First step in making gpt-engineer learn. Rename main_prompt -> prompt (#381)

* First step in collecting learnings

* Rename prompts

* remove requirements, use pip install -e . instead

* Add requirements

* Fix tests

gpt_engineer/collect.py

@@ -0,0 +1,85 @@
+import hashlib
+import json
+import os
+import random
+import tempfile
+
+from dataclasses import dataclass
+from pathlib import Path
+
+from dataclasses_json import dataclass_json
+
+from gpt_engineer import steps
+from gpt_engineer.db import DBs
+from gpt_engineer.steps import Step
+
+
+@dataclass_json
+@dataclass
+class Learning:
+    model: str
+    temperature: float
+    steps: str
+    steps_file_hash: str
+    prompt: str
+    feedback: str | None
+    session: str
+    version: str = "0.1"
+
+
+def steps_file_hash():
+    with open(steps.__file__, "r") as f:
+        content = f.read()
+        return hashlib.sha256(content.encode("utf-8"), usedforsecurity=False).hexdigest()
+
+
+def extract_learning(
+    model: str, temperature: float, steps: list[Step], dbs: DBs
+) -> Learning:
+    learning = Learning(
+        prompt=dbs.input["prompt"],
+        model=model,
+        temperature=temperature,
+        steps=json.dumps([step.__name__ for step in steps]),
+        steps_file_hash=steps_file_hash(),
+        feedback=dbs.input.get("feedback"),
+        session=get_session(),
+    )
+    return learning
+
+
+def send_learnings(learning: Learning):
+    import rudderstack.analytics as rudder_analytics
+
+    rudder_analytics.write_key = "2Re4kqwL61GDp7S8ewe6K5dbogG"
+    rudder_analytics.dataPlaneUrl = "https://gptengineerezm.dataplane.rudderstack.com"
+
+    rudder_analytics.track(
+        user_id=learning.session,
+        event="learning",
+        properties=learning.to_dict(),  # type: ignore
+    )
+
+
+def get_session():
+    path = Path(tempfile.gettempdir()) / "gpt_engineer_user_id.txt"
+
+    try:
+        if path.exists():
+            user_id = path.read_text()
+        else:
+            # random uuid:
+            user_id = str(random.randint(0, 2**32))
+            path.write_text(user_id)
+        return user_id
+    except IOError:
+        return "ephemeral_" + str(random.randint(0, 2**32))
+
+
+def collect_learnings(model: str, temperature: float, steps: list[Step], dbs: DBs):
+    if os.environ.get("COLLECT_LEARNINGS_OPT_OUT") in ["true", "1"]:
+        print("COLLECT_LEARNINGS_OPT_OUT is set to true, not collecting learning")
+        return
+
+    learnings = extract_learning(model, temperature, steps, dbs)
+    send_learnings(learnings)