Create app.py

app.py

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+import gradio as gr
+import os
+import google.generativeai as genai
+# Import other SDKs for Runway, ElevenLabs, Tavily
+# For example: from elevenlabs import generate, play, set_api_key
+import subprocess
+import json
+import time
+
+# --- 1. LOAD API KEYS FROM SECRETS ---
+# This is the secure way to do it in Hugging Face Spaces
+genai.configure(api_key=os.environ.get('GEMINI_API_KEY'))
+# set_api_key(os.environ.get('ELEVENLABS_API_KEY'))
+# ... and so on for other APIs
+
+# --- 2. DEFINE THE CORE VIDEO GENERATION FUNCTION ---
+# This function will take the client's prompt and do all the work.
+def generate_video_from_topic(topic_prompt):
+    print(f"Starting video generation for topic: {topic_prompt}")
+    
+    # Placeholder for the final video path
+    final_video_path = None
+    
+    try:
+        # STEP A: RESEARCH (Tavily) - Optional but recommended
+        # research_results = tavily_client.search(query=f"Key points about {topic_prompt}")
+        # facts = research_results['results']
+        
+        # STEP B: SCRIPT & SCENE PROMPTS (Gemini)
+        # We ask Gemini for a structured JSON output
+        gemini_model = genai.GenerativeModel('gemini-pro')
+        prompt = f"""
+        Create a short video script about '{topic_prompt}'.
+        The video should be about 30 seconds long.
+        Return a JSON object with two keys: 'narration_script' (a string) and 'scene_prompts' (a list of 4 detailed, cinematic visual prompts for an AI video generator).
+        
+        Example:
+        {{
+          "narration_script": "This is the complete narration for the video.",
+          "scene_prompts": ["prompt 1", "prompt 2", "prompt 3", "prompt 4"]
+        }}
+        """
+        response = gemini_model.generate_content(prompt)
+        script_data = json.loads(response.text)
+        
+        narration = script_data['narration_script']
+        scene_prompts = script_data['scene_prompts']
+        print(f"Generated Narration: {narration}")
+        print(f"Generated Scene Prompts: {scene_prompts}")
+
+        # STEP C: VOICE OVER (ElevenLabs)
+        # audio_bytes = generate(text=narration, voice="Adam", model="eleven_multilingual_v2")
+        # with open("audio.mp3", "wb") as f:
+        #     f.write(audio_bytes)
+        # print("Audio file generated.")
+        
+        # --- MOCKUP for now ---
+        # Since API calls cost money, let's use a placeholder for testing
+        print("MOCK: Skipping real API calls for audio. Using a placeholder.")
+        # Create a silent audio file of the right length for testing
+        narration_duration = len(narration.split()) / 2.5 # Estimate duration
+        subprocess.run(['ffmpeg', '-f', 'lavfi', '-i', 'anullsrc=r=44100:cl=mono', '-t', str(narration_duration), '-q:a', '9', '-acodec', 'libmp3lame', 'audio.mp3', '-y'])
+
+
+        # STEP D: VISUALS (Runway/Hailuo)
+        video_clips = []
+        for i, scene_prompt in enumerate(scene_prompts):
+            print(f"Generating video for scene {i+1}: {scene_prompt}")
+            # MOCKUP: Create a simple placeholder video clip
+            clip_path = f"scene_{i+1}.mp4"
+            subprocess.run([
+                'ffmpeg', '-f', 'lavfi', '-i', f'smptebars=size=1920x1080:rate=30',
+                '-t', '4', '-pix_fmt', 'yuv420p', clip_path, '-y'
+            ])
+            video_clips.append(clip_path)
+
+        # STEP E: STITCHING (FFmpeg)
+        # Create a file list for ffmpeg
+        with open("file_list.txt", "w") as f:
+            for clip in video_clips:
+                f.write(f"file '{clip}'\n")
+
+        # Concatenate video clips
+        subprocess.run(['ffmpeg', '-f', 'concat', '-safe', '0', '-i', 'file_list.txt', '-c', 'copy', 'combined_video.mp4', '-y'])
+        
+        # Add audio to the combined video
+        final_video_path = f"final_video_{int(time.time())}.mp4"
+        subprocess.run([
+            'ffmpeg', '-i', 'combined_video.mp4', '-i', 'audio.mp3', '-c:v', 'copy',
+            '-c:a', 'aac', '-shortest', final_video_path, '-y'
+        ])
+        print(f"Final video created at: {final_video_path}")
+
+    except Exception as e:
+        print(f"An error occurred: {e}")
+        # You can return an error message to the Gradio interface
+        raise gr.Error(f"Failed to generate video. Error: {e}")
+
+    # Return the path to the final video so Gradio can display it
+    return final_video_path
+
+
+# --- 3. CREATE THE GRADIO INTERFACE ---
+with gr.Blocks() as demo:
+    gr.Markdown("# 🤖 My Personal AI Video Studio")
+    gr.Markdown("Enter a topic to generate a short-form video for social media. Used for fulfilling Fiverr orders.")
+    
+    with gr.Row():
+        topic_input = gr.Textbox(label="Video Topic", placeholder="e.g., 'The benefits of a standing desk'")
+        generate_button = gr.Button("Generate Video", variant="primary")
+        
+    with gr.Row():
+        video_output = gr.Video(label="Generated Video")
+        
+    generate_button.click(
+        fn=generate_video_from_topic,
+        inputs=topic_input,
+        outputs=video_output
+    )
+
+# --- 4. LAUNCH THE APP ---
+demo.launch()