Update app.py

app.py

@@ -38,7 +38,6 @@ def generate_video_from_topic(topic_prompt, progress=gr.Progress(track_tqdm=True
         try:
             research_results = tavily_client.search(query=f"Key facts and interesting points about {topic_prompt}", search_depth="basic")
             facts = "\n".join([res['content'] for res in research_results['results']])
-            print(f"Tavily Research Results:\n{facts}")
         except Exception as e:
             print(f"Tavily API failed: {e}. Proceeding without research.")
             facts = "No research data available."
@@ -47,15 +46,8 @@ def generate_video_from_topic(topic_prompt, progress=gr.Progress(track_tqdm=True
         progress(0.2, desc="✍️ Writing script with Gemini...")
         gemini_model = genai.GenerativeModel('gemini-1.5-flash')
         prompt = f"""
-        You are a creative director for viral short-form videos.
-        Based on the topic '{topic_prompt}' and the following research:
-        ---
-        {facts}
-        ---
-        Create a script for a 30-45 second video.
-        Your output MUST be a valid JSON object with two keys:
-        1. "narration_script": A string containing the full voiceover narration. Make it engaging and concise.
-        2. "scene_prompts": A list of exactly 4 strings. Each string must be a highly detailed, visually rich, and cinematic prompt for a text-to-video AI like Runway Gen-2. Describe camera angles, lighting, and mood.
+        You are a creative director for viral short-form videos. Based on the topic '{topic_prompt}' and research, create a script.
+        Your output MUST be a valid JSON object with "narration_script" (string) and "scene_prompts" (a list of 4 detailed, cinematic prompts).
         """
         response = gemini_model.generate_content(prompt)
         
@@ -64,7 +56,6 @@ def generate_video_from_topic(topic_prompt, progress=gr.Progress(track_tqdm=True
             script_data = json.loads(cleaned_text)
             narration = script_data['narration_script']
             scene_prompts = script_data['scene_prompts']
-            print(f"Gemini Script:\n{narration}\nPrompts:\n{scene_prompts}")
         except (json.JSONDecodeError, KeyError) as e:
             raise gr.Error(f"Gemini did not return valid JSON. Error: {e}. Response was: {response.text}")
 
@@ -73,17 +64,20 @@ def generate_video_from_topic(topic_prompt, progress=gr.Progress(track_tqdm=True
         audio_path = f"audio_{job_id}.mp3"
         intermediate_files.append(audio_path)
         
-        # !!!!!!!!!!! THIS IS THE CORRECTED LINE !!!!!!!!!!!
-        audio_bytes = elevenlabs_client.generate.from_text(
+        # !!!!!!!!!!! THIS IS THE FINAL, CORRECTED SECTION !!!!!!!!!!!
+        # The API returns a stream (generator), so we must iterate over it.
+        audio_stream = elevenlabs_client.generate.from_text(
             text=narration,
-            voice="Adam", # You can choose any available voice
+            voice="Adam",
             model="eleven_multilingual_v2"
         )
-        # Save the audio file
+        
+        # Write the streamed audio chunks to a file.
         with open(audio_path, "wb") as f:
-            for chunk in audio_bytes:
+            for chunk in audio_stream:
                 f.write(chunk)
         print(f"Audio file saved: {audio_path}")
+        # !!!!!!!!!!! END OF CORRECTED SECTION !!!!!!!!!!!
 
         # STEP 4: VISUALS (Runway)
         video_clip_paths = []
@@ -123,8 +117,7 @@ def generate_video_from_topic(topic_prompt, progress=gr.Progress(track_tqdm=True
             video_response = requests.get(video_url, stream=True)
             with open(clip_path, "wb") as f:
                 for chunk in video_response.iter_content(chunk_size=1024):
-                    if chunk:
-                        f.write(chunk)
+                    if chunk: f.write(chunk)
             print(f"Video clip saved: {clip_path}")
 
         # STEP 5: STITCHING (FFmpeg)
@@ -140,10 +133,7 @@ def generate_video_from_topic(topic_prompt, progress=gr.Progress(track_tqdm=True
         subprocess.run(['ffmpeg', '-f', 'concat', '-safe', '0', '-i', file_list_path, '-c', 'copy', combined_video_path, '-y'], check=True)
         
         final_video_path = f"final_video_{job_id}.mp4"
-        subprocess.run([
-            'ffmpeg', '-i', combined_video_path, '-i', audio_path, '-c:v', 'copy',
-            '-c:a', 'aac', '-shortest', final_video_path, '-y'
-        ], check=True)
+        subprocess.run(['ffmpeg', '-i', combined_video_path, '-i', audio_path, '-c:v', 'copy', '-c:a', 'aac', '-shortest', final_video_path, '-y'], check=True)
         print(f"Final video created at: {final_video_path}")
         
         progress(1.0, desc="✅ Done!")
@@ -163,31 +153,19 @@ def generate_video_from_topic(topic_prompt, progress=gr.Progress(track_tqdm=True
 
 # --- 4. CREATE AND LAUNCH THE GRADIO INTERFACE ---
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown(
-        """
-        # 🤖 My Personal AI Video Studio
-        Enter a topic to generate a short-form video. This private tool is used for fulfilling freelance orders.
-        """
-    )
+    gr.Markdown("# 🤖 My Personal AI Video Studio")
+    gr.Markdown("Enter a topic to generate a short-form video. This private tool is used for fulfilling freelance orders.")
     
     with gr.Row():
-        topic_input = gr.Textbox(
-            label="Video Topic", 
-            placeholder="e.g., 'The history of coffee' or 'Benefits of our new CRM software'",
-            scale=3
-        )
+        topic_input = gr.Textbox(label="Video Topic", placeholder="e.g., 'The history of coffee'", scale=3)
         generate_button = gr.Button("Generate Video", variant="primary", scale=1)
         
     with gr.Row():
         video_output = gr.Video(label="Generated Video")
         
-    generate_button.click(
-        fn=generate_video_from_topic,
-        inputs=topic_input,
-        outputs=video_output
-    )
+    generate_button.click(fn=generate_video_from_topic, inputs=topic_input, outputs=video_output)
     
-    gr.Markdown("--- \n ### Examples of Good Topics:\n - A product: 'The new waterproof Chrono-Watch X1'\n - A concept: 'The science of sleep'\n - A story: 'The legend of El Dorado'")
+    gr.Markdown("--- \n ### Examples of Good Topics:\n - A product: 'The new waterproof Chrono-Watch X1'\n - A concept: 'The science of sleep'")
 
 
 if __name__ == "__main__":