update sentiment_tool.py

tools/sentiment_tool.py

@@ -5,136 +5,130 @@ from openai import OpenAI
 from typing import Type
 from pydantic import BaseModel, Field
 
-# 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
-# -----------------------------
+# -------------------------
+# Input schema
+# -------------------------
 class SentimentInput(BaseModel):
-    query: str = Field(
-        default="bitcoin",
-        description="Cryptocurrency to evaluate sentiment for."
-    )
+    query: str = Field(default="bitcoin", description="Cryptocurrency to analyze sentiment for.")
 
-
-# -----------------------------
-# Sentiment Tool (Serper-only)
-# -----------------------------
+# -------------------------
+# Sentiment Tool (Option C)
+# -------------------------
 class SentimentTool(BaseTool):
     name: str = "get_crypto_sentiment"
     description: str = (
-        "Fetches recent cryptocurrency sentiment using Serper.dev, including "
-        "Google News headlines and Reddit discussions, then classifies overall "
-        "sentiment as bullish, bearish, or neutral."
+        "Fetch crypto sentiment using Serper Search only. "
+        "News headlines come from Serper's News API. "
+        "Reddit-like sentiment is extracted from URLs or snippets containing 'reddit.com'. "
+        "Returns structured JSON: sentiment, reasoning, headlines, reddit_titles."
     )
     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"}
-
+    def _run(self, query: str = "bitcoin") -> dict:
         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)}
-
-    # -----------------------------
-    # Main execution
-    # -----------------------------
-    def _run(self, query: str = "bitcoin"):
-        try:
-            # -------------------------
-            # 1️⃣ Fetch Google News
-            # -------------------------
-            news_result = self._serper_post(
-                "news",
-                {"q": f"{query} crypto", "num": 5}
-            )
-
+            # -----------------------------------------
+            # Fetch NEWS via Serper News API
+            # -----------------------------------------
             news_headlines = []
             news_error = None
+            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}
 
-            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}
-            )
+                news_response = requests.post(news_url, headers=headers, json=payload, timeout=10)
+                news_response.raise_for_status()
+
+                news_items = news_response.json().get("news", [])
+                news_headlines = [n.get("title", "") for n in news_items[:5]]
+
+            except Exception as e:
+                news_error = str(e)
 
+            # -----------------------------------------
+            # Extract REDDIT-like content from Serper Search results
+            # -----------------------------------------
             reddit_titles = []
             reddit_error = None
+            try:
+                search_url = "https://google.serper.dev/search"
+                headers = {"X-API-KEY": SERPER_API_KEY, "Content-Type": "application/json"}
+                payload = {"q": f"{query} reddit crypto", "num": 8}
+
+                search_response = requests.post(search_url, headers=headers, json=payload, timeout=10)
+                search_response.raise_for_status()
+                organic = search_response.json().get("organic", [])
+
+                for item in organic:
+                    url = item.get("link", "")
+                    snippet = item.get("snippet", "")
+                    title = item.get("title", "")
+
+                    # Accept if URL or snippet mentions Reddit
+                    if "reddit.com" in url.lower() or "reddit" in snippet.lower():
+                        reddit_titles.append(title)
+
+            except Exception as e:
+                reddit_error = str(e)
+
+            # -----------------------------------------
+            # Build combined text for LLM classification
+            # -----------------------------------------
+            combined_text = (
+                "News: " + " | ".join(news_headlines) +
+                "\nReddit-like: " + " | ".join(reddit_titles)
+            )
 
-            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}
-"""
-
+            # -----------------------------------------
+            # LLM sentiment classification (JSON enforced)
+            # -----------------------------------------
             sentiment_prompt = f"""
-You are a cryptocurrency sentiment analyst.
+You are a crypto sentiment analyst.
 
-Given the following news and Reddit discussions for "{query}", classify overall sentiment 
-as **bullish**, **bearish**, or **neutral**.
+Based on the following news headlines and Reddit-like search results,
+give an *overall sentiment classification* for "{query}" as:
+- bullish
+- bearish
+- neutral
 
-Return ONLY valid JSON in the exact structure below:
+Respond ONLY in **valid JSON** using this schema:
 
 {{
   "sentiment": "bullish/bearish/neutral",
-  "reasoning": "Short explanation",
+  "reasoning": "short explanation summarizing why",
   "news_headlines": [...],
   "reddit_titles": [...],
-  "news_error": "... or null",
-  "reddit_error": "... or null"
+  "news_error": null or error string,
+  "reddit_error": null or error string
 }}
 
-Here is the data to analyze:
+Here is the text:
+
 {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}
-                ]
+                    {"role": "system", "content": "Return ONLY strict JSON. No commentary."},
+                    {"role": "user",  "content": sentiment_prompt}
+                ],
+                temperature = 0.2
             )
 
             return completion.choices[0].message.content
 
         except Exception as e:
-            return {"error": f"SentimentTool failed: {str(e)}"}
+            return {
+                "sentiment": "unknown",
+                "reasoning": "Sentiment tool failed to run.",
+                "news_headlines": [],
+                "reddit_titles": [],
+                "news_error": str(e),
+                "reddit_error": str(e)
+            }