Upload generate_explanation.py

modules/generate_explanation.py

@@ -0,0 +1,58 @@
+import os
+import google.generativeai as genai
+from dotenv import load_dotenv
+
+# Load environment variables
+load_dotenv()
+
+# Configure Gemini API
+genai.configure(api_key=os.getenv("GEMINI_API_KEY"))
+
+# Model configuration
+generation_config = {
+    "temperature": 0.7,
+    "top_p": 0.9,
+    "top_k": 40,
+    "max_output_tokens": 300,
+    "response_mime_type": "text/plain",
+}
+
+# Initialize the Gemini model
+model = genai.GenerativeModel(
+    model_name="gemini-1.5-flash", 
+    generation_config=generation_config,
+)
+
+
+def generate_explanation(news, context, truth_score):
+    """
+    Generate a concise explanation supporting the truth score.
+
+    Arguments:
+    - news (str): The news or claim being analyzed.
+    - context (str): Relevant context from external sources.
+    - truth_score (float): The score indicating the truthfulness of the news.
+
+    Returns:
+    - str: A simple explanation or an empty string if an error occurs.
+    """
+
+    # Define the prompt for explanation
+    prompt = (
+        "Summarize the context below and explain why the given truth score was assigned to the news claim.\n\n"
+        f"News: {news}\n\n"
+        f"Context: {context[:3000]}...\n\n"
+        f"Truth Score: {truth_score:.2f}\n\n"
+        "Keep the explanation short, factual, and easy to understand."
+    )
+
+    # Generate explanation
+    try:
+        chat_session = model.start_chat(history=[])  # Start a new chat session
+        response = chat_session.send_message(prompt)
+        return response.text.strip()  # Return only the explanation text
+
+    except Exception as e:
+        # Print error and return an empty string
+        print(f"Error generating explanation: {str(e)}")
+        return ""