors="pt").input_ids
@@ -146,69 +183,73 @@ def generate_speech(text, voice, temperature, top_p, repetition_penalty, max_new
         progress(0.8, "Converting to audio...")
         audio_samples = redistribute_codes(code_list, snac_model)
         
-        return (24000, audio_samples)
+        return (24000, audio_samples)  # Return sample rate and audio
     except Exception as e:
         print(f"Error generating speech: {e}")
         return None
 
-with gr.Blocks(title="Orpheus Text-to-Speech") as demo:
-    gr.Markdown(f"""
-    # 🎵 [Orpheus Text-to-Speech](https://github.com/canopyai/Orpheus-TTS)
-    Enter your text below and hear it converted to natural-sounding speech with the Orpheus TTS model.
+@spaces.GPU()
+def render_podcast(api_key, script, voice1, voice2, num_hosts):
+    try:
+        lines = [line for line in script.split('\n') if line.strip()]
+        audio_segments = []
+
+        for i, line in enumerate(lines):
+            voice = voice1 if num_hosts == 1 or i % 2 == 0 else voice2
+            try:
+                result = generate_speech(line, voice, temperature=0.6, top_p=0.95, repetition_penalty=1.1, max_new_tokens=1200)
+                if result is not None:
+                    sample_rate, audio = result
+                    audio_segments.append(audio)
+            except Exception as e:
+                logger.error(f"Error processing audio segment: {str(e)}")
+
+        if not audio_segments:
+            logger.warning("No valid audio segments were generated.")
+            return (24000, np.zeros(24000, dtype=np.float32))
+
+        podcast_audio = np.concatenate(audio_segments)
+        podcast_audio = np.clip(podcast_audio, -1, 1)
+        podcast_audio = (podcast_audio * 32767).astype(np.int16)
+        
+        return (24000, podcast_audio)
+    except Exception as e:
+        logger.error(f"Error rendering podcast: {str(e)}")
+        raise
+
+with gr.Blocks() as demo:
+    gr.Markdown("# AI Podcast Generator")
     
-    ## Tips for better prompts:
-    - Add paralinguistic elements like {", ".join(EMOTIVE_TAGS)} or `uhm` for more human-like speech.
-    - Longer text prompts generally work better than very short phrases
-    """)
+    api_key_input = gr.Textbox(label="Enter your Gemini API Key", type="password")
     
     with gr.Row():
-        with gr.Column():
-            text_input = gr.Textbox(
-                label="Text Input",
-                placeholder="Enter the text you want to convert to speech...",
-                lines=8
-            )
-            voice_select = gr.Dropdown(
-                choices=["tara", "leah", "jess", "leo", "dan", "mia", "zac", "zoe"],
-                value="tara",
-                label="Voice"
-            )
-            with gr.Accordion("Advanced Options", open=False):
-                temperature = gr.Slider(
-                    minimum=0.1, maximum=1.0, value=0.6, step=0.1,
-                    label="Temperature",
-                    info="Higher values increase randomness in the output"
-                )
-                top_p = gr.Slider(
-                    minimum=0.1, maximum=1.0, value=0.95, step=0.05,
-                    label="Top-p",
-                    info="Lower values increase determinism in the output"
-                )
-                repetition_penalty = gr.Slider(
-                    minimum=1.0, maximum=2.0, value=1.1, step=0.1,
-                    label="Repetition Penalty",
-                    info="Higher values discourage repetitive patterns"
-                )
-                max_new_tokens = gr.Slider(
-                    minimum=100, maximum=2000, value=1200, step=100,
-                    label="Max Length", 
-                    info="Maximum length of generated audio (in tokens)"
-                )
-            
-            with gr.Row():
-                submit_btn = gr.Button("Generate Speech", variant="primary")
-                clear_btn = gr.Button("Clear")
-        
-        with gr.Column():
-            audio_output = gr.Audio(label="Generated Speech")
+        content_input = gr.Textbox(label="Paste your content (optional)", lines=4)
+        document_upload = gr.File(label="Upload Document (optional)")
+    
+    duration = gr.Radio(["1-5 min", "5-10 min", "10-15 min"], label="Estimated podcast duration", value="1-5 min")
+    num_hosts = gr.Radio([1, 2], label="Number of podcast hosts", value=2)
     
-    submit_btn.click(
-        generate_speech,
-        inputs=[text_input, voice_select, temperature, top_p, repetition_penalty, max_new_tokens],
-        outputs=audio_output
-    )
+    voice_options = ["tara", "leah", "jess", "leo", "dan", "mia", "zac", "zoe"]
+    voice1_select = gr.Dropdown(label="Select Voice 1", choices=voice_options, value="tara")
+    voice2_select = gr.Dropdown(label="Select Voice 2", choices=voice_options, value="leo")
     
-    clear_btn.click(lambda: "", inputs=None, outputs=text_input)
+    generate_btn = gr.Button("Generate Script")
+    script_output = gr.Textbox(label="Generated Script", lines=10)
+    
+    render_btn = gr.Button("Render Podcast")
+    audio_output = gr.Audio(label="Generated Podcast")
+    
+    generate_btn.click(generate_podcast_script, 
+                       inputs=[api_key_input, content_input, document_upload, duration, num_hosts], 
+                       outputs=script_output)
+    
+    render_btn.click(render_podcast, 
+                     inputs=[api_key_input, script_output, voice1_select, voice2_select, num_hosts], 
+                     outputs=audio_output)
+
+    num_hosts.change(lambda x: gr.update(visible=x == 2), 
+                     inputs=[num_hosts], 
+                     outputs=[voice2_select])
 
 if __name__ == "__main__":
     try: