Add ReputationRadar community contribution (demo replaced by link)

community-contributions/Reputation_Radar/services/llm.py

@@ -0,0 +1,147 @@
+"""LLM sentiment analysis and summarization utilities."""
+
+from __future__ import annotations
+
+import json
+import logging
+from dataclasses import dataclass
+from typing import Any, Dict, Iterable, List, Optional, Sequence
+
+try:  # pragma: no cover - optional dependency
+    from openai import OpenAI
+except ModuleNotFoundError:  # pragma: no cover
+    OpenAI = None  # type: ignore[assignment]
+
+from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
+
+from .utils import ServiceWarning, chunked
+
+CLASSIFICATION_SYSTEM_PROMPT = "You are a precise brand-sentiment classifier. Output JSON only."
+SUMMARY_SYSTEM_PROMPT = "You analyze brand chatter and produce concise, executive-ready summaries."
+
+
+@dataclass
+class SentimentResult:
+    """Structured sentiment output."""
+
+    label: str
+    confidence: float
+
+
+class LLMService:
+    """Wrapper around OpenAI with VADER fallback."""
+
+    def __init__(self, api_key: Optional[str], model: str = "gpt-4o-mini", batch_size: int = 20):
+        self.batch_size = max(1, batch_size)
+        self.model = model
+        self.logger = logging.getLogger("services.llm")
+        self._client: Optional[Any] = None
+        self._analyzer = SentimentIntensityAnalyzer()
+        if api_key and OpenAI is not None:
+            try:
+                self._client = OpenAI(api_key=api_key)
+            except Exception as exc:  # noqa: BLE001
+                self.logger.warning("Failed to initialize OpenAI client, using VADER fallback: %s", exc)
+                self._client = None
+        elif api_key and OpenAI is None:
+            self.logger.warning("openai package not installed; falling back to VADER despite API key.")
+
+    def available(self) -> bool:
+        """Return whether OpenAI-backed features are available."""
+        return self._client is not None
+
+    def classify_sentiment_batch(self, texts: Sequence[str]) -> List[SentimentResult]:
+        """Classify multiple texts, chunking if necessary."""
+        if not texts:
+            return []
+        if not self.available():
+            return [self._vader_sentiment(text) for text in texts]
+
+        results: List[SentimentResult] = []
+        for chunk in chunked(list(texts), self.batch_size):
+            prompt_lines = ["Classify each item as \"positive\", \"neutral\", or \"negative\".", "Also output a confidence score between 0 and 1.", "Return an array of objects: [{\"label\": \"...\", \"confidence\": 0.0}].", "Items:"]
+            prompt_lines.extend([f"{idx + 1}) {text}" for idx, text in enumerate(chunk)])
+            prompt = "\n".join(prompt_lines)
+            try:
+                response = self._client.responses.create(  # type: ignore[union-attr]
+                    model=self.model,
+                    input=[
+                        {"role": "system", "content": CLASSIFICATION_SYSTEM_PROMPT},
+                        {"role": "user", "content": prompt},
+                    ],
+                    temperature=0,
+                    max_output_tokens=500,
+                )
+                output_text = self._extract_text(response)
+                parsed = json.loads(output_text)
+                for item in parsed:
+                    results.append(
+                        SentimentResult(
+                            label=item.get("label", "neutral"),
+                            confidence=float(item.get("confidence", 0.5)),
+                        )
+                    )
+            except Exception as exc:  # noqa: BLE001
+                self.logger.warning("Classification fallback to VADER due to error: %s", exc)
+                for text in chunk:
+                    results.append(self._vader_sentiment(text))
+        # Ensure the output length matches input
+        if len(results) != len(texts):
+            # align by padding with neutral
+            results.extend([SentimentResult(label="neutral", confidence=0.33)] * (len(texts) - len(results)))
+        return results
+
+    def summarize_overall(self, findings: List[Dict[str, Any]]) -> Dict[str, Any]:
+        """Create an executive summary using OpenAI."""
+        if not self.available():
+            raise ServiceWarning("OpenAI API key missing. Summary unavailable.")
+        prompt_lines = [
+            "Given these labeled items and their short rationales, write:",
+            "- 5 bullet \"Highlights\"",
+            "- 5 bullet \"Risks & Concerns\"",
+            "- One-line \"Overall Tone\" (Positive/Neutral/Negative with brief justification)",
+            "- 3 \"Recommended Actions\"",
+            "Keep it under 180 words total. Be specific but neutral in tone.",
+            "Items:",
+        ]
+        for idx, item in enumerate(findings, start=1):
+            prompt_lines.append(
+                f"{idx}) [{item.get('label','neutral').upper()}] {item.get('text','')}"
+            )
+        prompt = "\n".join(prompt_lines)
+        try:
+            response = self._client.responses.create(  # type: ignore[union-attr]
+                model=self.model,
+                input=[
+                    {"role": "system", "content": SUMMARY_SYSTEM_PROMPT},
+                    {"role": "user", "content": prompt},
+                ],
+                temperature=0.2,
+                max_output_tokens=800,
+            )
+            output_text = self._extract_text(response)
+            return {"raw": output_text}
+        except Exception as exc:  # noqa: BLE001
+            self.logger.error("Failed to generate summary: %s", exc)
+            raise ServiceWarning("Unable to generate executive summary at this time.") from exc
+
+    def _vader_sentiment(self, text: str) -> SentimentResult:
+        scores = self._analyzer.polarity_scores(text)
+        compound = scores["compound"]
+        if compound >= 0.2:
+            label = "positive"
+        elif compound <= -0.2:
+            label = "negative"
+        else:
+            label = "neutral"
+        confidence = min(1.0, max(0.0, abs(compound)))
+        return SentimentResult(label=label, confidence=confidence)
+
+    def _extract_text(self, response: Any) -> str:
+        """Support multiple OpenAI client response shapes."""
+        if hasattr(response, "output") and response.output:
+            content = response.output[0].content[0]
+            return getattr(content, "text", str(content))
+        if hasattr(response, "choices"):
+            return response.choices[0].message.content  # type: ignore[return-value]
+        raise ValueError("Unknown response structure from OpenAI client.")