checkpoint: massive refactor
This commit is contained in:
@@ -6,135 +6,118 @@ import os
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import session_logger # Import session_logger
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class GeminiCliAdapter:
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def __init__(self, binary_path="gemini"):
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self.binary_path = binary_path
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self.last_usage = None
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self.session_id = None
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self.last_latency = 0.0
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def __init__(self, binary_path="gemini"):
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self.binary_path = binary_path
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self.last_usage = None
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self.session_id = None
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self.last_latency = 0.0
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def count_tokens(self, contents: list[str]) -> int:
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"""
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def count_tokens(self, contents: list[str]) -> int:
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"""
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Counts the tokens for a list of string contents using a character-based estimation.
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Approximates tokens by assuming 4 characters per token.
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This replaces the broken 'gemini count' CLI call.
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"""
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input_text = "\n".join(contents)
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total_chars = len(input_text)
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estimated_tokens = total_chars // 4
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return estimated_tokens
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input_text = "\n".join(contents)
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total_chars = len(input_text)
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estimated_tokens = total_chars // 4
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return estimated_tokens
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def send(self, message, safety_settings=None, system_instruction=None, model: str = None):
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"""
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def send(self, message, safety_settings=None, system_instruction=None, model: str = None):
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"""
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Sends a message to the Gemini CLI and processes the streaming JSON output.
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Logs the CLI call details using session_logger.log_cli_call.
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System instruction is prepended to the message.
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Uses --prompt flag with a placeholder and sends the content via stdin.
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"""
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start_time = time.time()
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command_parts = [self.binary_path]
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if model:
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command_parts.extend(['-m', f'"{model}"'])
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# Use an empty string placeholder.
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command_parts.extend(['--prompt', '""'])
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if self.session_id:
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command_parts.extend(['--resume', self.session_id])
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command_parts.extend(['--output-format', 'stream-json'])
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command = " ".join(command_parts)
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# Construct the prompt text by prepending system_instruction if available
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prompt_text = message
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if system_instruction:
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prompt_text = f"{system_instruction}\n\n{message}"
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accumulated_text = ""
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tool_calls = []
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env = os.environ.copy()
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env["GEMINI_CLI_HOOK_CONTEXT"] = "manual_slop"
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process = None
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stdout_content = ""
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stderr_content = ""
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stdin_content = prompt_text
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try:
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process = subprocess.Popen(
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command,
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stdin=subprocess.PIPE,
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stdout=subprocess.PIPE,
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stderr=subprocess.PIPE,
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text=True,
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shell=True,
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env=env
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)
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stdout_output, stderr_output = process.communicate(input=prompt_text)
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stdout_content = stdout_output
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stderr_content = stderr_output
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for line in stdout_content.splitlines():
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line = line.strip()
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if not line:
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continue
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try:
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data = json.loads(line)
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msg_type = data.get("type")
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if msg_type == "init":
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if "session_id" in data:
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self.session_id = data.get("session_id")
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elif msg_type == "message":
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# CRITICAL: Only accumulate content from the assistant/model role.
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# The CLI echoes back the 'user' prompt in the stream, which we must skip.
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role = data.get("role", "")
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if role in ["assistant", "model"]:
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content = data.get("content", data.get("text"))
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if content:
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accumulated_text += content
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elif msg_type == "result":
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self.last_usage = data.get("stats") or data.get("usage")
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if "session_id" in data:
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self.session_id = data.get("session_id")
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elif msg_type == "tool_use":
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# Standardize format for ai_client.py
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# Real CLI might use 'tool_name'/'tool_id'/'parameters'
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# or 'name'/'id'/'args'. We'll map to 'name'/'id'/'args'.
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tc = {
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"name": data.get("tool_name", data.get("name")),
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"args": data.get("parameters", data.get("args", {})),
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"id": data.get("tool_id", data.get("id"))
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}
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if tc["name"]:
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tool_calls.append(tc)
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except json.JSONDecodeError:
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continue
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except Exception as e:
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if process:
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process.kill()
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raise e
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finally:
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current_latency = time.time() - start_time
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if process:
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session_logger.open_session()
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session_logger.log_cli_call(
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command=command,
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stdin_content=stdin_content,
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stdout_content=stdout_content,
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stderr_content=stderr_content,
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latency=current_latency
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)
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self.last_latency = current_latency
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return {
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"text": accumulated_text,
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"tool_calls": tool_calls,
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"stderr": stderr_content
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}
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start_time = time.time()
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command_parts = [self.binary_path]
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if model:
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command_parts.extend(['-m', f'"{model}"'])
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# Use an empty string placeholder.
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command_parts.extend(['--prompt', '""'])
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if self.session_id:
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command_parts.extend(['--resume', self.session_id])
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command_parts.extend(['--output-format', 'stream-json'])
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command = " ".join(command_parts)
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# Construct the prompt text by prepending system_instruction if available
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prompt_text = message
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if system_instruction:
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prompt_text = f"{system_instruction}\n\n{message}"
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accumulated_text = ""
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tool_calls = []
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env = os.environ.copy()
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env["GEMINI_CLI_HOOK_CONTEXT"] = "manual_slop"
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process = None
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stdout_content = ""
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stderr_content = ""
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stdin_content = prompt_text
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try:
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process = subprocess.Popen(
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command,
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stdin=subprocess.PIPE,
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stdout=subprocess.PIPE,
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stderr=subprocess.PIPE,
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text=True,
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shell=True,
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env=env
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)
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stdout_output, stderr_output = process.communicate(input=prompt_text)
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stdout_content = stdout_output
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stderr_content = stderr_output
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for line in stdout_content.splitlines():
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line = line.strip()
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if not line:
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continue
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try:
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data = json.loads(line)
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msg_type = data.get("type")
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if msg_type == "init":
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if "session_id" in data:
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self.session_id = data.get("session_id")
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elif msg_type == "message":
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# CRITICAL: Only accumulate content from the assistant/model role.
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# The CLI echoes back the 'user' prompt in the stream, which we must skip.
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role = data.get("role", "")
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if role in ["assistant", "model"]:
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content = data.get("content", data.get("text"))
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if content:
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accumulated_text += content
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elif msg_type == "result":
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self.last_usage = data.get("stats") or data.get("usage")
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if "session_id" in data:
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self.session_id = data.get("session_id")
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elif msg_type == "tool_use":
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# Standardize format for ai_client.py
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# Real CLI might use 'tool_name'/'tool_id'/'parameters'
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# or 'name'/'id'/'args'. We'll map to 'name'/'id'/'args'.
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tc = {
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"name": data.get("tool_name", data.get("name")),
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"args": data.get("parameters", data.get("args", {})),
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"id": data.get("tool_id", data.get("id"))
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}
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if tc["name"]:
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tool_calls.append(tc)
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except json.JSONDecodeError:
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continue
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except Exception as e:
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if process:
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process.kill()
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raise e
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finally:
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current_latency = time.time() - start_time
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if process:
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session_logger.open_session()
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session_logger.log_cli_call(
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command=command,
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stdin_content=stdin_content,
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stdout_content=stdout_content,
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stderr_content=stderr_content,
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latency=current_latency
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)
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self.last_latency = current_latency
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return {
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"text": accumulated_text,
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"tool_calls": tool_calls,
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"stderr": stderr_content
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}
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