Create mental_health_model.py

mental_health_model.py

@@ -0,0 +1,58 @@
+import openai
+from rank_bm25 import BM25Okapi
+
+class MentalHealthClassifier:
+    def __init__(self, train_data):
+        # Tokenize the training data for BM25
+        self.tokenized_train = [doc.split() for doc in train_data["text"]]
+        self.bm25 = BM25Okapi(self.tokenized_train)
+        self.train_data = train_data
+
+    def classify_text(self, api_key, input_text, k=20):
+        # Set the OpenAI API key
+        openai.api_key = api_key
+        if not openai.api_key:
+            return "Error: OpenAI API key is not set."
+
+        # Tokenize input text
+        tokenized_text = input_text.split()
+        # Get top-k similar examples using BM25
+        scores = self.bm25.get_scores(tokenized_text)
+        top_k_indices = sorted(range(len(scores)), key=lambda i: scores[i], reverse=True)[:k]
+
+        # Build examples for the prompt
+        examples = "\n".join(
+            f"Example {i+1}:\nText: {self.train_data.iloc[idx]['text']}\nClassification: "
+            f"Stress={self.train_data.iloc[idx]['Ground_Truth_Stress']}, "
+            f"Anxiety={self.train_data.iloc[idx]['Ground_Truth_Anxiety']}, "
+            f"Depression={self.train_data.iloc[idx]['Ground_Truth_Depression']}, "
+            f"Other={self.train_data.iloc[idx]['Ground_Truth_Other_binary']}\n"
+            for i, idx in enumerate(top_k_indices)
+        )
+
+        # Construct OpenAI prompt
+        prompt = f"""
+        You are a mental health specialist. Analyze the provided text and classify it into one or more of the following categories: Stress, Anxiety, Depression, or Other.
+
+        Respond with a single category that best matches the content: Stress, Anxiety, Depression, or Other.
+
+        Here is the text to classify:
+        "{input_text}"
+
+        ### Examples:
+        {examples}
+        """
+
+        try:
+            response = openai.ChatCompletion.create(
+                messages=[
+                    {"role": "system", "content": "You are a mental health specialist."},
+                    {"role": "user", "content": prompt},
+                ],
+                model="gpt-4",
+                temperature=0,
+            )
+            content = response.choices[0].message.content.strip()
+            return content  # Return the label directly
+        except Exception as e:
+            return f"Error: {e}"