Update app.py

app.py

@@ -2,8 +2,6 @@ from flask import Flask, request, jsonify
 from flask_cors import CORS
 import logging
 import os
-from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
-import torch
 
 app = Flask(__name__)
 CORS(app)
@@ -12,124 +10,67 @@ CORS(app)
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
-# Initialize model variables
-model_name = "google/gemma-1b-it"
-tokenizer = None
-model = None
-generator = None
-
-def initialize_model():
-    global tokenizer, model, generator
+@app.route('/debug', methods=['GET'])
+def debug():
+    # Check if HF_TOKEN is accessible
+    hf_token = os.environ.get('HF_TOKEN')
+    all_env_vars = dict(os.environ)
     
-    if tokenizer is None:
-        try:
-            # Get Hugging Face token from environment
-            hf_token = os.environ.get('HF_TOKEN')
-            
-            if not hf_token:
-                logger.error("HF_TOKEN environment variable not set!")
-                raise ValueError("Hugging Face token not configured")
-            
-            logger.info("Loading tokenizer...")
-            tokenizer = AutoTokenizer.from_pretrained(
-                model_name,
-                token=hf_token
-            )
-            
-            logger.info("Loading model...")
-            model = AutoModelForCausalLM.from_pretrained(
-                model_name,
-                torch_dtype=torch.float16,
-                device_map="auto",
-                token=hf_token
-            )
-            
-            logger.info("Creating pipeline...")
-            generator = pipeline(
-                "text-generation",
-                model=model,
-                tokenizer=tokenizer,
-                device=0 if torch.cuda.is_available() else -1
-            )
-            
-            logger.info("Model loaded successfully!")
-            
-        except Exception as e:
-            logger.error(f"Error loading model: {str(e)}")
-            raise
+    return jsonify({
+        "HF_TOKEN_exists": hf_token is not None,
+        "HF_TOKEN_length": len(hf_token) if hf_token else 0,
+        "HF_TOKEN_start": hf_token[:10] + "..." if hf_token and len(hf_token) > 10 else hf_token,
+        "all_env_vars": {k: v for k, v in all_env_vars.items() if 'TOKEN' in k or 'KEY' in k or 'SECRET' in k}
+    })
 
-@app.route('/generate_script', methods=['POST', 'GET'])
+@app.route('/generate_script', methods=['POST'])
 def generate_script():
     try:
-        logger.info("Received request to /generate_script")
+        # First, let's just check the token and return a mock response
+        hf_token = os.environ.get('HF_TOKEN')
         
-        if request.method == 'GET':
-            return jsonify({"message": "Use POST method with {'topics': ['topic1', 'topic2']}"})
+        if not hf_token:
+            return jsonify({
+                "error": "HF_TOKEN not found in environment variables",
+                "message": "Please set HF_TOKEN in your Space settings"
+            }), 500
         
         data = request.json
-        logger.info(f"Request data: {data}")
-        
         topics = data.get('topics', [])
         
         if not topics:
             return jsonify({"error": "No topics provided"}), 400
         
-        # Initialize model (will load on first request)
-        initialize_model()
+        # Return mock response for now
+        mock_script = f"""🎯 GOLDEN HOOK: "Discover how {topics[0]} is changing everything!"
         
-        # Create prompt for Gemma
-        prompt = f"""Create a viral 1-minute video script about {topics[0]}.
+📝 VALUABLE CONTENT:
+- Scene 1: Show stunning visuals of {topics[0]} in action
+- Scene 2: Explain 3 key benefits in simple terms
+- Scene 3: Share real-world examples and success stories
 
-REQUIREMENTS:
-- Duration: Exactly 1 minute
-- Include: Golden hook, valuable content, CTA
-- Virtual descriptions for each scene
-- Voiceover instructions for each scene
-- Make it engaging and viral-worthy
+🎬 CTA: "Like and follow for more insights on {topics[0]}!"
+
+⏰ Duration: Exactly 60 seconds
+🎤 Voiceover: Energetic and engaging tone
+📱 Optimized for: TikTok/YouTube Shorts"""
 
-SCRIPT STRUCTURE:
-"""
-        
-        # Generate script
-        logger.info("Generating script...")
-        result = generator(
-            prompt,
-            max_length=1024,
-            temperature=0.8,
-            do_sample=True,
-            top_p=0.9,
-            num_return_sequences=1
-        )
-        
-        generated_text = result[0]['generated_text']
-        
-        # Extract just the script part (remove the prompt)
-        script = generated_text.replace(prompt, "").strip()
-        
-        logger.info("Script generated successfully!")
-        
         return jsonify({
             "topic": topics[0],
-            "script": script,
+            "script": mock_script,
             "full_topics": topics,
-            "status": "success"
+            "status": "success",
+            "token_status": "found",
+            "token_length": len(hf_token)
         })
         
     except Exception as e:
-        logger.error(f"Error generating script: {str(e)}")
+        logger.error(f"Error: {str(e)}")
         return jsonify({"error": str(e)}), 500
 
 @app.route('/health', methods=['GET'])
 def health():
-    return jsonify({
-        "status": "healthy", 
-        "message": "Server is running",
-        "model_loaded": tokenizer is not None
-    })
-
-@app.route('/test', methods=['GET'])
-def test():
-    return jsonify({"message": "Test endpoint working!"})
+    return jsonify({"status": "healthy", "message": "Server is running"})
 
 if __name__ == '__main__':
     port = int(os.environ.get('PORT', 7860))