Use google-genai client for Gemini and pin ytmusicapi 1.10

app.py

@@ -11,6 +11,7 @@ from dotenv import load_dotenv
 from fastapi import FastAPI, File, HTTPException, UploadFile
 from fastapi.middleware.cors import CORSMiddleware
 from pydantic import BaseModel
+from google import genai
 
 from ytmusic_client import (
     YouTubeMusicError,
@@ -33,10 +34,12 @@ if GEMINI_API_KEY:
 
 if not GEMINI_API_KEY or GEMINI_API_KEY == "YOUR_API_KEY_HERE":
     print("⚠️ WARNING: GEMINI_API_KEY not found or using placeholder.")
+    GEMINI_CLIENT = None
 else:
     masked_key = f"{GEMINI_API_KEY[:4]}...{GEMINI_API_KEY[-4:]}"
     print(f"✅ API Key detected: {masked_key} (Length: {len(GEMINI_API_KEY)})")
     print(f"✅ Using Gemini model: {GEMINI_MODEL}")
+    GEMINI_CLIENT = genai.Client(api_key=GEMINI_API_KEY)
 
 YTMUSIC_OAUTH_FILE = os.getenv("YTMUSIC_OAUTH_FILE", "oauth.json")
 YTMUSIC_CLIENT_ID = os.getenv("YTMUSIC_CLIENT_ID")
@@ -109,38 +112,43 @@ def _analyze_face_deepface(image_bytes: bytes) -> tuple[str, float]:
 
 
 def _analyze_face_gemini(image_bytes: bytes) -> tuple[str, float]:
-    if not GEMINI_API_KEY or GEMINI_API_KEY == "YOUR_API_KEY_HERE":
+    if GEMINI_CLIENT is None:
         raise ValueError("GEMINI_API_KEY not configured")
-    
-    base64_image = base64.b64encode(image_bytes).decode('utf-8')
-    
+
     prompt = """
     You are an emotion detection AI. Analyze the facial expression in this image.
     DO NOT use 'disgust'.
-    
+
     Return ONLY a valid JSON object with this exact structure:
     {
       "dominant_emotion": "happy|sad|angry|neutral|fear|surprise",
       "confidence": 0.0-1.0
     }
     """
-    
-    url = f"https://generativelanguage.googleapis.com/v1/models/{GEMINI_MODEL}:generateContent?key={GEMINI_API_KEY}"
-    payload = {
-        "contents": [{"parts": [{"text": prompt}, {"inline_data": {"mime_type": "image/jpeg", "data": base64_image}}]}],
-        "generationConfig": {"response_mime_type": "application/json"}
-    }
-    
-    raw_response = requests.post(url, json=payload)
-    response = raw_response.json()
-    
-    if 'error' in response:
-        raise ValueError(f"Gemini API Error: {response['error']['message']}")
-    
-    if 'candidates' not in response:
-        raise ValueError("Image blocked by AI Safety Filters")
-    
-    result = json.loads(response['candidates'][0]['content']['parts'][0]['text'])
+
+    response = GEMINI_CLIENT.models.generate_content(
+        model=GEMINI_MODEL,
+        contents=[
+            {
+                "role": "user",
+                "parts": [
+                    {"text": prompt},
+                    {
+                        "inline_data": {
+                            "mime_type": "image/jpeg",
+                            "data": base64.b64encode(image_bytes).decode("utf-8"),
+                        }
+                    },
+                ],
+            }
+        ],
+    )
+
+    text = response.text or ""
+    try:
+        result = json.loads(text)
+    except Exception:
+        raise ValueError(f"Gemini response not JSON: {text}")
     
     emotion_map = {
         "happy": "joy",
@@ -158,39 +166,33 @@ def _analyze_face_gemini(image_bytes: bytes) -> tuple[str, float]:
 
 
 def _analyze_text_gemini(text: str) -> tuple[str, float]:
-    if not GEMINI_API_KEY or GEMINI_API_KEY == "YOUR_API_KEY_HERE":
+    if GEMINI_CLIENT is None:
         raise ValueError("GEMINI_API_KEY not configured")
-    
+
     prompt = f"""
-    Analyze the emotional tone of this text: "{text}"
-    
+    Analyze the emotional tone of this text: \"{text}\"
+
     Return ONLY a valid JSON object with this exact structure:
     {{
       "dominant_emotion": "joy|sadness|anger|neutral|fear|surprise",
       "confidence": 0.0-1.0
     }}
     """
-    
-    url = f"https://generativelanguage.googleapis.com/v1/models/{GEMINI_MODEL}:generateContent?key={GEMINI_API_KEY}"
-    payload = {
-        "contents": [{"parts": [{"text": prompt}]}],
-        "generationConfig": {"response_mime_type": "application/json"}
-    }
-    
-    raw_response = requests.post(url, json=payload)
-    response = raw_response.json()
-    
-    if 'error' in response:
-        raise ValueError(f"Gemini API Error: {response['error']['message']}")
-    
-    if 'candidates' not in response:
-        raise ValueError("Text analysis blocked")
-    
-    result = json.loads(response['candidates'][0]['content']['parts'][0]['text'])
-    
+
+    response = GEMINI_CLIENT.models.generate_content(
+        model=GEMINI_MODEL,
+        contents=prompt,
+    )
+
+    raw_text = response.text or ""
+    try:
+        result = json.loads(raw_text)
+    except Exception:
+        raise ValueError(f"Gemini response not JSON: {raw_text}")
+
     dominant = result.get("dominant_emotion", "neutral").lower()
     confidence = float(result.get("confidence", 0.5))
-    
+
     return dominant, confidence
 
 

requirements.txt

@@ -9,3 +9,4 @@ python-dotenv
 tf-keras
 tensorflow
 python-multipart
+google-genai