Oracle/deepfundingoracle.py

"""
DeepFunding Oracle:
This script dynamically loads dependency data and for each repository URL:
  • Fetches GitHub features (stars, forks, watchers, open issues, pull requests, activity) using the GitHub API.
  • Uses the LLama model to analyze parent-child behavior (based on the fetched features and parent info)
    and returns a base weight (0-1) for the repository.
  • Trains a RandomForest regressor on these features (with the base weight as the target) to predict a final weight.
The output submission CSV has three columns: repo, parent, and final_weight.
"""

from io import StringIO
import os
import warnings
import csv
import re
import requests
import numpy as np
import pandas as pd
import time
import threading
import logging
import concurrent.futures
from concurrent.futures import ThreadPoolExecutor
import signal
from tqdm import tqdm
import sys
import re

from sklearn.model_selection import train_test_split, RandomizedSearchCV
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error

from Oracle.SmolLM import SmolLM

warnings.filterwarnings("ignore")

# Configure logging to file and console
logging.basicConfig(
    handlers=[
        logging.FileHandler("deepfundingoracle.log"),
        logging.StreamHandler(sys.stdout)
    ],
    level=logging.INFO,
    format="%(asctime)s - %(levelname)s - %(message)s"
)

##############################
# Enhanced GitHub API helper: Fetch repository metrics
##############################
def fetch_repo_metrics(repo_url):
    """
    Fetch GitHub metrics (stars, forks, watchers, open issues, pull requests, and activity) given a repository URL.
    Assumes repo_url is in the form "https://github.com/owner/repo".
    """
    try:
        # Extract owner and repo name
        m = re.search(r"github\.com/([^/]+)/([^/]+)", repo_url)
        if not m:
            return {"stargazers_count": 0, "forks_count": 0, "watchers_count": 0, "open_issues_count": 0, "pulls_count": 0, "activity": 0}
        owner, repo_name = m.group(1), m.group(2)
        api_url = f"https://api.github.com/repos/{owner}/{repo_name}"
        headers = {}

        token = os.environ.get("GITHUB_API_TOKEN", "")
        if token: headers["Authorization"] = f"token {token}"
        r = requests.get(api_url, headers=headers)
        if r.status_code == 200:
            data = r.json()
            pulls_url = data.get("pulls_url", "").replace("{\/*state}", "")
            pulls_count = len(requests.get(pulls_url, headers=headers).json()) if pulls_url else 0
            activity = data.get("updated_at", "")
            return {
                "stargazers_count": data.get("stargazers_count", 0),
                "forks_count": data.get("forks_count", 0),
                "watchers_count": data.get("watchers_count", 0),
                "open_issues_count": data.get("open_issues_count", 0),
                "pulls_count": pulls_count,
                "activity": activity,
                "owner": owner,
                "repo_name": repo_name,
                "token": token
            }
        else:
            return {"stargazers_count": 0, "forks_count": 0, "watchers_count": 0, "open_issues_count": 0, "pulls_count": 0, "activity": 0}
    except Exception:
        return {"stargazers_count": 0, "forks_count": 0, "watchers_count": 0, "open_issues_count": 0, "pulls_count": 0, "activity": 0}


##############################
# Enhanced Feature Extraction
##############################
def load_data(file):
    """
    Dynamically load the dependency data CSV from the uploaded file.
    Expects at least "repo" and "parent" columns.
    """
    try:
        print("[INFO] Loading data from uploaded file...")
        start_time = time.time()
        # Read the uploaded file directly into a DataFrame
        df = pd.read_csv(file)
        end_time = time.time()
        print(f"[INFO] Data loaded successfully in {end_time - start_time:.2f} seconds.")
        return df
    except Exception as e:
        print("[ERROR] Error loading data:", e)
        return None

def fetch_github_features(df):
    """
    For each row, using the repo URL, call the GitHub API to fetch:
      stars, forks, watchers, open issues, pull requests, activity, and contributors count.
    Adds these as new columns to the DataFrame.
    """
    print("[INFO] Fetching GitHub features for repositories...")
    start_time = time.time()
    stars_list = []
    forks_list = []
    watchers_list = []
    issues_list = []
    pulls_list = []
    activity_list = []
    contributors_list = []

    cache = {}

    def get_metrics(repo_url):
        if repo_url in cache:
            return cache[repo_url]
        val = fetch_repo_metrics(repo_url)
        cache[repo_url] = val
        return val

    with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executor:
        futures = {executor.submit(get_metrics, row['repo']): i for i, row in df.iterrows()}
        for fut in tqdm(concurrent.futures.as_completed(futures), total=len(futures), desc="Fetching metrics"):
            res = fut.result()
            stars_list.append(res["stargazers_count"])
            forks_list.append(res["forks_count"])
            watchers_list.append(res["watchers_count"])
            issues_list.append(res["open_issues_count"])
            pulls_list.append(res["pulls_count"])
            activity_list.append(res["activity"])

            # Fetch contributors count
            try:
                contributors_url = f"https://api.github.com/repos/{res['owner']}/{res['repo_name']}/contributors"
                headers = {"Authorization": f"token {res['token']}"}
                contributors_response = requests.get(contributors_url, headers=headers)
                if contributors_response.status_code == 200:
                    contributors_list.append(len(contributors_response.json()))
                else:
                    contributors_list.append(0)
            except Exception:
                contributors_list.append(0)

    df["stars"] = stars_list
    df["forks"] = forks_list
    df["watchers"] = watchers_list
    df["open_issues"] = issues_list
    df["pulls"] = pulls_list
    df["activity"] = activity_list
    df["contributors"] = contributors_list

    end_time = time.time()
    print(f"[INFO] GitHub features fetched successfully in {end_time - start_time:.2f} seconds.")
    return df

def timeout_handler(signum, frame):
    raise TimeoutError("LLama model prediction timed out.")

# def assign_base_weight(df, max_workers=32):
#     """
#     Assign base weights using LLama model in parallel.
#     """
#     print("[INFO] Starting base weight assignment using LLama model...", flush=True)
#     logging.info("[INFO] Assigning base weights using LLama model...")
#     start_time = time.time()
#     llama = SmolLM()
#     base_weights = []
#     llm_cache = {}
#
#     # Prepare prompts for all repositories
#     prompts = {}
#     for idx, row in df.iterrows():
#         repo = row.get("repo", "")
#         parent = row.get("parent", "")
#         stars = row.get("stars", 0)
#         forks = row.get("forks", 0)
#         watchers = row.get("watchers", 0)
#         issues = row.get("open_issues", 0)
#         pulls = row.get("pulls", 0)
#         activity = row.get("activity", "")
#         prompts[idx] = (
#             f"Repository: {repo}\n"
#             f"GitHub Metrics: {stars} stars, {forks} forks, {watchers} watchers, {issues} open issues, {pulls} pull requests, activity: {activity}.\n"
#             f"Parent or dependency: {parent}\n\n"
#             "Based on these features, assign a dependency weight between 0 and 1 for the repository "
#             "that reflects how influential the repository is as a source relative to its parent. "
#             "Only output the numeric value."
#         )
#
#     # Define the prediction function
#     def _predict(idx, prompt):
#         if idx in llm_cache:
#             return idx, llm_cache[idx]
#         try:
#             resp = llama.predict(prompt)
#             match = re.search(r"[-+]?\d*\.\d+|\d+", resp)
#             weight = min(max(float(match.group()), 0), 1) if match else 0.0
#             llm_cache[idx] = weight
#             return idx, weight
#         except Exception as e:
#             print(f"[ERROR] Failed to process repository {idx}: {e}", flush=True)
#             logging.error(f"[ERROR] Failed to process repository {idx}: {e}")
#             return idx, 0.0  # Default weight in case of failure
#
#     # Run predictions in parallel
#     with ThreadPoolExecutor(max_workers=max_workers) as executor:
#         futures = [executor.submit(_predict, idx, prompt) for idx, prompt in prompts.items()]
#         for fut in tqdm(concurrent.futures.as_completed(futures), total=len(futures), desc="LLM Prompts"):
#             idx, weight = fut.result()
#             base_weights.append((idx, weight))
#
#     # Sort weights by index and assign to DataFrame
#     base_weights.sort(key=lambda x: x[0])
#     df["base_weight"] = [weight for _, weight in base_weights]
#
#     end_time = time.time()
#     print(f"[INFO] Base weights assigned successfully in {end_time - start_time:.2f} seconds.", flush=True)
#     logging.info(f"[INFO] Base weights assigned successfully in {end_time - start_time:.2f} seconds.")
#     return df

def assign_base_weight(df, max_workers=32):
    """
    Assign base weights using a single LLM call to determine feature weights,
    and programmatically calculate repository weights.
    """
    print("[INFO] Starting optimized base weight assignment...", flush=True)
    logging.info("[INFO] Assigning base weights using optimized approach...")
    start_time = time.time()
    llama = SmolLM()

    # Step 1: Call LLM once to determine weights for each feature
    prompt = (
        "The following are GitHub repository features:\n"
        "- Stars\n"
        "- Forks\n"
        "- Watchers\n"
        "- Open Issues\n"
        "- Pull Requests\n"
        "- Activity (days since last update)\n"
        "- Contributors\n\n"
        "Assign a weight (0-1) to each feature based on its importance in determining "
        "the influence of a repository. Provide the weights as a JSON object with "
        "keys as feature names and values as their weights."
    )
    try:
        response = llama.predict(prompt)
        feature_weights = eval(response)  # Convert JSON string to dictionary
        print(f"[INFO] Feature weights from LLM: {feature_weights}", flush=True)
    except Exception as e:
        print(f"[ERROR] Failed to fetch feature weights from LLM: {e}", flush=True)
        logging.error(f"[ERROR] Failed to fetch feature weights from LLM: {e}")
        return df

    # Step 2: Programmatically calculate weights for each repository
    def calculate_weight(row):
        weight = 0
        for feature, feature_weight in feature_weights.items():
            if feature in row and pd.notna(row[feature]):
                weight += row[feature] * feature_weight
        return weight

    df["base_weight_raw"] = df.apply(calculate_weight, axis=1)

    # Step 3: Normalize weights per parent
    df["base_weight"] = df.groupby("parent")["base_weight_raw"].transform(
        lambda s: (s - s.min()) / (s.max() - s.min() if s.max() != s.min() else 1)
    )

    end_time = time.time()
    print(f"[INFO] Base weights assigned successfully in {end_time - start_time:.2f} seconds.", flush=True)
    logging.info(f"[INFO] Base weights assigned successfully in {end_time - start_time:.2f} seconds.")
    return df

def prepare_dataset(file):
    print("[INFO] Starting dataset preparation...")
    start_time = time.time()
    df = load_data(file)
    if df is None:
        raise ValueError("Failed to load data.")
    if not {"repo", "parent"}.issubset(df.columns):
        raise ValueError("Input CSV must contain 'repo' and 'parent' columns.")
    print("[INFO] Fetching GitHub features...")
    df = fetch_github_features(df)
    print("[INFO] GitHub features fetched successfully.")
    print("[INFO] Assigning base weights using LLama model...")
    df = assign_base_weight(df)
    end_time = time.time()
    print(f"[INFO] Dataset preparation completed in {end_time - start_time:.2f} seconds.")
    return df


##############################
# Enhanced RandomForest Regression
##############################
def train_predict_weight(df):
    print("[INFO] Starting weight prediction...", flush=True)
    start_time = time.time()
    target = "base_weight"
    if "activity" in df.columns:
        # Parse ISO timestamps as UTC and subtract with a UTC timestamp
        df["activity"] = pd.to_datetime(df["activity"], errors="coerce", utc=True)
        now = pd.Timestamp.now(tz="UTC")
        df["activity"] = (now - df["activity"]).dt.days.fillna(-1)
    feature_cols = ["stars", "forks", "watchers", "open_issues", "pulls", "activity", "contributors"]
    if target not in df.columns:
        raise ValueError("Base weight column missing.")
    X = df[feature_cols]
    y = df[target]
    print("[INFO] Splitting data into training and testing sets...", flush=True)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
    rf_model = RandomForestRegressor(random_state=42, max_depth=None)
    param_dist = {
        "n_estimators": [100, 200, 300],
        "min_samples_split": [2, 5, 10],
        "min_samples_leaf": [1, 2, 4]
    }
    print("[INFO] Performing randomized search for hyperparameter tuning...", flush=True)
    rand_search = RandomizedSearchCV(
        estimator=rf_model,
        param_distributions=param_dist,
        n_iter=20,
        cv=3,
        scoring="neg_mean_squared_error",
        random_state=42,
        error_score="raise"
    )
    rand_search.fit(X_train, y_train)
    print("[INFO] Randomized search completed.", flush=True)
    print("Best Parameters:", rand_search.best_params_, flush=True)
    print("Best MSE:", -rand_search.best_score_, flush=True)
    y_pred = rand_search.best_estimator_.predict(X_test)
    mse = mean_squared_error(y_test, y_pred)
    print("Final RF Test MSE:", mse, flush=True)
    print("[INFO] Predicting final weights for all rows...")
    df["final_weight_raw"] = rand_search.best_estimator_.predict(X)
    # Normalize weights per parent for meaningful spread
    df["final_weight"] = df.groupby("parent")["final_weight_raw"].transform(
        lambda s: (s - s.min()) / (s.max() - s.min() if s.max() != s.min() else 1)
    )
    end_time = time.time()
    print(f"[INFO] Weight prediction completed in {end_time - start_time:.2f} seconds.", flush=True)
    return df

##############################
# CSV Output
##############################
def create_submission_csv(df, output_filename="submission.csv"):
    print(f"[INFO] Writing results to {output_filename}...", flush=True)
    required_cols = ["repo", "parent", "final_weight"]
    submission_df = df[required_cols]
    submission_df.to_csv(output_filename, index=False)
    print(f"[INFO] Results written to {output_filename}.", flush=True)
    return output_filename

# Removed Gradio UI code from this file to ensure modular workflow.
# This file now focuses solely on data processing and prediction.

if __name__ == "__main__":
    print("DeepFunding Oracle is now ready for backend processing.", flush=True)