refactor: generator-gemini.py

generator-gemini.py

@@ -8,15 +8,13 @@
 
 from templates import PROMPT_STRATEGY
 
-# Constants
+# TODO: generator-gemini.py to converge with generator.py
 API_KEY = "..."
 MODEL_NAME = "gemini-1.5-pro-001"
 
-# Configure the Gemini API
 genai.configure(api_key=API_KEY)
 model = genai.GenerativeModel(MODEL_NAME)
 
-# Safety settings
 safety_settings = {
     "HARM_CATEGORY_SEXUALLY_EXPLICIT": "BLOCK_NONE",
     "HARM_CATEGORY_HATE_SPEECH": "BLOCK_NONE",
@@ -28,63 +26,57 @@
 parser.add_argument("-o", "--output_dir", help="Directory to save outputs", default="./generated")
 args = parser.parse_args()
 
-print(f"Args - {args}")
-
 df_questions = pd.read_json("questions.jsonl", orient="records", encoding="utf-8-sig", lines=True)
 
 if not os.path.exists(args.output_dir):
     os.makedirs(args.output_dir)
 
 
 @retry(stop=stop_after_attempt(10), wait=wait_fixed(1), retry=retry_if_exception_type(Exception))
-def call_gemini_api(messages):
+def call_gemini_api(input_text):
     """Function to call the Gemini API and return the generated text."""
-    response = model.generate_content(messages, safety_settings=safety_settings)
+    response = model.generate_content([input_text], safety_settings=safety_settings)
 
     if not response.candidates:
         raise ValueError("Invalid operation: No candidates returned in the response.")
 
     candidate = response.candidates[0]
-    if not candidate.messages:
+    if not candidate.content.parts:
         print(candidate)
-        raise ValueError("Invalid operation: No messages found in the candidate.")
+        raise ValueError("Invalid operation: No parts found in the candidate.")
 
-    return candidate.messages[-1].content
+    return candidate.content.parts[0].text
 
 
 for strategy_name, prompts in PROMPT_STRATEGY.items():
 
     def format_single_turn_question(question):
-        # Make a deep copy of the prompts to avoid modifying the original
-        formatted_prompts = [dict(p) for p in prompts]
-        formatted_prompts.append({"role": "user", "content": question[0]})
-        return formatted_prompts
+        messages = prompts + [{"role": "user", "content": question[0]}]
+        formatted_text = "\n".join([f"{message['role']}: {message['content']}" for message in messages])
+        return formatted_text
 
     single_turn_questions = df_questions["questions"].map(format_single_turn_question)
     single_turn_outputs = []
-    for messages in tqdm(single_turn_questions, desc=f"Generating single-turn outputs for {strategy_name}"):
-        generated_text = call_gemini_api(messages)
+    for formatted_text in tqdm(single_turn_questions, desc=f"Generating single-turn outputs for {strategy_name}"):
+        generated_text = call_gemini_api(formatted_text)
         single_turn_outputs.append(generated_text)
 
     def format_double_turn_question(question, single_turn_output):
-        # Make a deep copy of the prompts to avoid modifying the original
-        formatted_prompts = [dict(p) for p in prompts]
-        formatted_prompts.extend(
-            [
-                {"role": "user", "content": question[0]},
-                {"role": "assistant", "content": single_turn_output},
-                {"role": "user", "content": question[1]},
-            ]
-        )
-        return formatted_prompts
+        messages = prompts + [
+            {"role": "user", "content": question[0]},
+            {"role": "assistant", "content": single_turn_output},
+            {"role": "user", "content": question[1]},
+        ]
+        formatted_text = "\n".join([f"{message['role']}: {message['content']}" for message in messages])
+        return formatted_text
 
     multi_turn_questions = df_questions[["questions", "id"]].apply(
         lambda x: format_double_turn_question(x["questions"], single_turn_outputs[x["id"] - 1]),
         axis=1,
     )
     multi_turn_outputs = []
-    for messages in tqdm(multi_turn_questions, desc=f"Generating multi-turn outputs for {strategy_name}"):
-        generated_text = call_gemini_api(messages)
+    for formatted_text in tqdm(multi_turn_questions, desc=f"Generating multi-turn outputs for {strategy_name}"):
+        generated_text = call_gemini_api(formatted_text)
         multi_turn_outputs.append(generated_text)
 
     df_output = pd.DataFrame(