Update app.py

app.py

@@ -1,35 +1,27 @@
 import gradio as gr
 import os
-import google.generativeai as genai
-# from elevenlabs.client import ElevenLabs # Temporarily disabled
-from tavily import TavilyClient
-import requests
-import subprocess
 import json
 import time
 import random
+import subprocess
+import google.generativeai as genai
+from tavily import TavilyClient
+from runwayml import RunwayML, TaskFailedError
 
-# --- 1. CONFIGURE API KEYS FROM HUGGING FACE SECRETS ---
+# --- 1. CONFIGURE API KEYS ---
 try:
     genai.configure(api_key=os.environ["GEMINI_API_KEY"])
     tavily_client = TavilyClient(api_key=os.environ["TAVILY_API_KEY"])
     RUNWAY_API_KEY = os.environ["RUNWAY_API_KEY"]
+    runway_client = RunwayML(api_key=RUNWAY_API_KEY)
 except KeyError as e:
-    if 'ELEVENLABS_API_KEY' not in str(e):
-        raise ValueError(f"API Key Error: Please set the {e} secret in your Hugging Face Space settings.")
+    raise ValueError(f"API Key Error: Please set the {e} secret in your environment.")
 
-# --- 2. DEFINE API ENDPOINTS AND HEADERS ---
-RUNWAY_API_URL = "https://api.runwayml.com/v2" 
-RUNWAY_HEADERS = {
-    "Authorization": f"Bearer {RUNWAY_API_KEY}",
-    "Content-Type": "application/json"
-}
-
-# --- 3. THE CORE VIDEO GENERATION FUNCTION ---
+# --- 2. CORE VIDEO GENERATION FUNCTION ---
 def generate_video_from_topic(topic_prompt, progress=gr.Progress(track_tqdm=True)):
     job_id = f"{int(time.time())}_{random.randint(1000, 9999)}"
     print(f"--- Starting New Job: {job_id} for topic: '{topic_prompt}' ---")
-    
+
     intermediate_files = []
 
     try:
@@ -37,7 +29,10 @@ def generate_video_from_topic(topic_prompt, progress=gr.Progress(track_tqdm=True
         progress(0.1, desc="🔍 Researching topic with Tavily...")
         facts = "No research data available."
         try:
-            research_results = tavily_client.search(query=f"Key facts and interesting points about {topic_prompt}", search_depth="basic")
+            research_results = tavily_client.search(
+                query=f"Key facts and interesting points about {topic_prompt}",
+                search_depth="basic"
+            )
             if research_results and 'results' in research_results:
                 facts = "\n".join([res['content'] for res in research_results['results']])
         except Exception as e:
@@ -51,14 +46,21 @@ def generate_video_from_topic(topic_prompt, progress=gr.Progress(track_tqdm=True
         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)
-        
+
         try:
-            cleaned_text = response.text.strip().replace("```json", "").replace("```", "")
+            cleaned_text = (
+                response.text
+                .strip()
+                .replace("```json", "")
+                .replace("```", "")
+            )
             script_data = json.loads(cleaned_text)
             narration = script_data['narration_script']
             scene_prompts = script_data['scene_prompts']
         except (json.JSONDecodeError, KeyError) as e:
-            raise gr.Error(f"Gemini did not return valid JSON. Error: {e}. Response was: {response.text}")
+            raise gr.Error(
+                f"Gemini did not return valid JSON. Error: {e}. Response was: {response.text}"
+            )
 
         # STEP 3: MOCK VOICE OVER
         progress(0.3, desc="🎙️ MOCKING voiceover to save credits...")
@@ -66,62 +68,39 @@ def generate_video_from_topic(topic_prompt, progress=gr.Progress(track_tqdm=True
         intermediate_files.append(audio_path)
         narration_duration = len(narration.split()) / 2.5
         subprocess.run([
-            'ffmpeg', '-f', 'lavfi', '-i', f'anullsrc=r=44100:cl=mono', 
-            '-t', str(narration_duration), '-q:a', '9', '-acodec', 'libmp3lame', 
+            'ffmpeg', '-f', 'lavfi', '-i', 'anullsrc=r=44100:cl=mono',
+            '-t', str(narration_duration), '-q:a', '9', '-acodec', 'libmp3lame',
             audio_path, '-y'
         ], check=True)
-        print(f"MOCK audio file saved (no credits used): {audio_path}")
+        print(f"MOCK audio file saved: {audio_path}")
 
-        # STEP 4: VISUALS (Runway)
+        # STEP 4: GENERATE VIDEO SCENES (Runway SDK)
         video_clip_paths = []
-        for i, scene_prompt in enumerate(scene_prompts):
-            progress(0.4 + (i * 0.12), desc=f"🎬 Generating video scene {i+1}/{len(scene_prompts)}...")
-            
-            # !!!!!!!!!!! THE FINAL, FINAL, VERIFIED RUNWAY FIX !!!!!!!!!!!
-            # 1. The payload structure is now correct for V2
-            runway_payload = {
-                "task_type": "gen2",
-                "options": {
-                    "text_prompt": scene_prompt
-                }
-            }
-            # 2. The POST request goes to the base /tasks endpoint
-            post_response = requests.post(f"{RUNWAY_API_URL}/tasks", headers=RUNWAY_HEADERS, json=runway_payload)
-            # !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
-
-            if post_response.status_code != 200:
-                raise gr.Error(f"Runway API Error (start job): {post_response.status_code} - {post_response.text}")
-            
-            task_id = post_response.json().get("uuid")
-            if not task_id:
-                raise gr.Error(f"Runway API did not return a task UUID. Response: {post_response.json()}")
-
-            video_url = None
-            for _ in range(60):
-                get_response = requests.get(f"{RUNWAY_API_URL}/tasks/{task_id}", headers=RUNWAY_HEADERS)
-                status_details = get_response.json()
-                status = status_details.get("status")
-                
-                if status == "succeeded":
-                    video_url = status_details.get("outputs", {}).get("video")
-                    break
-                elif status == "failed":
-                    raise gr.Error(f"Runway job failed. Details: {status_details.get('error_message')}")
-                
-                print(f"Scene {i+1} status: {status}. Waiting 10 seconds...")
-                time.sleep(10)
-            
-            if not video_url:
-                raise gr.Error(f"Runway job timed out for scene {i+1}.")
-
-            clip_path = f"scene_{i+1}_{job_id}.mp4"
+        for i, scene_prompt in enumerate(scene_prompts, start=1):
+            progress(0.4 + (i * 0.12), desc=f"🎬 Generating scene {i}/{len(scene_prompts)}...")
+            try:
+                task = (
+                    runway_client.image_to_video.create(
+                        model="gen4_turbo",
+                        prompt_text=scene_prompt,
+                        duration=5,
+                        ratio="1280:720"
+                    )
+                    .wait_for_task_output()
+                )
+                video_url = task.output[0]
+            except TaskFailedError as e:
+                raise gr.Error(f"Runway job failed: {e.task_details}")
+
+            clip_path = f"scene_{i}_{job_id}.mp4"
             intermediate_files.append(clip_path)
             video_clip_paths.append(clip_path)
-            
-            video_response = requests.get(video_url, stream=True)
+
+            # Download the scene clip
             with open(clip_path, "wb") as f:
-                for chunk in video_response.iter_content(chunk_size=1024):
-                    if chunk: f.write(chunk)
+                for chunk in runway_client._session.get(video_url, stream=True).iter_content(chunk_size=1024):
+                    if chunk:
+                        f.write(chunk)
             print(f"Video clip saved: {clip_path}")
 
         # STEP 5: STITCHING (FFmpeg)
@@ -134,43 +113,56 @@ def generate_video_from_topic(topic_prompt, progress=gr.Progress(track_tqdm=True
 
         combined_video_path = f"combined_video_{job_id}.mp4"
         intermediate_files.append(combined_video_path)
-        subprocess.run(['ffmpeg', '-f', 'concat', '-safe', '0', '-i', file_list_path, '-c', 'copy', combined_video_path, '-y'], check=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!")
         return final_video_path
 
     except Exception as e:
-        print(f"--- JOB {job_id} FAILED --- \nError: {e}")
+        print(f"--- JOB {job_id} FAILED ---\nError: {e}")
         raise gr.Error(f"An error occurred: {e}")
-    
+
     finally:
-        # STEP 6: CLEANUP
         print("Cleaning up intermediate files...")
         for file_path in intermediate_files:
             if os.path.exists(file_path):
                 os.remove(file_path)
                 print(f"Removed: {file_path}")
 
-# --- 4. CREATE AND LAUNCH THE GRADIO INTERFACE ---
+# --- 3. LAUNCH GRADIO APP ---
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
     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'", 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)
-    
-    gr.Markdown("--- \n ### Examples of Good Topics:\n - A product: 'The new waterproof Chrono-Watch X1'\n - A concept: 'The science of sleep'")
 
+    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'")
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()