update sentiment_tool.py

tools/sentiment_tool.py

@@ -5,77 +5,136 @@ from openai import OpenAI
 from typing import Type
 from pydantic import BaseModel, Field
 
-SERPER_API_KEY = os.getenv(key="SERPER_API_KEY")
-OPENAI_API_KEY = os.getenv(key = "OPENAI_API_KEY")
+# Environment variables
+SERPER_API_KEY = os.getenv("SERPER_API_KEY")
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
 
 client = OpenAI(api_key=OPENAI_API_KEY)
 
+
+# -----------------------------
+# Pydantic Input Schema
+# -----------------------------
 class SentimentInput(BaseModel):
-    query: str = Field(default="bitcoin", description="Cryptocurrency name to evaluate sentiment for.")
+    query: str = Field(
+        default="bitcoin",
+        description="Cryptocurrency to evaluate sentiment for."
+    )
+
 
+# -----------------------------
+# Sentiment Tool (Serper-only)
+# -----------------------------
 class SentimentTool(BaseTool):
     name: str = "get_crypto_sentiment"
     description: str = (
-        "Fetches recent cryptocurrency news and Reddit discussions,"
-        "then analyses sentiment as bullish, bearish or neutral."
+        "Fetches recent cryptocurrency sentiment using Serper.dev, including "
+        "Google News headlines and Reddit discussions, then classifies overall "
+        "sentiment as bullish, bearish, or neutral."
     )
-    arg_schema: Type[BaseModel] = SentimentInput
+    args_schema: Type[BaseModel] = SentimentInput
+
+    # -----------------------------
+    # Helper: call Serper API safely
+    # -----------------------------
+    def _serper_post(self, endpoint: str, payload: dict):
+        url = f"https://google.serper.dev/{endpoint}"
+        headers = {"X-API-KEY": SERPER_API_KEY, "Content-Type": "application/json"}
+
+        try:
+            response = requests.post(url, headers=headers, json=payload, timeout=10)
+            response.raise_for_status()
+            return response.json()
+        except Exception as e:
+            return {"error": str(e)}
 
-    def _run(self, query: str = "bitcoin") -> str:
+    # -----------------------------
+    # Main execution
+    # -----------------------------
+    def _run(self, query: str = "bitcoin"):
         try:
-            #Google news sentiment
-            headlines = []
-            try: 
-                news_url = "https://google.serper.dev/news"
-                headers = {"X-API-KEY": SERPER_API_KEY, "Content-Type": "application/json"}
-                payload = {"q": f"{query} crypto", "num": 5}
-                news_response = requests.post(news_url, headers=headers, json=payload, timeout=10)
-                news_response.raise_for_status()
-                news = news_response.json().get("news", [])
-                headlines = [n["title"] for n in news[:5]]
-            except Exception:
-                pass # silent fail
-            
-            #Reddit sentiment
+            # -------------------------
+            # 1️⃣ Fetch Google News
+            # -------------------------
+            news_result = self._serper_post(
+                "news",
+                {"q": f"{query} crypto", "num": 5}
+            )
+
+            news_headlines = []
+            news_error = None
+
+            if "error" in news_result:
+                news_error = news_result["error"]
+            else:
+                raw_news = news_result.get("news", [])
+                news_headlines = [item.get("title", "") for item in raw_news[:5]]
+
+            # -------------------------
+            # 2️⃣ Fetch Reddit Discussions
+            # -------------------------
+            reddit_result = self._serper_post(
+                "reddit",
+                {"q": query, "num": 5}
+            )
+
             reddit_titles = []
-            try:
-                reddit_url = f"https://www.reddit.com/search.json?q={query}&sort=new&limit=5"
-                reddit_headers = {"User-Agent": "CryptoAgent/1.0"}
-                reddit_response = requests.get(reddit_url,headers=reddit_headers, timeout=10)
-                reddit_response.raise_for_status()
-                reddit_posts = reddit_response.json().get("data", {}).get("children", [])
-                reddit_titles = [p["data"]["title"] for p in reddit_posts[:5]]
-            except Exception:
-                pass 
-
-            combined_text = "News: " + " | ".join(headlines) + "\nReddit: " + " | ".join(reddit_titles)
-
-            #analyze sentiment
+            reddit_error = None
+
+            if "error" in reddit_result:
+                reddit_error = reddit_result["error"]
+            else:
+                raw_reddit = reddit_result.get("organic", [])
+                reddit_titles = [item.get("title", "") for item in raw_reddit[:5]]
+
+            # -------------------------
+            # 3️⃣ Create LLM prompt
+            # -------------------------
+            combined_text = f"""
+News Headlines:
+{news_headlines}
+
+Reddit Posts:
+{reddit_titles}
+
+Errors:
+news_error={news_error}
+reddit_error={reddit_error}
+"""
+
             sentiment_prompt = f"""
-You are a crypto sentiment analyst. Based on the following headlines and Reddit posts,
-intuitively determine if the overall sentiment towards "{query}" is bullish, bearish, or neutral.
-Respond with a short summary and a one-word sentiment classification.
+You are a cryptocurrency sentiment analyst.
 
- {combined_text}
+Given the following news and Reddit discussions for "{query}", classify overall sentiment 
+as **bullish**, **bearish**, or **neutral**.
+
+Return ONLY valid JSON in the exact structure below:
 
-Return ONLY valid JSON in the format:
 {{
   "sentiment": "bullish/bearish/neutral",
-  "reasoning": "short explanation",
+  "reasoning": "Short explanation",
   "news_headlines": [...],
-  "reddit_titles": [...]
+  "reddit_titles": [...],
+  "news_error": "... or null",
+  "reddit_error": "... or null"
 }}
- """
+
+Here is the data to analyze:
+{combined_text}
+"""
+
+            # -------------------------
+            # 4️⃣ Call OpenAI GPT-4.1
+            # -------------------------
             completion = client.chat.completions.create(
                 model="gpt-4.1",
                 messages=[
                     {"role": "system", "content": "You are a precise sentiment classifier."},
                     {"role": "user", "content": sentiment_prompt}
-                ],
-                temperature=0.2
+                ]
             )
-            sentiment_result = completion.choices[0].message.content
-            return sentiment_result
-        
+
+            return completion.choices[0].message.content
+
         except Exception as e:
-            return f"Error fetching sentiment data: {str(e)}"   
+            return {"error": f"SentimentTool failed: {str(e)}"}